AGRICULTURE SECTOR: IMPORTANT CHALLENGES AND PROSPEROUS OF INDIAN ECONOMY.

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The basic characteristics of underdeveloped countries discussed in research paper might as well be regarded as the obstacles to economic development. Even thought the general characteristics of under development are not common to all the under developed countries. Yet broad answer to the Question why a poor country is poor is implicit in these Characteristic. A number of these characteristics are both the causes and consequence of poverty, unemployment, education, health care, price control, agriculture, industry, service sectors. The above factors analyze the causative relationship that inhibits development. Each and every citizen of the country has a right to lead a decent life. Everybody must be able to fulfil minimum needs such as food, health care, housing basic education etc. However, India is a poor country where a large section of the population cannot afford all there. The matter is made worse due to the fact that our economy does not provide adequate employment Opportunities so that poor people cannot get jobs and earn income. Hence eradication of poverty and unemployment is a major challenge before the economy similarly higher quality of life is achieved by getting proper education and health care facilities. Agriculture is a basic occupation. It is the oldest business in the world and nearly two thirds of the population of the worlds is dependent on agriculture for it livelihood. Agricultural progress is normally regarded as a prerequisite of economic development. Agriculture is the foundation on which the entire superstructure of the growth of industrial and other sectors of the economy has to stand. Industrialization plays a crucial role in the economic development of a country. Industrialization means development of industries, mining power plants, transport etc. It is a continuous process of creation and growth of factors, mills, mines, power plants etc. In a developing country like India building up of industries is essential without the development of industries economic development is impossible in fact, industrialization. It is in this context service sector together play crucial role indeed services have grown an important in Indian economy. Today majority of population earn their livelihood from service sector. Service sector is "Tertiary Sector" of Indian economy. It covers a wide range of activities like banking and finance, insurance, tourism and transportation, trading construction and ISSN: 2320-5407 Int. J. Adv. Res. 6(3), 1318-1324 1320 The economic growth rate is very slow due to there is no work then the suffering the labor class for their earning. The slow growth of industries, education and training are main reason for unemployment in India. Also our agriculture is already over burden with population and employment is seasonal there. Four main causes of poverty in India are as follows: It is said that "a country is poor because it is poor." This idea has come down from Ragner Nurkse who pin pointed the problem of vicious circle of poverty, low level of saving reduce the scope for investment, low level of investment yields low income and thus, the circle of poverty goes on indefinitely, measures to poverty.
The govt. has relied mainly on the approaches for reduction of poverty. The first entails pursuit of higher economic growth which will improve the level of living of all groups of people in the society including the poor. The second involves direct antipoverty program and third has stressed high priority to govt. expenditure on social sectors, the govt. Has launched several poverty alleviation programmed.
Unemployment or problem of unemployment:-India has been facing the problem of unemployment and labor surplus, unemployment is normally found in all economics, but it is serious in the Indian economy. Unemployment depicts the darker side of economic progress and it only illustrates inefficient utilization of human resources, lower production and lower standard of living.

Causes of unemployment in India:-
The problem of unemployment in India is deep-rooted and complex. It is a result of interaction of a number of forces which are following reason. 1. Excessive growth of population 2. Slow growth production. 3. Backwardness of Agriculture 4. Lack of manpower planning 5. Increase in labor force 6. Defective Education System 7. In appropriate technology 8. Neglect of small Scale and cottage industries 9. Immobility of labor Measuring policy of The Governor:-Though the removal of unemployment has been proclaimed goal Indian economic planning on attempts is made to implement a long term strategy so far. The employment orientation programmed implements under a different five year plans include small farmer development agency. The marginal farmers and agricultural labor development Integrated Rural development programme. The scheme of training for youth employment, the Rural land less employment Guarantee programmed. Jawar Rojgar Yojana, Nehru Rojgar Yojana. The schemes main aim is providing more and more employment opportunities in rural and urban areas in India. The Indian population day by day increases in increasing rate, the above policies are creating the more job opportunities in rural as well as urban level.
The main aim is more and more job opportunities and provide types job in rural and urban areas with the help above measuring policies.
Health care, Role of Development:-Another major challenge before the country is to provide better health care 254 females out of every 100000 die giving birth. This is called maternal mortality rate (MMR) 50 out of 1000 die at the time of birth is called a infant mortality rate. 15 children out of 1000 die before completing 4 years of age which is known as child mortality rate. Certainly this news is not encouraged. There are so many villages and remote areas do not have adequate health center or hospitals and doctors to attend the problem of people. In 2010-expenditure on health care which is only 1.27 % of our national income measuring of health care.
The rate of govt. is important in providing health facilities, the schemes were launched by the govt. National Rural Health Mission (NRHM) was launched in 2005 to provide affordable and quantitative health services to rural population. It aims at strengthening health and family welfare programmed, removing, diseases such as Malaria, 1321 Blindness, Iodine deficiency, T.B. (Tuberculosis), Leprosy etc. by improving public health delivery system, NHRM has started revitalizing the existing primary an community health centers. In the September 2010 around 08 lack health workers have been given training on health care and more than 09 thousand doctors and 26 thousand nurses have been appointed on contact basis to give health services to rural population.
Price rise, Role of Development:-People pay prices to buy different commodities in the market. It price increase than it becomes difficult to buy the same amount of the goods and services. As a result, the level of satisfaction of the individual falls When you pay more prices, your existing income looks less than before since you have to now pay more money to buy less amount of goods people wants to buy in the market is more than its actual availability then this will create a situation of shortage of the particular goods. As a result the price of goods has not been adequate. For example, food grain production falls if a drought situation happens. Another reason of shortage could be wastage of the goods due to lack of proper storage facilities. Finally shortage will accrue if the seller hoards the goods without selling it so that a man made shortage creates high prices of goods.

Controlling the Prices:
The govt. price policy has been to stabilize price for this. It has been adopting various monetary, fiscal and other measures to check demand and raise supplies of commodities. The reserve Bank of India has been following the policy of controlling monetary expansion. It is guided by the twin objectives of avoiding excess build up to liquidity at a time when inflationary (price increasing period) pressure reemerge in the economy and regulating the flow of credit to stimulate and sustain production.

Feature of Agriculture-Role of Development:-
Agriculture is the main occupation of the people nearly 60 to 80 % of the population is engaged in agriculture and its related activities. Poverty is wide spread; population grows at a high rate (about 2 % per annum). The standard of living of people is generally low and productivity of labor is also considerably low. The production techniques are backward. Investment in research and development is quite low. The incidence of unemployment and under development is quite high. Income inequalities are wide spread.
Agriculture is the main occupation of the people inIndia. At the time of independence nearly 76 % of the population was dependent on agriculture. At present nearly 56 % population is dependent on agriculture. There has been an increase in the absolute number of people engaged in agricultural activities in India. The agriculture sector is the backbone of an economy which provides the basic ingredients to mankind and now raw materials for industrialization. Causes of low productivity statistical evidence suggest that farm productivity in India is low as compared to other countries of world. The low productivity of the agriculture sector is attributable to a host of technological and institutional factors. Some of the important of these factors described 1. Technological factors 2. Institutional factors.
The technological factors are lack of irrigation facilities to farmers, the farmer are used limited fertilizer for their agricultural production and limited use of high yield of variety of seeds. To former are lack of knowledge about new mechanization and they are not used new equipments in their field for agriculture production.
The farmers do not know the knowledge of flood controls and soil erosion system prosperous of Indian agriculture sector.
Employment opportunities for rural people:-Agriculture provides employment opportunities for rural people on a large scale in underdeveloped and developing countries (India also). It is an important source of livelihood. Generally landless workers and marginal farmers are engaged in non-agricultural jobs like handicraft furniture, textiles, leather, metal -work, producing industries and in other service sectors to related of the agriculture sector. These rural units fulfill merely local demands. In India about 70.6% of total labor provided the job opportunities in agriculture sectors. Agriculture makes its contribution to economic development in several ways-1. By providing food and raw material to non agricultural sectors of the economy. 1322 2. By creating demand for goods produced in non-agricultural sectors, by the rural people on the strength of the purchasing power earned by them on selling the marketable surplus. 3. By providing investable surplus in the farm of savings and taxes to be invested in Non-agricultural sectors. 4. By earning valuable foreign exchange through the export of agricultural products. 5. Providing employment to a vast people of uneducated, landless labor and unskilled labor As a matter of fact. If the process of economic development is to be initiated and made self Industrialization plays a vital and crucial role in economic development of an under-development nation (like India) compared to the developed nations. Which are largely industrial economics the under developed nation are predominantly agricultural economics. Industrialization in such a underdeveloped nations is dependent on the role of agricultural sector. In India industrialization plays an important role in modernizing agriculture and improving productivity in that sector. Similarlymagriculture plays an important role in the industrial development.
Industrialization accelerate economic growth following various ways-1. Industrialization increase per capita income. 2. Industrialization expands employment opportunities. 3. Industrialization brings increasing returns. 4. Industrialization builds a self radiant and self sustained economy. 5. Industrialization generate modern attitude. 6. Industrialization ensures growth with stability. 7. Industrialization provides expending market. 8. Industrialization increase in national income.

Some problem of Industrialization:-
The small scale and cottage big industries are beset with a number challenges. Many of big industries are sick at the end of 2010. There were as many 13.5 % big units in the country. The industries, due to various problems have failed to make their contribution to the full to the development of country along the desirable lines. The important problems are shortage of capital.
The institutions which provide finance to their industries are not adequately developed. As result they are faced capital shortage. The next problem is raw material. Raw materials are not available in sufficient quantities at require site quality and at seasonal rates. Due to this there is wastage of productive capacity and many units thus incur hugs losses.
VII) Service sectors role in Development: The service sector with around 57% contribution to gross domestic product GDP in 2014-15, has made rapid strides in the past decade and a half encourage as the largest and one of the fastest growing sectors of the economy. The service sectors is not only the dominant sector in India"s GDP, but has attracted significant foreign investment flow, contributed significantly to expect has as well as provide large scale employment. India"s service sector cover a wide variety of activities such as made. Hotel and restaurants, transport, storage and communication, financing, insurance, real estate, services, community social and personal service and services associated with construction.
As result employment also adjusting to changes and people are leaving the agriculture sector to find work in the service sector. This job creation is particularly use full as often it provides employment for low skilled labors in the tourism and retail sectors. Thus benefiting the poor in particular and repenting an overall increase in employment. The service economy in developing countries is most often made up the financial services, tourism distribution, health and education etc.
India has the second fastest growing services sector with its compound annual growth rate at nine percent, just below China"s 10.9 % during the last 11 year periods from 2001-2012. The economic survey for 2013-2014 said Russia at 5.4 % is a distant third. Among the world"s level countries are in terms of GDP. India ranked 10th in terms of overall GDP and 12th terms services GDP in 2012. It said adding that services share in the world GDP was 65.9% but share in employment was only 44% in 2012.

Summary And Conclusion:-
India is the under developed country, even though the general characteristic of underdevelopment are not a common are not common to all the under developed countries. Yet abroad answer to the question why "a poor country is poor" is implies in there characteristic. A number of characteristic are causes and consequence of poverty, unemployment, education, health care, price control, agriculture, industry, service sector. Each and every citizen of the country has a right to lead a decent life. Everybody must be able to fulfill minimum needs such as food, health care and shelter, basic education etc. So that poor people can get jobs and earn income. Hence eradication of poverty and unemployed is possible to avoided or protected, similarly higher quality of life is achieved by getting proper education and health care facilities. Agriculture is a basic occupation. It is the oldest business in the world and nearly two third of the population of the worlds depend upon agriculture sector for it livelihood. Agriculture is the foundation on which the entire super structure of the growth of industrial and other sectors of the economy has to stand. Service sector together play crucial role indeed services have grown an important in Indian economy. Today majority of population earn their livelihood from service sector.
The role of govt. is important in providing health facilities. The schemes were launched by the govt. National health rural health mission was launched in 2005 to provide affordable and quantitative health services to rural population. It aims at strengthening health and family welfare program removing diseases. Such as Malaria, Blindness, Iodine deficiency, T.B., Leprosy etc. by improving public health delivery system.


extfieldsofstudy:
Computer Science
Economics

text:
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning

We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns. Our model captures well both the time-varying characteristic and the asymmetrical heavy-tail property of financial time series. It combines the merits of a popular sequential neural network model, i.e., LSTM, with a novel parametric quantile function that we construct to represent the conditional distribution of asset returns. Our model also captures individually the serial dependences of higher moments, rather than just the volatility. Across a wide range of asset classes, the out-of-sample forecasts of conditional quantiles or VaR of our model outperform the GARCH family. Further, the proposed approach does not suffer from the issue of quantile crossing, nor does it expose to the ill-posedness comparing to the parametric probability density function approach.

Introduction
In general, machine learning models aim to predict one single value of output variable y given input x, usually to estimate the conditional mean E[y|x]. In many situations, we are also interested in the characteristics of the conditional distribution p(y|x). A typical domain needing the learning of these characteristics is financial returns. Data from financial markets is highly stochastic or noisy. It is impossible to accurately predict future financial returns. What we can predict and what we really care about are their conditional distributional characteristics like volatility, heavy tails, and Value-at-Risk, which are all widely used measures of risks. The huge and increasing demands for risk management and for understanding market behaviors make it extremely important to predict these characteristics.
In the scope of discrete-time econometric models, the benchmark of forecasting conditional distribution of time-t asset return r t conditional on past return history is the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and its variants. First appearing in [8] and [2] to model time-varying volatility, GARCH-type models have now become a big family, including popular variants like EGARCH [21], GJR-GARCH [13], TGARCH [27], etc. They all describe the distribution p(r t |r t−1 , r t−2 , . . . ) by making strong assumptions on the probability density function of it, e.g., assuming it is Gaussian, and let the distribution parameters depend on past information. Usually, t-distribution is assumed to model heavy tails. Quantile regression [16] [17] is another type of method to forecast the conditional distributional characteristics. It predicts the quantiles of p(y|x) without making any distributional assumption. In this paper, we model and predict conditional quantiles and heavy tails of financial return series in a parsimonious quantile regression framework that describes the distribution p(r t |r t−1 , r t−2 , . . . ) in a parametric quantile function way.
It is known that financial asset returns are heavy-tail distributed, both conditionally and unconditionally [6]. This has important consequences for both the pricing of assets and the management of their risks [12]. More importantly, their tail behaviors are not only asymmetrical but also time-varying. In GARCH family, t-distribution is heavy-tailed but symmetric, and more critically, the degrees of freedom which control the tail heaviness cannot vary with time. Previous studies [14] [19][1] [22] modelled time-varying conditional skewness and kurtosis in an autoregressive way , like volatility modelling in GARCH. However, they all assumed complicated probability density functions, some of which even had no analytical forms, which make model estimation difficult. Besides, models that allow the data to speak for itself rather than being restricted to linear auto-regressiveness are needed.
In this paper, we approach the problem by parameterizing the conditional quantile function of asset returns, instead of assuming they are coming from a given probability density function, hence suffering from tractability and ill-posedness. Except for probability density function, quantiles are another representation of the asymmetry and tailedness of a distribution. If one can model and estimate conditional quantiles for a fine set of probability levels, it achieves almost the same effect as modelling conditional mean, volatility, skewness, and kurtosis simultaneously. Quantile regression has the potential to undertake this interesting task, but the traditional version suffers from some issues. One is the lack of monotonicity in the estimated quantiles, also known as quantile crossing, despite some imperfect solutions proposed in [24] and [4]. Other issues include the increasing number of parameters when estimating more quantiles, and the lack of interpretability. Recently some works deal with the large-scale [26] and high-dimensional [23] situations of quantile regression.
In this paper, we propose a parametric heavy-tailed quantile function (HTQF) to model a distribution with asymmetric left and right heavy tails. The Q-Q plot of the proposed HTQF against the standard normal distribution is of an inverted S shape and the degrees of the tail heaviness are controlled by two parameters in a flexible way. Our HTQF overcomes the disadvantages of the probability density function approach in GARCH-type models when modelling asymmetric heavy tails. For financial asset returns, we let the quantile function of p(r t |r t−1 , r t−2 , . . . ) be an HTQF and let the parameters of it be time-varying and depend on past information through a Long Short-term Memory (LSTM) [15], which is a popular sequential neural network model. Parameters of the LSTM can be learned in a quantile regression framework with multiple probability levels. After training, the conditional quantiles of r t and the interpretable parameters of HTQF representing tail heaviness can be estimated.
Our method has significant advantages over GARCH-type models and traditional quantile regression. To summarize, our contributions are: (1) We propose a novel parametric quantile function to represent a distribution with asymmetric heavy tails, and leverage it to model the conditional distribution of financial return series. (2) In the quantile regression framework, coupled with an LSTM, our method can learn the time-varying tail behaviors successfully and predict conditional quantiles more accurately, as verified by our experiments. (3) We overcome the disadvantages of traditional quantile regression, including the quantile crossing, the increasing number of parameters when estimating more quantiles, and the lack of interpretability.

GARCH-type Models
For an univariate financial return series {r t }, GARCH model was first proposed in [8] and [2] to model its time-varying volatility or volatility clustering. By making a prior assumption on the conditional distribution of the residual ε t = r t − µ t (µ t is the conditional mean of r t ), letting it be a normal one N (0, σ 2 t ), the time-t volatility σ t is modelled to depend on past residuals ε t−1 , . . . , ε t−q and past volatilities σ t−1 , . . . , σ t−p . Formally, GARCH(p, q) is specified as follows: where ψ t−1 denotes the past information set. The parameters ω, α i , β j can be estimated with maximum likelihood method. Because of its success in modelling and forecasting conditional volatility, a lot of extensions and variants had been proposed such as EGARCH [21], GJR-GARCH [13], TGARCH [27], etc. Most of them made reasonable and interpretable changes to the Equation (2) and achieved better performances.
Alternatives can also be made in Equation (1). A better choice of the distribution assumption is Student's t-distribution: ε t = σ t z t , z t |ψ t−1 ∼ t(ν) where ν is the degrees of freedom. t-distribution has symmetric heavy tails at the left and right sides. We denote GARCH-type models with tdistribution assumption by GARCH-t, EGARCH-t, etc. Besides, one can also choose different ways to model the conditional mean µ t , e.g., to use GARCH-type models alone, it can be set to be a constant µ t = µ. One can also adopt the linear autoregressive way: µ t = γ 0 + γ 1 r t−1 + · · · + γ s r t−s . We denote GARCH-type models with this linear autoregressive specification of conditional mean and with the t-distribution assumption by AR-GARCH-t, AR-EGARCH-t, etc.
Although GARCH-type models were initially designed to model and forecast conditional volatility, because they fully describe the conditional distribution, naturally they can be used to predict conditional quantiles. Actually they are widely employed in finance to predict Value-at-Risk (VaR), which are the left-tail side quantiles, e.g., 0.01 or 0.05-quantile, representing downside risk of asset prices.
Another big family of models that have similarities with GARCH-type models are stochastic volatility (SV) models. Some comparisons between GARCH-type and SV models were made in [25][10][3] [11]. SV models are applied in situations when volatility contains independent risk driver. In continuous time, if driven by Brownian Motion , they are Markovian, which is essentially different from GARCHtype models and our proposed model and may not be suitable for modelling serial dependence of volatility. What are comparable with GARCH-type and our models and are consistent with the focus of this paper, are long-memory volatility models driven by, e.g., fractional Brownian Motion or Hawkes process, and preferably in discrete time. CAViaR [9] is another similar model for estimating conditional quantiles inspired by GARCH. It models conditional quantiles separately for different probability levels instead of making assumptions on the full conditional distribution. So it is somewhat difficult to estimate the conditional moments, also different from GARCH-type and our models.

Traditional Quantile Regression
Quantiles are important characteristics of a distribution. For a continuous distribution density p(y), for a given probability level τ ∈ (0, 1), e.g., τ = 0.1 or 0.9, the τ -quantile q of p(y) is defined as q = F −1 (τ ) where F (y) is the cumulative distribution function of p(y). Quantile regression [16] [17] aims to estimate the τ -quantile q of the conditional distribution p(y|x). To do this, without making any assumption on p(y|x), a parametric function q = f θ (x) is chosen, for example, a linear one q = w T x + b. Note that q is an unobservable quantity, a specially designed loss function (named pinball loss in this paper) between y and q makes the estimation feasible in quantile regression: Then we minimize the expected loss in a traditional regression way to get the estimated parameterθ: is minimized instead. When we want to estimate multiple conditional quantiles q 1 , q 2 , . . . , q K for different probability levels τ 1 < τ 2 < · · · < τ K , K different parametric functions q k = f θ k (x) are chosen and the losses are summed up to be minimized simultaneously: However, this combination may lead to an embarrassing problem called quantile crossing, i.e., for some x and τ j < τ k , it is possible that f θj (x) > f θ k (x) which contradicts the probability theory. It occurs because θ j and θ k are in fact independently estimated in the optimization. To overcome this, additional constraints on the monotonicity of the quantiles can be added to the optimization to ensure non-crossing [24]. Another simpler solution is post-processing, i.e., to sort or rearrange the original estimated quantiles to be monotone [4]. Another two shortcomings of this traditional quantile regession include the increasing number of parameters when estimating quantiles for a larger set of τ , i.e., K is larger. For more elaborate description of a distribution, large K is necessary in some cases. The other shortcoming is that the explicit mapping from x to the conditional quantile has no interpretability, makes it difficult to combine domain knowledge.

Our Method
We first describe the proposed parametric quantile function, then show how it is used to model the conditional distribution p(r t |r t−1 , r t−2 , . . . ) of financial return series and how the dependence on past information is modelled. Our proposed method is completed in a quantile regression framework.

Heavy-tailed Quantile Function
There are three common ways to fully express a continuous distribution, through probability density function (PDF), cumulative distribution function (CDF), or quantile function. In financial data modelling, much attention is paid to how to choose an appropriate parametric PDF that is consistent with the empirical facts of financial returns, like heavy tails. In our method, we design a parametric quantile function that allows varying tails and is intuitively easy to understood.
Our idea starts from the Q-Q plot, which is a popular method to determine whether a set of observations follows a normal distribution or not. The theory behinds this is quite simple: the τ -quantile of a normal distribution N (µ, σ 2 ) is µ + σZ τ , where Z τ is the τ -quantile of the standard normal one. When τ takes different values in (0, 1), their Q-Q plot forms a straight line. If the Q-Q plot yields an inverted S shape, it indicates that the corresponding distribution is heavy-tailed, see Figure 1 (a) for the Q-Q plot of t-distribution with 2 degrees of freedom against N (0, 1).
We construct a parsimonious parametric quantile function, as a function of Z τ , to let it have controllable shape in the Q-Q plot against the standard normal distribution. Specifically, the up tail and down tail of the inverted S shape in the Q-Q plot are controlled by two parameters respectively. Our proposed heavy-tailed quantile function (HTQF) has the form: where µ, σ are location and scale parameters respectively, A is a relatively large positive constant. u > 0 controls the up tail of the inverted S shape, i.e., the right tail of the corresponding distribution. v > 0 controls the down tail, i.e., the left tail of the corresponding distribution. The larger u or v, the heavier the tail. When u = v = 0, the HTQF becomes the quantile function of a normal distribution.
To understand these, note that in Equation (6), Z τ is first multiplied by two factors f u (Z τ ) = e uZτ /A + 1 and f v (Z τ ) = e −vZτ /A + 1, then multiplied by σ and added by µ (for simplicity one can set µ = 0 and σ = 1). The factor f u is a monotonically increasing and convex function of Z τ , and satisfies f u → 1 as Z τ → −∞. So Z τ f u (Z τ ) will exhibit up tail of the inverted S only. The same analysis applies to the factor The roles of A are to let f u (0) and f v (0) be close to 1, and to ensure the HTQF is monotonically increasing with Z τ . Figure 1

Quantile Regression with HTQF
For the distribution p(r t |r t−1 , r t−2 , . . . ), different from GARCH-type models, we do not make assumptions on the PDF of it, instead we assume its quantile function being an HTQF, denoted by Q(τ |µ t , σ t , u t , v t ), where µ t , σ t , u t , v t are time-varying parameters representing the location, scale, and heavy tails of the corresponding distribution. So conditional τ k -quantile of r t can be easily obtained by putting τ k into the function: q t k = Q(τ k |µ t , σ t , u t , v t ), k = 1, . . . , K. Obviously, the parameters µ t , σ t , u t , v t should depend on past series r t−1 , r t−2 , . . . . To model that, we select a subsequence of fixed length from r t−1 , r t−2 , . . . to construct a feature vector sequence, and apply LSTM on it. LSTM [15] is a popular and powerful sequential neural network model in machine learning, so it is a natural choice in our method, see the supplementary material for a brief introduction and [20] for a comprehensive review of it. In detail, a fixed length L is chosen, then a feature vector sequence of length L is constructed from r t−1 , . . . , r t−L : The intuition behinds this construction is straightforward, that is to extract information contained in raw quantities associated with the first, second, third, and fourth central moments of past L samples. After this construction, we model the four HTQF parameters µ t , σ t , u t , v t as the output of an LSTM when feeding input x t 1 , . . . , x t L : where Θ is the LSTM parameters, h t is the last hidden state. W o , b o are the output layer parameters.
At last, for multiple probability levels 0 < τ 1 < τ 2 < · · · < τ K < 1, the pinball losses between r t and its conditional quantiles q t k = Q(τ k |µ t , σ t , u t , v t ) are summed up to be minimized together, like in traditional quantile regression: Combine Equation (6)(7)(8)(9) to complete our proposed quantile regression method with LSTM and HTQF, denoted by LSTM-HTQF. After training, for new subsequent series {r t }, the time-varying parameters µ t , σ t , u t , v t of HTQF can be calculated directly with the learned model parameterŝ Θ,Ŵ o ,b o . Among them, {u t } and {v t } can represent how the tails behave temporally. In addition, conditional quantiles q t k can be predicted and the summed loss in Equation (9) is evaluated again for testing the performance on the new subsequent series, since no ground truth of quantiles are available.

Discussions
The advantages of our method over GARCH-type models are obvious. The proposed HTQF is more intuitive and flexible to model asymmetric heavy tails than the PDFs in GARCH-type models, like the skewed generalized t-distribution in [1]. To have varying tails, one PDF must be in complicated analytical form that will make model estimation difficult. Even for the simplest one, the t-distribution, the analytical complexity of its PDF makes model estimation unfeasible if one assumes time-varying degrees of freedom, while our HTQF parameters can be easily set to be time-varying in the quantile regression framework. Besides, the LSTM can help to learn nonlinear dependence on past information while the linear auto-regressiveness in GARCH-type models cannot. We quantitatively compare our method to several classical GARCH-type models in the experiments.
Comparing to traditional quantile regression, our method overcomes the three shortcomings mentioned in Section 3. First, it is not hard to prove that the HTQF is a monotonically increasing function with Z τ , and also with τ , so quantile crossing will never happen. Then, no matter how large the K is, i.e., a lot of quantiles need to be estimated, we just need HTQF's four parameters µ t , σ t , u t , v t to determine all of them. That is a big saving in number of parameters. At last, our model is interpretable, and combines domain knowledge in finance. For quantitative evaluation, we implement the traditional quantile regression in our experiments, also coupled with an LSTM. Mathematically describing it, in Equation (8), the output µ t , σ t , u t , v t are replaced by quantiles q t k : is minimized as in Equation (9). For new subsequent time t , the predicted quantiles q t 1 , . . . , q t K of r t are sorted to ensure no crossing. We denote this method by LSTM-TQR.
Generally, feature vector sequence x t 1 , x t 2 , . . . , x t L should be designed to contain any information that is related to the conditional distribution of r t or is helpful to the prediction, like trading volume, related assets, or fundamentals. To keep consistency with GARCH family and ensure the fairness of the comparisons in experiments, we construct x t 1 , x t 2 , . . . , x t L only from past returns r t−1 , r t−2 , . . . . In real applications of our method, more information can be included in the feature vector sequence.
Our method is widely applicable in quantile prediction or time series modelling in many other nonfinancial fields. Time series data exhibiting asymmetrical time-varying tail behavior and nonlinear serial dependence of conditional distribution, e.g., hydrologic data, internet traffic data, and electricity price and demand, are most suited. One can also change the standard normal distribution in the Q-Q plot (Z τ in HTQF in Equation (6)) to other baseline distribution, to let the HTQF have controllable shape in the Q-Q plot against the specified distribution, like exponential one or lognormal one, the choice of which relies on domain knowledge.

Experiments
Our experiments are conducted on three types of time series datasets: simulated data, daily asset returns (of stock indexes, exchange rates, and treasury yields), and intraday 5-minute commodity futures returns. For daily returns, for every time series, the data of maximum possible length is used, e.g., S&P 500 index returns start from January 4, 1950 and end at July 2, 2018, which is the longest series with more than 17000 observations. The shortest has nearly 8000 observations. For intraday commodity futures returns, the recent 1-year every 5-minute returns are used and each series has about 20000 observations. All returns are calculated by r t = P t /P t−1 − 1 where P t is the price, rate, or yield at time t.
Each time series is divided into three successive parts, for training, validation, and testing respectively. The training set is four fifths of the original series, the validation and test sets are both one tenth. The training set is normalized to have sample mean 0 and sample variance 1, followed by normalizing the validation and test sets in the exactly same way. The validation set is used for tuning hyperparameters, and for stopping training when the loss on the validation set begins to increase, to prevent over-fitting. Our method has two hyper-parameters, the length L of past series r t−1 , . . . , r t−L on which time-t HTQF parameters µ t , σ t , u t , v t depend, and the hidden state dimension H of the LSTM. We denote our method with them by LSTM-HTQF(L,H). Similarly, the LSTM-TQR method described in Section 4.3 also has L and H as hyper-parameters. Our competitor methods are mainly GARCH-type models, from which we select some popular ones for comparisons: GARCH, GARCHt, EGARCH-t, GJR-GARCH-t, AR-EGARCH-t, and AR-GJR-GARCH-t. In all of them, s, p, q are hyper-parameters that will be tuned, see Section 2 for details.
The tuning of the hyper-parameters is done in the following sets: L ∈ {40, 60, 80, 100}, H ∈ {8, 16}, and s, p, q ∈ {1, 2, 3}. The A in the HTQF is set to be 4 arbitrarily. We choose K = 21 probability levels into the τ set: [τ 1 , . . . , τ 21 ] = [0.01, 0.05, 0.1, . . . , 0.9, 0.95, 0.99]. Performance is evaluated using the pinball loss on the test set. GARCH-type models can easily do this because the conditional PDF is modelled. For comparisons from different perspectives, two test performances over two different τ sets are evaluated, one is the full τ set, the other is [0.01, 0.05, 0.1], the quantiles of which are VaR representing downside risk.

Simulated Data
The purpose of simulation experiment is to verify whether our method can learn the true temporal behavior of conditional distribution of a given time series. We generate our simulated time series in a way similar to GARCH-t model, but differently, let the degrees of freedom ν t be time-varying. Specifically, starting from r 0 = 0 and σ 0 = 1, the time series {r t } together with the scale parameter {σ t } and tail parameter {ν t } are generated as follows: Totally 10000 data points are generated. Some example pieces of the generated {σ t } and {ν t } are shown in Figure  One can see that the learned HTQF scale and tail parameters {σ t } {û t } are highly linearly correlated to the true parameters {σ t } {ν t }, on both the training set and the test set. It means that our method has successfully learned the temporal behavior of conditional distribution of r t . In fact, the linear correlation coefficients between the two lines in the four subplots are 0.8751, -0.8974, 0.9548, and -0.8808 respectively. Negative signs are due to the fact that the heavier the tail, the bigger theû t but the smaller the ν t . Run linear regressions between them, we get R-squared values: 0.7658, 0.8054, 0.9116, and 0.7758. Another learned parameter {v t } is similar to {û t }, and is not shown here, because the t-distribution used for generating the data is symmetric.

Real-world Market Data
In this experiment, first, world's representative stock indexes, exchange rates, and treasury yields are selected, including S&P 500, NASDAQ 100, HSI, Nikkei 225, DAX, FTSE 100, exchange rate of USD to EUR/GBP/CHF/JPY/AUD, and U.S. treasury yield of 2/10/30 years. We report the pinball losses of every methods on the test sets of every asset return series, as shown in Table 1. In part (a) and (c) of Table 1, the losses over the full τ set are reported, while in part (b) and (d) the losses over only [0.01, 0.05, 0.1] (the quantiles of which are VaR) are reported. It is clearly shown that on most assets our LSTM-HTQF outperforms the competitors. Moreover, in part (b) and (d), the performance improvements are more significant than in (a) and (c), which is consistent with the intuition that the tails are better modelled by us. Note that the pinball loss is such a measure between observation and quantile that, even for ground truth quantile, the loss is not zero and is bounded by a positive number. So a small decrease in the loss may actually be a substantial improvement. We also conduct the Kupiec's unconditional coverage test [18], the Christoffersen's independence test [5], and the mixed conditional coverage test for backtesting the VaR forecasts of various models, and show the results in the supplementary material (see [7] for a description of details of these statistical tests). To investigate the tail dynamics captured by the LSTM-HTQF model, we plot the HTQF parameters {û t } and {v t } on S&P 500 test set in Figure 3 (a), where the blue line is the right tail parameter {û t } and the red one is the left {v t }. We can see roughly similar patterns in the two lines, both with clustering and spikes, but difference in details.
At last, we collect intraday 5-minute returns of five commodity futures from Chinese futures market: steel rebar, natural rubber, soybean, cotton, and sugar. To reduce the difficulty, the overnight jumps are eliminated. In the same way as daily asset returns, the losses on the test sets are reported in Table  2, which also shows that our LSTM-HTQF outperforms the competitors on most assets. The plotting of {û t } and {v t } on steel rebar test set is shown in Figure 3 (b), which indicates that high-frequency financial asset returns also have time-varying heavy tails. The different tail dynamic with S&P 500 may attribute to the different time scales of the two time series.

Conclusion
In summary, in this paper, we propose a parametric HTQF to represent the asymmetric heavy-tailed conditional distribution of financial return series. The dependence of HTQF's parameters on past  information is modelled by an LSTM. The pinball loss between the observation and conditional quantiles makes the learning of LSTM parameters be in a quantile regression framework, which overcomes the disadvantages of traditional quantile regression. After learning, conditional quantiles or VaR can be predicted with relatively better accuracy, and besides, the plotting of HTQF parameters shows us the dynamic tail behaviors of financial asset returns, some of which display clustering and spikes but difference between left and right tails.
Although our paper focuses on the tail dynamics, in the future, more advanced models that can learn more elaborate dynamics of the conditional distribution of financial time series are necessary, e.g., improving the flexibility of the HTQF or modifying the way how LSTM is used may be needed. Moreover, it is important to discover how LSTM and HTQF in our model work respectively and how they contribute to the performance improvements. It is also interesting to interpret what tail dynamics of financial assets we have captured, and what consequences it has for understanding market behaviors, for asset pricing, and for risk management. To make those clear, we may need more in-depth analysis of our model and more statistical testing and analysis of the VaR or quantile forecasts of the model in the future.

Appendix A. The Proof of the Existence of HTQF's Unique Probability Distribution
The proof idea is to show that HTQF is continuously differentiable, is strictly monotonically increasing over (0, 1), and approaches −∞/ + ∞ as τ tends to 0/1. So the inverse function of HTQF exists and is a cumulative distribution function.
The HTQF has the specification: where g(x) = x( e ux A + 1)( e −vx A + 1). Z τ is the quantile function of the standard normal distribution, so we only need to prove that g(x) is continuously differentiable, is strictly monotonically increasing over (−∞, +∞), and approaches −∞/+∞ as x tends to −∞/+∞. Obviously g(x) is continuously differentiable and lim x→−∞/+∞ g(x) = −∞/ + ∞. To prove the monotonicity, we calculate the derivative of g(x): Next we prove h(x) ≥ −1, ∀x. This is equivalent to 1 + x ≥ −e −x , or 1 + x + e −x ≥ 0, ∀x. A simple monotonic analysis on the function 1 + x + e −x can reveal that its global minimum is reached at If we choose A ≥ 3, then g (x) ≥ − 1 9 − 1 3 − 1 3 + 1 = 2 9 > 0 holds for all x. So g(x) is strictly monotonically increasing and our proof is completed.

Appendix B. A Brief Introduction to LSTM
In machine learning, LSTM is a type of recurrent neural network model designed for capturing both long-term and short-term dependencies or complex dynamics in sequential data. Recently remarkable successes have been made in its applications like speech recognition, machine translation, protein structure prediction, etc. Mathematically, LSTM is a highly composite nonlinear parametric function that maps a sequence of vectors x 1 , . . . , x n to another sequence of vectors y 1 , . . . , y n (or to just one vector y), through hidden state vectors h 1 , . . . , h n . Examples include machine translation from a Chinese sentence to an English sentence, and the classification of a music clip to its genre.
Before describing the full mathematics of it, we first introduce the simple recurrent neural network model, which is the understructure of LSTM and has the functional form: (18) W h , U h , b h , W y , b y are the parameters and σ h , σ y are nonlinear activation functions. It models a nonlinear functional relationship between x 1 , . . . , x n and y 1 , . . . , y n . One can stack this structure multiple times to get multi-layered or hierarchical recurrent neural network, which is a type of deep learning model. LSTM extends this structure and has the equations: and at last, the output y j is a nonlinear function of h j , like: All W, U, b are parameters and σ g , σ h are nonlinear activation functions, which are chosen as logistic function and tanh function respectively in this paper. * represents element-wise multiplication of two vectors. In the case of outputting only one vector y, one can use the average of all hidden state vectors 1 n h j or just the last one h n , e.g., y = σ h (W y h n + b y ), like the Equation (8) in our paper.
The tanh function and logistic function are the two most popular activation functions in neural network models, playing a role of nonlinear transformation with finite range. Multiple compositions of these activation functions can approximate complex nonlinear relationships between input vectors x 1 , . . . , x n and output vectors y 1 , . . . , y n or y.

Appendix C. Statistical Testing of VaR Forecasts
Consider a sequence of realized returns or observations {r t } and a sequence of VaR or quantile forecasts {q t } of a fixed probability level τ by any model. In order to implement the testing procedure, we need the definition of hitting sequence of quantile violations: Ideally, {I t } should be an i.i.d. Bernoulli distribution sequence with parameter τ . To test that, Kupiec's unconditional coverage test [18] checks if the unconditional distribution of {I t } is the Bernoulli distribution, i.e., if the proportion of quantile violations is equal to τ . Christoffersen's independence test [5] checks if I t is independent of I t −1 , i.e., current violation (or not) is independent of previous violation (or not). The mixed conditional coverage test jointly checks these two null hypotheses. One can refer to [7] for details of the three tests. We report the statistics of these tests for τ = 0.01, 0.05, 0.1 VaR forecasts in the following tables. In some sense, the smaller the test statistic, the better the forecasts. The results show that our LSTM-HTQF model does quite well in some cases.


extfieldsofstudy: Business Physics

text:
An Examination of Regional Competitiveness: Early Findings from Banten, Indonesia

Over time, the regional development planning paradigm continues to change procedural, substantive, actor and institutional aspects. The increasing globalization and decentralized governance systems will continue to determine how a country or region can compete and even be able to take advantage of the positive value of economic globalization. This certainly provides challenges and opportunities for the renewal of regional development concepts, in particular, the concept of regional competitiveness. This concept still lacks a scientific basis and its interpretation is still in debate. To date, the clear scientific basis is the concepts of corporate and national competitiveness. Whereas, the concept of competitiveness at the meso-level, namely the competitiveness of the region, becomes very important. Regional competitiveness prepares a region and to survive in response to the more open dynamics of economic globalization and the system of decentralization of authority in Indonesia. The objective of this research is to complement the concept of competitiveness which is still at the level of microeconomics (business theory) and macroeconomy (theory of growth) with the concept of competitiveness at the regional level that focuses on (i) finding determinant factors at the regional level in Indonesia; (ii) profiling regional competitiveness in several regions; and (iii) assessing the role of local government (institutional and actor) on determinants of regional competitiveness. This research is carried out by using a mixed-methods approach with a concurrent triangulation strategy model. The research follows the stages of literature study to identify the determinant factors of regional competitiveness relevant to the purpose of research, followed by descriptive analysis of the variables and selected by statistics through Analytical Network Process (ANP) to become the determinant factor of competitiveness. In accordance with the concept and definition of regional competitiveness, this research develops five main indicators determining regional competitiveness and its 30 variables. These indicators are regional economy; manpower and human resources; productive business environment; infrastructure; natural resources and environment; and banking and financial institutions. The data used are primary and secondary data by compiling from the Central Statistics Agency (BPS) and questionnaires to the Regional Development Planning Agency (BAPPEDA) following by in-depth interviews with experts. This research uses the case study of Banten Province. The main reason is that Banten has varied areas, ranging from developed cities to lagging regions. It is expected to represent other areas in Indonesia. This research provides an alternative framework of competitiveness analysis methods at the regional level. The concept of regional competitiveness can be a catalyst and enabler to the development of regional specialization and networking among regions in the era of economic globalization.

Introduction
There are deep concerns about the performance of the regional economy when viewed from the tendency of international competitiveness in recent years. The development of a country's competitiveness certainly cannot be separated from the development of its components, namely regional competitiveness. Therefore, the position of regional competitiveness rating is a reflection of the competitiveness position of the regions. Increased regional competitiveness is believed to be able to improve the competitiveness of the Region at the national level. One of the efforts to improve the competitiveness of regional economy is through increasing its economic competitiveness. The improvement of regional competitiveness is expected not only to improve the competitiveness of the regional economy in a global market but also to accelerate the growth of the real sector which has been considered not yet optimal.
The implementation of regional autonomy and fiscal decentralization since 2001 (Law 32/2004 and Law 33/2004), states that decentralization in the regions is focused at the regency/municipality level. Regions/municipalities are given the widest authority to regulate and carry out development in accordance with the potential and function of each region (except for some sectors that have been agreed to be the authority of the central government). Decentralization has given freedom to regions, especially regencies to develop local economies, thus accelerating regional development and improving the welfare of their communities.
The background mentioned above shows the importance of the ability of the region in synergizing its strengths and potentials in an effort to improve competitiveness. The ability of this region will depend on its ability to identify the factors that affect the competitiveness of each region, as well as its ability in implementing policies to improve its regional competitiveness relative to other regions.
Therefore, a new methodology and broader scope are needed to determine the competitiveness of a regency/municipality. The methodology adapted to the current policy concept will have policy implications far different from those of previous studies. This methodology will systematically identify the determinants of competitiveness and then describe the competitiveness of a region through the determinants of competitiveness above. The framework of this regional competitiveness methodology will provide an overview of the level of competitiveness of a region and what policy interventions are required by the government in enhancing regional competitiveness.

Goals
Considering the above-mentioned background, in more detail this study aims to: a. Determine the main factors that affect the competitiveness of a region; b. Determine the weight of the main factors that affect the competitiveness of the region; c. Calculate scores and competitiveness between regencies/municipalities for each classification of the regions in Banten Province; d. Classify the area using the clustering method.

Benefits
In accordance with the research objectives to be achieved, this research will contribute to: a. Increasing the role of regional governments to increase economic growth through enhancing regional competitiveness; b. Improving roles and policies to assist local governments in enhancing regional economic competitiveness; c. Increasing the effectiveness of fiscal planning and fiscal policy.
2. The concept of regional competitiveness Understanding of regional competitiveness originates from the concept of competitiveness that has evolved to enterprise and country level. The thinking of Michael Porter [5,6] colored the development and application of the concept of competitiveness at the enterprise level. Furthermore, this concept is developed at the country level as a Global Competitiveness, particularly through the institutions of the 3 1234567890 ''"" World Economic Forum (Global Competitiveness Report) and the International Institute for Management Development (World Competitiveness Yearbook). The strategic impact of this Global Competitiveness Index measurement on the country's strategic position in addressing its international competitiveness has made this measure a reference to the economic performance and investment climate of an economy. The trend of decentralization has reinforced the need for identification of competing determinants at the local level. This section discusses the development of previous and ongoing research libraries since the concept of state competitiveness is rolled out to the development of concepts and research on regional competitiveness. The discussion includes also the development of the concept of regional competitiveness used in research which is an adaptation of the concept of regional competitiveness that has developed so far. The notion of regional competitiveness began to develop after [23] defined national competitiveness. He defined it as an outgrowth of a country's ability to innovate in order to achieve or maintain a favorable position compared to other countries in a number of key sectors. This understanding is further developed by Thurow [8], who stated that the key sectors can be categorized as sectors that have a high technological composition with high demand and have a highly competitive base which is then called the "knowledge-based industries". Specialization of these sectors is believed to be a trigger for the improvement of higher living standards for the community.
The notion of competitiveness at the sub-national level was then developed by Storper [7], who defined regional competitiveness as the economic capacity of an area to attract and retain firms with a stable or increasing market share in their operations, while ensuring the improvement or stability of living standards for the communities participating in such activities. Regional competitiveness will be assured only if sustainable development can be achieved at the level of the workforce that can enhance overall living standards.
One of the key factors in achieving such growth is the existence of potential firms indicated by the level of business density that can lead to the birth of entrepreneurs and the new innovations in developing sectors and markets in the area; even more important is to create new jobs [2]. The regional competitiveness of American cases is more influenced by the growth of companies in the region that are characterized by being innovative and able to take advantage of new technological discoveries to create new business opportunities in the market.
Regional competitiveness in European countries is often defined as the ability of an area to achieve economic growth by producing goods and/or services, in which the product is able to compete at both national and international levels, providing a high standard of living to its people as well as employment, and the results of such development can be enjoyed on an ongoing basis. As defined by the UK Department of Trade and Industry (UK-DTI), regional competitiveness is a region's ability to generate high income and employment while remaining open to domestic and international competition. Moreover, the Center of Urban and Regional Studies (CURS) in 1998 defined regional competitiveness as the ability of a business or company in a region in generating high income and wealth that is more equitable for the population.
Bank Indonesia and the Faculty of Economics of UNPAD (2001) defined regional competitiveness by modifying the definition of UK-DTI. The regional competitiveness of the study is defined as the ability of the regional economy to achieve high and sustainable growth while remaining open to domestic and international competition. IMD (2001) analyzed competitiveness for 49 countries based on both quantitative and qualitative data, classified into eight groups. Quantitative data is given weight 1, while the survey data was given a weight 0.64. While WEF (1999) weighted the data in eight classifications: openness, 1/6; government, 1/6; finance, 1/6; infrastructure, 1/9; technology, 1/9; management, 1/18; employment, 1/6; and institutional, 1/18. The weighting is based on regression analysis by looking at the correlation between these factors and per capita economic growth.
A study of regional competitiveness in China in 2006 developed the concept of regional competitiveness using two main components, i.e., the technological progress component of each region (calculated based on the Total Factor Productivity score) as well as the regional investment climate 4 1234567890 ''"" component (consisting of several assessment components such as market share; labor market flexibility; technological characteristics and regional expertise; government bureaucracy efficiency; enforcement and protection of business agreements and copyright; regional access to financial institutions; and quality of life of local communities).

The concept and definition of regional competitiveness in this research
The concept and definition of regional competitiveness developed in this study are based on two considerations, namely regional economic development in terms of regional economic aspects, and the development of concepts and definitions of regional competitiveness of previous studies. From the regional economics aspect, regional economic development cannot be separated from the development of national economy and economy of other regions. The interaction between regions (both goods, services, and labor) and policies at the national level will have an impact on the economy of a region. The description of the economy of a region [4] can be illustrated as a drum (figure-1), where the regional economy is heavily dependent on the outflows and incomes and commodities of other regions or nationalities, the magnitude of potential products in the region, and the magnitude of potential leakage in the region (such as unemployment rate, and waste in resource use).
Meanwhile, the development of regional economic progress is determined by four factors [1]. First, the job opportunities in the area (including the understanding of the quality of the workforce so as to provide a good location access for companies that will do business in the area). Secondly, the basis of regional development (in the sense that the development of good economic institutions that can lead to increased business desires for the business community). Third, the location asset is a regional competitive advantage based on environmental quality. Fourth, the knowledge resources, in the sense of knowledge as the basis of the economic drivers (knowledge-based development).
Based on the above understanding, the process of economic development in the region aims to:  Establish a new "institution" that supports the regional economy;  Develop alternative industries;  Increase the capacity of workers to produce better products;  Stimulate the occurrence of technology transfer; and  Open investment opportunities for entrepreneurs.
Therefore, the main objective of regional economic development is to increase local initiatives to create employment opportunities for the community (both in the number and type of work) and to stimulate the increase of economic activity in order to increase community incomes.  On the other hand, based on several preceding definitions of regional competitiveness, it appears that regional competitiveness is produced by complex interactions between input, output, and outcome factors in their respective regions. Therefore, the interrelationship between these three aspects will largely determine the success of a region compared with other regions. Local competitiveness is also related to non-economic factors, such as political, social, and cultural parameters of society. The concept of competitiveness, which is the interaction of input, output and outcome components, for example, has been used by Huggins in calculating regional competitiveness index in England [3]. Figure 2 shows the Regional Competitiveness Pyramid, which can explain the differences in regional competitiveness in a region, referring to the definition of regional competitiveness developed in this study. Regional competitiveness is formed by the main factors (inputs) which are both regional endowment or caused by the interaction of community activities such as differences in the productive business environment; the structure and the condition of the regional economy; the difference in the quality and quantity of human resources in each region; infrastructure, natural resources and environment; and the condition of financial and banking institutions. The performance of the main factors of the regional competitiveness will make a difference in the performance of the economy (output) of each region, such as the difference in the level of employment created and the productivity of its workforce. These differences will ultimately result in differences in the sustainability of the quality of life and the welfare of the people of each region. Thus, based on the previous literature on the theory of regional development as well as the concept and definition of regional competitiveness, the meanings of regional competitiveness in this study are: the ability of regions to synergize between input, output and outcome that exist in the region on a continuous basis, with attention to technological changes and institutions in the area, in order to compete both nationally and internationally, so as to improve the living standards of the community. The definition and concept of regional competitiveness of this study are as described in the previous section where the realization will be represented in the competitiveness index consisting of the outputs of input, output, and outcome. Besides a descriptive method and literature study, the main method used in this research is econometrics and expert opinion polling especially by using Delphi method (omitted). This method is a more systematic and consistent implementation of focus groups.

Determination of competitiveness factors and variables
The factors that shape competitiveness at the regional economic level, particularly at the regency and municipal levels, may be specific to the region itself (internal factors) as well as external factors that are exogenous and cannot be used as policy variables for the regions concerned. Referring to the Huggins study [3], competitiveness indexes are grouped into three main components, namely input, output, and outcome. In Huggins, the input component is equivalent to internal factors [3]. In this study, the input component is further expanded to include the external competitiveness factors that will primarily affect the business environment. The internal competitiveness factors refer to the concept of 'productivity', i.e., the amount or rate of change in value added per unit of input generated by an entity. This productivity is further elaborated into measurable variables that generally have a dimension of 'ratio'. The external factors can be 'industryspecific', region-specific, 'or' country-specific'.
After establishing the determinants of competitiveness, the next step is to describe it in terms of the variables that represent each of these factors. The method used for this is semi ad-hoc in the sense that the variables used in this study are selected based on the benchmarking method which is then modified as needed for regional and regency levels through literature search and 'simple focus groups'.

Determination of Weight and Ranking of Competitiveness
Some of the important outputs expected in this study are the rank and balance of competitiveness for each region or group of regions. Before the ranking and balance of competitiveness can be made, scoring and weighting methods are required. The commonly used method for this is 'expert opinion polling', which is to use expert knowledge and opinions to generate a criterion or index of valuing and weighing factors or variables. Two popular methods are Analytic Hierarchy Process (AHP) and Delphi Method. AHP is a method of preparing a functional hierarchy that is done systematically. The main target of AHP is to find out the weight of relevance and importance.
In this study, the Delphi method of analysis is used in determining the weight and rank of competitiveness. This method is chosen because it is believed to be more appropriate to the context under study. Delphi is the name of a set of procedures for obtaining and filtering opinions from a group, usually an expert panel. This method is a way to achieve a consensus of a group of experts after obtaining opinions in defining problems based on the knowledge and intuition of experts. The collective appraisal of these experts, though derived from subjective opinions, remains better than an individual statement and produces a more objective outcome.
The Delphi method has four main characteristics: (1) structured questions, (2) iterations, (3) controlled feedback, and (4) the anonymity of the respondents. The structured questions are manifested through the use of a questionnaire. This questionnaire will keep the problem-solving in focus and allow the moderator to control the process to produce good results. Iteration is the process by which questionnaires are distributed and answered in several rounds so that respondents can rethink the answers they have given. Controlled feedback can be achieved by sharing the results of group responses to all respondents for consideration to rethink the answer. Anonymity is manifested by a freely answerable questionnaire expressing opinions without fear of 'pressure' from other panelists. Decisionmaking in a group can be influenced by several things, including the dominance of some panelists and a strong panelist personality. Sometimes these things will reduce the ability to generate unbiased decisions or predictions. With the 'anonymity' characteristics doubts about a biased decision can be avoided. Delphi can be executed as often as three rounds to reach a level of convergence that will generate the weight for each variable or factor, which will then be used to rate the competitiveness between regions. However, before the rating can be made, the different measurement units that occur in the various variables require that the variables need to be adjusted first so that the rating is 'apple-to-apple'. For that, all variables will be converted first into a standard normal score (standard normal score).

Clustering
Comparing competitiveness between one regency/municipality and other regencies/municipalities sometimes does not provide a desirable picture, since the characteristics of the regency/municipality are in different dimensions or classes. For example, comparing a regency/municipality in Java with a regency/municipality in Papua is often considered unwise. Therefore, in addition to comparing total competitiveness, comparisons and competitiveness ratings will be based on groups, both ad-hoc (exante) and ex-post. For ad-hoc, in addition to the total comparison, the grouping will also be performed based on input and output components. The grouping is done based on western and eastern regions in Indonesia. Ex-post, grouping is based on clusters formed by considering the cluster forming factor or variable by applying cluster analysis.

Data and data sources
In accordance with the concept and definition of Regional Competitiveness developed in this study, there are five main indicators for the formation of regional competitiveness and its variables as presented in Table 1 below. These indicators are the Regional Economy; Employment and Human Resources; Productive Business Environment; Infrastructure, Natural Resources, and Environment; and Banking and Financial Institutions. Data sources rely solely on the availability of secondary data from the Central Statistics Agency (BPS) or other agencies.

The analytical framework and calculation stages of regional competitiveness in Banten Province
After collecting all the data required for this study, the next step was to identify the data that had a negative effect on the competitiveness of the region and change it in the formula. This is done so all data has a positive correlation with competitiveness. For example, without transformation, the more problematic regional regulations (Perda) the smaller the competitiveness of the regency/municipality, but after the transformation its correlation becomes positive. In other words, the 'statement' was changed to the fewer problematic regional Regulations, the higher the competitiveness of the regency/municipality. The main results of this study are the competitiveness ratings among regions and the Regional Competitiveness Balance for each regency/municipality in Banten. There are two calculations necessary to rank and balance the competitiveness, i.e., variables that can show and indicate the level of competitiveness of regions and the scores that can transform the variables or indicators of competitiveness into a ranking of competitiveness of the region. Determining, screening, and selecting the competitiveness variables was done through literature tracking and discussions. Meanwhile, the weight is generated by applying the analysis network process (ANP) method through the dispersion of competitiveness questionnaires to the relevant service offices. In this study, the variables that form the competitiveness of the regency/municipality based on the theoretical framework can be classified into groups of input and output variables. The input variables can be defined as a "flow concept" and are a resource to rise to a higher rank in the competitiveness 12 1234567890 ''"" ranking. Meanwhile, the output variable can be interpreted as "stock concept" and reflects the position of competitiveness of a regency/municipality. The scoring mechanism -commonly referred to as the scoring process -comprises several stages. The respective stages of scoring calculation can be explained as follows.
1) All data used in this study should be transformed to be "comparable".
The first step in calculating the competitiveness score to transform the data into the normal standard. The method used in this study is to use the normal standard (z-score) as the basis of normalization so that each variable will have an average value of 0 and a standard deviation of 1.
The use of normalization methods by using the z-score method is also expected to facilitate readers in understanding the variables, provided that the value of a positive variable (+) means the value is above the average value of all variables that exist throughout Banten.
2) The competitiveness score for a regency/municipality is calculated by counting the weighted sum of all variables, which has been transformed into a standard z-score standard using the weight obtained from the ANP workshop. The weighted numbers of normal high standard indicate a higher level of competitiveness. Increasing the competitiveness among regencies/municipalities is done by comparing the weighted-sum score of this variable for all regencies/municipalities.
3) In addition to competitiveness ratings among regencies/municipalities throughout Banten, this study also discusses the competitiveness ratings for each indicator or sub-indicator of each regency/municipality. The calculation of scores at the indicator and sub-indicators level is, in principle, the same as the calculation of scores at the regency/municipality level. The only difference is that this calculation involves fewer variables. In other words, the score at the indicator level is a 'subset' score at the regency/municipality level and the score at the subindicator level is the 'subset' score at the indicator level.
In more detail, the calculation stage of regional competitiveness is as follows: 1) As mentioned in the previous section, this research uses the analytical network process (ANP) method. The data is calculated and processed by using Microsoft Excel and Super Decisions version 2.8.
2) The collect and compiling of secondary data consisting of 37 input variables and 3 output variables as shown in table 1 above.
3) Conducting survey interviews using questionnaires to determine the weight of competitiveness of each variable. The survey was conducted with experts from the representatives of the department who were competent to increase the competitiveness of the regions. There were 13 respondents from various agencies. The scores that were obtained were put together using Microsoft Excel and entered in the template that has been created on the dashed box, as in Figure  4 below. The same is done for other respondents, so we will have 13 sheets 1 through 13. 4) The next step is to get normalized and idealized values using super decision software. To accommodate the thirteen respondents and to have a super decision file recap for them, a template file was made with the file type 01.sdmod up to 13.sdmod, as can be seen in Figure 5 below.

5)
Step 5 is to create the framework of the analytical network process (ANP) using predetermined competitiveness variables. Then the file type "s.mod" is entered into the software super decision for all 13 files (respondents). The output of the data processing consists of normalized and idealized values for each variable of regional competitiveness.   . Data processing to determine normalized and idealized data for weighting each variable of regional competitiveness.
6) The recap results can be seen on the joined sheet. The red box shows the average value of all respondents. This value is used as the weight that will be multiplied by the variable value of each region. The processed data is then used to rank the competitiveness of regions and to see the various variables that are advantages and disadvantages of the competitiveness of a region. Both analyses will be discussed in the next section.
Step 1. Enter the score given by the respondent for each red box node Step 2. Copy the numbers generated on the green box by clicking "copy to clipboard"  Figure 8. The result of data processing using the ANP method.

Analyzing regional competitiveness based on input and output variables
This study classifies the variables forming the competitiveness of the regency/municipality based on the developed theoretical framework into groups of input and output variables. The input variables can be defined as "flow concept" and are a resource to rise to a higher competitiveness rank. Meanwhile, the output variable can be interpreted as "stock concept" and reflects the position of competitiveness of a regency/municipality. Both variables are positively correlated. If it happens that a regency/municipality has a high input but low output, this can be interpreted as 'inefficiency'. On the other hand, if a regency/municipality has a low input position but high output, then other external factors have positively increased the competitiveness of the area. In extreme conditions, this can be interpreted as "over-capacity", which in the long term can reduce the competitiveness of the region.

Analyzing regional competitiveness based on input variables.
In the context of the input factors (see Figure 9 below), each region has different competitive determinants that accumulatively determine the different levels of competitiveness in every regency/municipality in Banten.

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The  Figure 9. Comparison of input competitiveness factor of regency/municipality in Banten.
Regional economy indicators. Regional economic indicators consisting of several sub-indicators such as sectoral productivities, local fiscal capacity, government size, total investment, and firm density contribute in determining the level of regional competitiveness. This indicator shows that the regencies of Lebak, Pandeglang, Serang and Serang Municipality have relatively low competitiveness compared to other regencies/municipalities. This happens for several reasons. First, regencies/municipalities with low competitiveness in the aspect of the regional economy only rely on primary sectoral productivity that lacks the value added of economic activity, while regencies/municipalities that rely on secondary and tertiary productivities have relatively high competitiveness. Furthermore, areas with high per capita investment and firm density such as Tangerang Regency, Tangerang Municipality, Cilegon Municipality and Tangsel Municipality contribute significantly to the regional economy, which, in turn, can improve the competitiveness of these areas. The investment per capita and firm density also affect local fiscal capacity and government size so these governments have a large budget to develop their regions, inversely with regions of low fiscal capacity and small government size. Human resource and employment indicators.The human resource and employment indicators consist of several sub-indicators such as dependency ratio, life expectancy index, mean years of schooling, mean years of labor schooling, and growth rate of the labor force. As can be seen in Figure 9 above, this indicator shows that the regencies of Lebak, Pandeglang, Serang as well as Serang Municipality have relatively low competitiveness compared to the other regencies/municipalities. This is caused by a low quality of human resources and labor. Figure 10 explains the mean years of labor schooling in these areas is very low; in Lebak Regency only 5.86 years. Whereas one of the determinants of regional competitiveness is the availability of human resources and labor with a high ability and extensive knowledge, mastering IT, have creativity and innovations so that the resulting product has competitiveness. Furthermore, the growth rate of the labor force in regions with a low competitiveness is low; the labor force growth rate in Serang Regency and Pandeglang Regency is even negative (-2.20 and-4.31). Likewise, the low life expectancy index correlates with other human resource and employment data. Meanwhile, Tangerang Municipality, Tangerang Regency, Tangsel Municipality and Cilegon Municipality have high enough human resource quality and workforce to contribute to improving the competitiveness of its region.  Figure 10. Human resource and employment variables for determining regional competitiveness.

REGIONS
Productive business environment indicator. An important indicator for determining regional competitiveness is a productive business environment. This indicator is divided into two sub-indicators. First, the government's productive business environment indicators consist of Public Service Expenditure Per Capita and the number of problematic Regional Regulations. Second, the productive business environment indicator by the community consists of the number of base sectors in the regency/municipality, the percentage of the population with university as the highest education, the poverty gap index, and population density. As can be seen in Figure 9, the productive business environment of Pandeglang, Lebak, and Serang is very low. There are several things that cause a low productivity business environment. First, the Public Service Expenditure Per Capita in these three areas is low. This means that spending on public services is still low compared with the population or the needs of the community. As shown in Figure 6, the Public Service Expenditure Per Capita in Pandeglang Regency and Serang Regency only amounts to 600 thousand rupiah. Secondly, Serang Municipality, Lebak Regency, and Pandeglang Regency have a relatively large poverty gap index of about 1.5 times greater than the other regencies/municipalities (see figure 6). Third, the percentage of the population with university as the highest education in these areas is still low. This leads to a lack of research and development in the region as well as low human resources that ultimately hinder regional competitiveness. On the other hand, Tangerang Regency, Tangerang Municipality, Cilegon Municipality and Tangsel Municipality have relatively productive business environment indicators that are conducive to building their regional competitiveness (see Figure 11).

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The  Figure 11. Productive Business Environment variable for determining regional competitiveness.
Infrastructure, natural resources and environment indicator. The factors of availability of infrastructure, natural resources, and environment also determine the level of competitiveness of a region. Some of the infrastructure, natural resources, and environment that can form the competitiveness of the region include the value added of the transportation sector per capita; road conditions according to road quality; electricity production per capita; fuel consumption of industry and household per capita; the ratio of productive land to total land area; and value-added mining and quarrying sector per capita. Based on figure 9 above, cumulatively there are only three regions with relatively good input indicators, namely Cilegon Municipality, Tangerang Municipality and Serang Municipality. This means that the availability of adequate infrastructure, natural resources, and environment can create regional competitiveness. As illustrated in figure 12 below, Cilegon Municipality and Pandeglang Regency have good and adequate road conditions that support regional development. Thus, the focus of development is no longer on road infrastructure provision but can be focused on other sectors. Meanwhile, Lebak Regency and Tangerang Regency have about 30 percent good road conditions. Therefore, it is necessary to focus on the development and improvement of road infrastructure because the road is a growth engine that has high leverage in supporting economic activities.

Analyzing Regional Competitiveness based on Output Variables
After analyzing the regional competitiveness based on the input variables, this study then examines the output of the variables as a result of input activity variables. Output variables embody high or low competitiveness of a region. The variables used to measure regional competitiveness are labor productivity, GRDP per capita, and the level of employment opportunities. Figure 9 shows that Tangerang Municipality and some areas in the northern region of Banten have relatively high output factor. The GRDP per capita Cilegon is the highest in Banten, about 146 million rupiah, followed by Tangerang Municipality with 44 million rupiah, while the lowest is Lebak with 13 million rupiah. Furthermore, Cilegon also has the highest labor productivity in Banten, amounting to 366 million rupiah, while the smallest is Lebak Regency with 33 million rupiah. Furthermore, the highest level of employment is found in Tangsel Municipality with 93.87 percent, while the lowest is Serang Regency at 85.2%. The regions ranked based on the highest output variables to the lowest are; Tangerang Municipality, Tangerang Regency, Tangsel Municipality, Cilegon Municipality, Serang Municipality, Serang Regency Pandeglang Regency, and Lebak Regency. Therefore, Tangerang Municipality is a competitive region among other regions, while Lebak Regency has the region's lowest regional competitiveness.
The northern region of Banten greatly benefited because it has a very strategic location. The northern part of Banten is part of the economic corridor of Sumatra-Java along with some areas of the megapolitan Jabodetabek area such as Tangsel, Tangerang, and Tangerang Municipality. Many large-scale economic activities exist in the northern region ranging from commercial areas to large-scale industrial areas. Meanwhile, the southern part of Banten, e.g., Pandeglang Regency and Lebak Regency is left behind, which causes a very high gap. However, this study did not emphasize the importance of regional ratings. The emphasis of the study is how a region is able to identify the advantages and disadvantages of the determinants of each region's competitiveness. Therefore, the next section examines the advantages and disadvantages of the regions against the regional competitiveness variables.

Regional competitiveness balance sheet
Making the Regional Competitiveness Balance requires a criterion in the form of numbers to filter the variables that are advantages for a region and the variables that are disadvantages. The basis for calculating the Competitiveness Balance is a normalized variable (semi-normalized) in a regency/municipality. Then the variables will be grouped into two major groups, indicating the advantage of the area if the number of variables is greater than the average -indicated by a positive zscore value. Meanwhile, the other variables will indicate a disadvantage for a regency/municipality if the rank number of those variables is smaller than the normal standard value (z-score); the variables that have a negative sign are deducted 0.5 times the standard deviation. The number of problematic regional regulations The number of problematic regional regulations The number of problematic regional regulations The number of base sectors The number of problematic regional regulations The number of problematic regional regulations The number of problematic regional regulations In the era of a massive and unlimited global economy, together with the adoption of decentralization systems especially in Indonesia, the concept of regional competitiveness is essential to complement the clearly defined concepts of competitiveness at the enterprise and national level. There needs to be a concept of regional competitiveness that is not only from a microeconomic and macroeconomic perspective but also a territorial perspective. Five main indicators and 30 sub-indicators determine the level of competitiveness of a region. These indicators are (i) regional economy, (ii) manpower and human resources, (iii) productive business environment, (iv) infrastructure, natural resources, and environment, and (v) banking and financial institutions. The combination of input and output factors represents the competitiveness of a region seen based on GRDP per capita, labor productivity, and the level of employment opportunities.
2. This study did not emphasize the importance of regional ratings. Instead, the most important point of the study is how a region can identify the advantages and disadvantages of the determinants of each region's competitiveness.
3. Regions with low competitiveness generally have an economic base sourced from natural resource wealth.
4. Regencies/municipalities will have high regional competitiveness if this is supported by human resource wealth and industrial-based or service-based economic activities. Those regions are located in the northern part of Banten; The number of problematic regional regulations  5. Regencies/municipalities with the lowest regional competitiveness position, generally rely on the primary sector (especially agriculture). Those regions are located in southern part of Banten.

Recommendations
Local governments need to implement some policies to create a highly competitive region: a. Increase economic growth through optimizing local revenue, increasing local fiscal capacity, and increasing local investment.
b. Improve the quality of human resources and employment through improving the health status of the population and labor; and improving the quality of education as well as competence, technology, and skills.
c. Create a productive business environment that can attract businesses through the simplification and harmonization of various regulations, the implementation of one-stop integrated services to accelerate and facilitate the process of licensing and non-licensing, and ease the process of land acquisition, creating a controlled security.