Stock return predictability and market integration: The role of global and local information

This paper examines the predictability of a range of international stock markets where we allow the presence of both local and global predictive factors. Recent research has argued that US returns have predictive power for international stock returns. We expand this line of research, following work on market integration, to include a more general definition of the global factor, based on principal components analysis. Results identify three global expected returns factors, one related to the major stock markets of the US, UK and Asia and one related to the other markets analysed. The third component is related to dividend growth. A single dominant realised returns factor is also noted. A forecasting exercise comparing the principal components based factors to a US return factor and local market only factors, as well as the historical mean benchmark finds supportive evidence for the former approach. It is hoped that the results from this paper will be informative on three counts. First, to academics interested in understanding the dynamics asset price movement. Second, to market participants who aim to time the market and engage in portfolio and risk management. Third, to those (policy makers and others) who are interested in linkages across international markets and the nature and degree of integration. Subjects: Econometrics; Economic Forecasting; Finance; International Economics

PUBLIC INTEREST STATEMENT
This work reveals that information flows that affect stock market behaviour arise from both global and local sources. In contrast to much of the previous work, what defines global information is allowed to expand beyond just information from the USA. Indeed, using US-based global information misses several of the more interesting dynamics. The results reveal the importance of accounting for, and separating, both global and local information. In particular, they reveal how individual stock returns respond to changes in global risk premium (expected returns) as well as how movements in actual returns are transmitted across markets. The robustness of the results is supported by an out-of-sample forecast exercise. The results have implications for market participants in terms of portfolio and risk management, policy-makers and regulators in understanding how shocks can be transmitted and academics involved in modelling market behaviour.

Introduction
This paper examines stock market predictability across a range of international markets and seeks to consider the role of global and local information in terms of any predictive power. Notably, we are interested in whether global information affects local markets to a greater extent than local information. There now exists a body of empirical evidence that supports the presence of stock return predictability arising from movements in financial ratios (particularly the dividend-price ratio) across a range of markets (e.g. Cochrane, 2008Cochrane, , 2011Kellard, Nankervis, & Papadimitriou, 2010;McMillan & Wohar, 2010;. The central argument behind this predictability is that movements in financial ratios proxy for movements in expected returns (risk premium). Most recently, that literature has been extended to consider to what extent movements in US stock returns contains predictive power for international stock returns. Rapach, Strauss, and Zhou (2013) demonstrate that lagged US stock returns have significant predictive power for non-US stock returns. However, non-US markets have limited predictive power for other markets (including the US). Therefore, the US stock market acts in a leading role, which may arise from the diffusion of news from the US. In a slightly different context, Goh, Jiang, and Tu (2013) considered the role of US economic variables in the predictability of the Chinese stock market. Again pointing to the view that the US acts as a leader and that such global information dominates over local market information.
Within the international finance literature, and related to the above work that seeks to link stock market behaviour, empirical evidence regarding the nature of integration has examined the extent to which movements in asset prices and valuations in one market (typically the US) are related to movements in asset prices and valuations in another market. Early research along this line goes back to, e.g. Bekaert and Hodrick (1992) and Bekaert (1995) who include US returns, dividend yield and interest rates in predictive regressions for a range of developed and emerging markets. Evidence of significant US-based variables in non-US markets would indicate the presence of global information. More recently, Cooper and Priestley (2013) argue that a measure of the world business cycle captures stock return predictability in seven developed markets. This, they argue, highlights two important findings. First, the business cycles of the markets considered in their research are integrated and second, that stock returns respond to global factors more than local factors.
The aim of this paper is to build upon these related lines of research and consider whether a predictability model that incorporates both global and local information on expected returns can provide additional information over a standard individual country predictability model. Thus, we reconsider the nature of stock market predictability for a range of international markets. We begin with regressions that contain single country information only in order to provide a baseline model. We then extend this following Bekaert (1995) and Rapach et al. (2013) through the inclusion of US variables which represent global information. However, as local market variables are likely to incorporate both local and global information we seek to orthogonalise local returns and dividend-price ratios. By doing so, we separate global information from local market information (i.e. local information about local market conditions).
Extending this further, it is our view that the nature of global information is greater than that captured only through US variables (a point equally made in Cooper & Priestley, 2013). Therefore, using principal components analysis, we identify those factors that account for the majority of the variations across international returns and dividend-price ratios. We then examine whether these global factors have predictive power for stock returns, while also incorporating local factors in the predictability model. An out-of-sample forecasting exercise aims to provide evidence regarding the ability of the separate global and local factors to predict stock returns in comparison to alternative models including the historical mean.
It is hoped the results in this paper would be of interest to those engaged in asset price modelling and international integration. In particular, for academics through understanding the role of global information and the need to adopt international-based asset pricing models. Furthermore, the results should be of interest to portfolio and risk managers in demonstrating the need to incorporate international information as inputs into their model building process, as well as in market-timing strategies. Finally, for policy makers and regulators who are interested in linkages across international markets and the nature and degree of integration.

Standard country predictability regressions
We begin the examination of predictability with the standard baseline model where individual country returns, r t , are regressed on a lag of the individual country (log) dividend-price ratio, d t − p t , and a lag of the return. 1 We consider results for sixteen markets over the time period 1973 month 1 to 2012 month 12. The data on stock returns and dividend-price ratios are obtained from Datastream and the results from the following predictability regression are reported in Table 1: Following, for example, Cochrane (2011) we would expect the parameter β to be statistically significant and positive. This would indicate that a higher dividend-price ratio is associated with a higher expected return and arises due to an increase in risk, which lowers current prices and increases the required risk premium. However, the reported results, which are consistent with those widely reported in the literature, are not supportive of such a relationship. Specifically, there is relatively little evidence of dividend-price ratio predictability, with only four of the sixteen markets demonstrating a positive and statistically significant coefficient. With regard to the lagged returns, there is more evidence of a statistical relationship, with lagged values significant across fifteen of the markets (albeit, at the marginal 10% significance level for two markets).
One explanation for the relative lack of significant results is the potential for confounding effects within the dividend-price ratio series, particularly as this ratio relates to both stock return and dividend growth predictability. For example, Menzly, Santos, and Veronesi (2004) demonstrate that the presence of dividend growth predictability can cause a negative relationship within the returns predictability equation. We consider the issue of confounding effects within the dividend-price ratio particularly as it relates to the presence of global and local information. In examining the effects of a global factor in predictability, we first consider the role of the US market as a lead market in terms of both stock returns and the dividend-price ratio (expected returns). Significant US effects would indicate a degree of international integration as noted by Bekaert and Hodrick (1992) and Bekaert (1995).

The role of US information in predictability
Following Rapach et al. (2013), it is argued that incorporating lagged US stock returns can improve predictive power for other market stock returns. Therefore, Table 2 reports the results of Equation (2) for each market where we now include lagged US returns, as such: where r usa,t refers to the stock return of the USA and the subscript i references the other markets. In examining these results we can see that, amongst the predictor variables, the greatest amount of statistical significance occurs for the lagged US stock return. More specifically, the lagged dividendprice ratio is positive and significant for only five of the sixteen markets (albeit, one more than previously reported), own country lagged returns are significant for four markets, with a further two significant at the weaker 10% level, while all coefficients are positive. For the lagged US stock returns, there are eight markets that report a statistically significant effect, with a further one significant at the 10% level. Of interest, all the coefficients are positive, indicating that an increase in US stock returns last month leads to an increase in other market stock returns this month. This supports the information diffusion argument of Rapach et al. (2013) and that US stock returns act as a leader. Furthermore, for the vast majority of the markets the explanatory power (adjusted R 2 ) has increased, more than doubling in several cases (e.g. Germany, Italy and Japan amongst others). That said, for Singapore the adjusted R 2 value has remained constant, while for the UK it has decreased. However, information regarding the movement of stock returns around the world may not only emanate from lagged US realised returns, but also from expected returns, as proxied by the dividendprice ratio. Indeed, as noted above, this was considered by Bekaert and Hodrick (1992). In particular, our belief is that the US dividend-price ratio may carry information about global (expected) returns and thus provide a predictive effect on global stock returns. We also contend that local market dividend-price ratios will in part reflect this global information as well as local, market-specific, information. Thus, the local dividend-price ratios may contain confounding effects from the behaviour of global and local expected returns, as well as any local dividend growth predictability, which appears more prevalent in non-US markets (Ang, 2012;Rangvid, Schmeling, & Schrimpf, 2013). (1) Therefore, in order to separate the local and global expected return information (we return to the issue of dividend growth predictability below), we regress the local dividend-price ratio on the US dividend-price ratio. In addition, we also undertake the same procedure for stock returns, regressing local returns on the US stock return. From both sets of regressions we extract the residual term, which will represent the local dividend-price ratio and returns, respectively, which will now also be orthogonal to the equivalent US variables. Thus, our explanatory variables for local stock return predictability are given as the lagged US dividend-price ratio and the lagged US stock return, together with the residual from the local dividend-price ratio on the US dividend-price ratio and the residual  from the local stock returns on the US stock returns. This allows us to separate global (US) information and local market information and prevents the likelihood of multicollinearity in the predictive regressions. For the sake of precision, first, we estimate the following regression: where x refers in turn to the stock return and dividend-price ratio series and the ε xi,t can be regarded as the orthogonalised local market return or dividend-price ratio. Using this separated local and global information our predictability regression now becomes: Table 3 presents the two separate regressions of the local dividend-price ratio on the US dividendprice ratio and the local stock return on the US stock return for our fifteen non-US markets based on Equation (3). Considering these results, two broad conclusions can be drawn. First, across the 15 markets there is a high degree of statistical significance within these regressions. For the local dividend-price ratio regressions, the US dividend-price ratio is significant for 13 markets, while for the local stock returns regressions, the US stock return is significant for all 15 markets. Furthermore, for both sets of regressions the coefficients are positive (except the insignificant Italy dividend-price ratio regression), hence the movement of the markets is in the same direction, indicting the presence of common information. Second, in terms of explanatory power, the US dividend-price ratio has greater power for local dividend-price ratios than in the corresponding returns regressions for ten of the markets, with the opposite therefore found for five markets. Overall, therefore, both sets of results support the idea of the presence of global information within returns and expected returns, which may aid in the predictive regressions. Table 4 now presents the predictive regressions from Equation (4) where the explanatory variables are the US dividend-price ratio and stock returns and the local dividend-price ratio and stock returns given by the residuals from the respective market regressions in Equation (3). These results show that the dominant factor in determining local market stock returns is the US stock return (consistent with Rapach et al., 2013). Looking across the four explanatory variables, US stock returns have the greatest degree of statistical significance, including now being significant for markets that previously reported insignificance lagged US returns. This latter point suggests that the previous insignificance may have been due to collinearity between the two lagged returns series and hence, supportive of the orthogonalising procedure. More specifically, for the local market dividend-price ratio this is only significant for two markets (with a further one significant at the 10% level). Equally, the US dividend-price ratio is only significant for two markets (with a further one significant at the 10% level), while only three markets report a significant home market lagged returns (again, with a further market significant at the 10% level). However, for 14 of the 15 markets the lagged US return is significant and positive (while it is 10% significant for the remaining market). This result emphasises the view that US returns have predictive effect on returns around the rest of the world. In terms of the model fit, the adjusted R 2 is marginally lower for ten markets and marginally higher for five markets. This reflects the fact that adding the US dividend-price ratio does not improve the explanatory power and is largely insignificant.

Global information and principal components
The above results suggest that US stock returns have predictive power for stock returns in a range of international markets, although the US dividend-price ratio does not have similar global predictive power. However, this approach assumes that US returns and dividend-price ratio proxy fully for global information and ignores any global information content that may also exist in other markets. Moreover, we argue above that local market dividend-price ratios contain both global and local information, so the same should be true with the US variables. Thus, the above results really only inform us of predictive ability of US variables, with the belief that as the largest market it acts as a (3) leader with information emanating from the US and diffusing towards other markets. However, global information will be present across all markets as it represents common information.
Therefore, in order to separate global and local information from market returns and the dividendprice ratio, we undertake principal components analysis. Principal component analysis allows us to extract common factors (components) from a group of data series. The components are ordered according to how much of the variation across the series they can account for and are orthogonal to each other, thus representing independent information. In order to report the output of the principal components analysis, we present in Figure 1 for the dividend-price ratio and Figure 2 for returns, the scree plot. This plot represents the ordered components from the highest to the lowest eigenvalue against the maximum number of components (equal to the number of series). A rule of thumb in choosing the number of principal components is to choose those whose eigenvalue is greater one.

Table 3. Regression of local market on the US market
Note: Entries are the coefficient values with Newey-West t-statistics from the regression on the country dividendprice ratio and stock return on the US dividend-price ratio and stock return, respectively, Equation (3), given by Alternatively, this is seen in the eigenvalue cumulative plot whose slope is steeper than the 45 o line. Equally, the cut-off number of components is often regarded as the point where the plot hinges. For the dividend-price ratio it can be seen that the first three principal components account for over 80% of the variability in the sixteen series and is chosen as the preferred number of components. For stock returns we also chose three components for consistency with dividend-price ratio, however arguably we could chose only two or even one component.
To further examine the nature of the principal components analysis, Table 5 presents the factor loading for the first three factors for both the dividend-price ratio and stock returns. These loadings

Table 4. Predictive regression with local and global (US) information
Note: Entries are coefficient values with Newey-West t-statistics for the predictive regression, Equation (4), given by: where home stock returns are regressed on lagged US stock returns and log dividend yield and the lagged home stock returns and dividend yield, with the latter defined as the residuals from the equations reported in Table 3 will reveal if any particular component is dominated by one series, for example, following the general belief that the US market can proxy for the world then we would expect to see the first principal component loading heavily on the US market with little elsewhere. Given this therefore, these results present a contrasting view to this perception; for both the dividend-price ratio and stock returns the first principal component loads consistently across all markets (with perhaps the exception of Italy for the dividend-price ratio) supporting the view that the global factor emanates across all markets. With regard to the other principal components, it is noteworthy that the second component for stock return is negative for all European markets (except the UK) and positive elsewhere, while the second component for the dividend-price ratio is negative and of similar value for the UK and US. This indicates country differences, perhaps around Anglo-Saxon markets. Table 6 presents the predictive regressions for our sixteen markets that include the three principal components identified as representing global information from the dividend-price ratio and stock return series. As with the previous analysis, in order to obtain local market information we again regress the home markets dividend-price ratio (return) on the three identified principal components for the dividend-price ratio (returns) series and use the resulting residual, which will be orthogonal to the principal component series. Again, for clarity, first we estimate: where x again refers to the dividend-price ratio and stock return series, respectively, pc refers to the principal component of which we include the first three (j = 1, …, 3) and η xi,t is the orthogonalised local

Table 6. Predictive regression with local and global (principal components) information
Notes: Entries are coefficient values with Newey-West t-statistics for the predictive regression, Equation (6), given by: Home stock returns are regressed on lagged stock return and log dividend yield principal components and the lagged home stock returns and dividend yield, which are defined as the residuals from a regression of stocks returns and log dividend yield on the principal components. The results in Table 6 present an interesting pattern for both the dividend-price ratio and returns. Focusing on the dividend-price ratio, for the first principal component we can see that this is significant only in the regressions for the UK and US (and marginally for Hong Kong, Japan and Singapore). This accords with the view that a large part of the global information regarding expected returns emanates from the UK (as the dominant European market) and the US, while information from Asia appears dispersed across the three noted major markets. With regard to the second principal component, there are no significant markets for which this series has predictive power (discussed further below), while the third principal component is significant for thirteen markets and indicates it acts as a global factor. Turning to the principal components for returns, as perhaps expected from the plots in Figure 2, the first principal component is significant for all markets, with limited evidence of significance elsewhere. This indicates that a choice of one principal component would indeed be acceptable.
Regarding local dividend-price ratio and returns information there is limited evidence of significance across our markets, suggesting the dominance of global information. For the local dividendprice ratio, although this is positive for all bar two markets (consistent with our beliefs regarding the role of the ratio as a proxy for expected returns) but is only significant for four markets (France, Hong Kong, Singapore and the UK). With respect to the local stock returns, the coefficient sign are split between positive (nine markets) and negative (seven markets) however significance only occurs for a selection of the North European markets, Austria, Belgium, Denmark, Germany and the Netherlands (and only at the 10% level for the latter four).
The use of principal components analysis is designed to consider evidence for global information regarding expected returns (dividend-price ratio) and returns in contrast to using a US only proxy. Factor loadings on the dominant component for both the dividend-price ratio and returns support the view that they represent global information. Arising from this we report evidence of predictability for the major financial centres from the first dividend-price ratio principal component and for all markets from the first stock return principal component. The third dividend-price ratio principal component has predictive power for the majority of the series and suggests a secondary global factor with information content that is common to the majority of markets. Equally, it is also of key interest to note that the first and significant third dividend-price ratio principal coefficients are all positive and this is consistent with the view that a higher dividend-price ratio predicts higher returns, i.e. that the ratio proxies for expected returns and an increase in risk leads to lower prices, a higher ratio and higher expected future returns. Overall, these results demonstrate the role of global (and to a lesser extent local) information within stock return predictability for sixteen markets.

Dividend growth predictability
The above analysis leaves open the issue of how the second principal component relates to the information content within the dividend-price ratio. The returns predictability relation is only one side of the dividend-price ratio. The other side is the dividend growth predictability relationship. Table 7 presents the dividend growth predictability regression for each market with the three identified principal components and the local dividend yield component derived as above. We also include a lag of the dividend growth in each regression, but do not report it. These results present two interesting conclusions. First, looking at the local market dividend-price ratio parameter, we can see that this is statistically significant (and correctly, negatively, signed) for 13 markets (with a further market significant at the 10% level). Only for one market (the US) is the parameter significant and incorrectly signed. This significance can be contrasted with the returns equation, where the local market dividend-price ratio was only significant for four markets (France, Hong Kong, Singapore and the UK). This result suggests that while movement in returns is dominated by global information, movement in dividend growth rates is related to local information. Second, examining the second principal component, which was not significant in the returns regressions, we can see that it is significant and negative for 11 of the considered markets. Moreover, the negative reported relationship is consistent with the view that higher expected dividend growth is associated with a higher stock price and hence a lower ratio.
The results presented here suggest important conclusions with respect to both asset pricing and international finance. With regard to international market co-movement, the principal components results demonstrate the presence of a global component in international expected and actual returns, which in turn have a greater influence on local market returns than movements in local market expected and actual returns. Moreover, over 80% of the variation in expected returns

Table 7. Dividend growth predictive regression with local and global (principal components) information
Notes: Entries are coefficient values with Newey-West t-statistics for the predictive regression, Equation (6) but with dividend growth replacing stock returns as the dependent variable, of home dividend growth on lagged log dividend yield principal components and the lagged home dividend yield, which is defined as the residuals from a regression of the log dividend yield on the principal components. Home lagged dividend growth is also included in the regression but not reported. *Level of significance at 1%. **Level of significance at 5%. ***Level of significance at 10%.

Country
Home (dividend-price ratio) can be accounted for by just three principal components, while just under 70% of the variation in stock returns themselves can be accounted for by three principal components. Thus, these results confirm the integrated nature of international stock markets. Furthermore, as movement in expected returns is driven by considerations of risk and the risk premium, these results suggest risk and hence, economic conditions are equally linked.
With regard to asset pricing, these results suggest that contained within the dividend-price ratio is information that is useful for both stock return and dividend growth predictability. A debate has grown up surrounding whether movements in asset prices arise from changes to expected dividend growth or expected risk premium (see, e.g. Ang, 2012;Cochrane, 2011). This analysis, which considers the separate components within the dividend-price ratio, highlights the importance of both avenues. The results here demonstrate important differences between returns and dividend growth predictability and the potential confounding effects within the dividend-price ratio. By using the principal components analysis we are able to identify the separate global and local information that contain differing predictive power for stock returns and dividend growth. Noticeable in the results is that returns are driven almost solely by global information while dividend growth predictability arises from both global and local information. Furthermore, these results support the view of Menzly et al. (2004) that the analysis of the dividend-price ratio per se will include confounding effects which mask predictive power. Separating the components within the ratio is required to reveal that power.

Forecasting
Having established evidence above for the predictive power of global and local information components, this section proceeds to provide further evidence through out-of-sample forecasting. Following Rapach et al. (2013) as well as the earlier work of Campbell and Thompson (2008) and Welch and Goyal (2008) we utilise the out-of-sample R 2 approach as such: where τ is the forecast sample size, r t is the actual return, r t,alt is the forecast value obtained from the alternate model of interest and r t,base is the baseline forecast. When the R 2 oos value is positive then the alternative predictive model has greater forecasting power than the baseline forecast model. The baseline model chosen is the historical means model, i.e. the stock return regressed upon only a constant. The alternative models are given by the dividend-price ratio as in Equation (1), this ratio augmented by the US return as in Equation (2), the global and local model information model based on the US dividend-price ratio and returns and the global and local information model based on the principal components analysis, Equations (4) and (6) respectively. For ease of interpretations, these are referred to as Models 1 through 4 respectively in Tables 8 and 9. To supplement the out-ofsample R 2 analysis we also conduct the test of Clark and West (2007) which tests the null hypothesis of equal predictive accuracy between the baseline and alternative model against the alternative hypothesis that the competing model is preferred. We conduct the forecast exercise twice, first using a fixed rolling window of five years and second using an expanding recursive window that begins with the first five years. These two approaches allow us to mimic trader behaviour in real time. In conducting the forecasts we must be careful not to use any information that would not be available to a market participant. Thus, all estimations are conducted either in a rolling or recursive fashion, including the regressions of local information on global information and the principal components analysis. Although we maintain the number of principal components at three. Table 8 reports the results of the five-year rolling forecast exercise, while Table 9 reports the recursive forecast results. In each table the results are reported for each alternative forecast models against the benchmark historical mean, which is also estimated in either a rolling or recursive fashion, respectively. The entry is the out-of-sample R 2 value, R 2 oos , for which a positive value indicates preference for the alternative model, while the number in parentheses represents the p-value from the Clark and West test.
Examining the rolling forecasts, Table 8, we can see that for the usual single market dividend-price ratio (Model 1) regression then only once at the 5% level and once at the 10% level is the historical mean model significantly outperformed. Although, for all markets the R 2 oos values are positive indicating some reduction in the forecast error. For the two models that include US-based predictor variables (Models 2 and 3) the performance is slightly improved, but only marginally. Again, all the R 2 oos values are positive indicating an improved forecast performance over the historical mean, however, it is only Table 8. Out-of-sample R 2 values-rolling forecasts Notes: Entries are the out-of-sample R 2 values from Equation (7) with the p-values in parentheses from the Clark and West (2007) test of equal predictive accuracy. The models are given by: Model 1-the dividend-price ratio as in Equation (1); Model 2-this ratio augmented by the US return as in Equation (2)  significantly for two (one) markets at the 5% (10%) level for model that includes US returns (Model 2) and for three (three) markets at the 5% (10%) level for the model that includes the US dividend-price ratio and returns (Model 3). In contrast, for the model that separates global and local information using the principal components analysis (Model 4), we see much greater evidence of superior forecast power. Again all the R 2 oos values are positive, supporting a lower forecast error over the historical mean. Moreover, for each market these values are at their highest level with this model in comparison Table 9. Out-of-sample R 2 values-recursive forecasts Notes: Entries are the out-of-sample R 2 values from Equation (7) with the p-values in parentheses from the Clark and West (2007) test of equal predictive accuracy. The models are given by: Model 1-the dividend-price ratio as in Equation (1); Model 2-this ratio augmented by the US return as in Equation (2)  to the previous three forecast models. For nine of the markets there is a statistically significant improvement at the 5% level, while the remaining seven markets are significant at the 10% level.
For the recursive forecasts the results are similar in nature, although there is, arguably, a lower degree of statistical significance overall. As with the rolling forecasts, all the R 2 oos values are positive, indicating that the competing forecast models provide a lower mean squared error than the historical mean approach. In terms of whether there is a statistical difference on the basis of the Clark and West (2007) test, for the dividend yield (Model 1) that occurs for three markets at the 5% level and a further market at the 1% level. For the two forecast models that incorporate US variables the number of markets with statistically significant forecast differences are five (three) and four (two) for the US returns (Model 2) and US dividend yield and returns (Model 3) respectively at the 5% level (additional markets at the 10% level). For the principal components based approach (Model 4), seven markets have a statistically lower forecast error at the 5% level (with a further five at the 10% level). Thus, for two markets there is no improvement, which contrasts to the rolling approach where all markets exhibited at least a 10% significance level forecast improvement.
Overall, these results support the following conclusions. First, as observed elsewhere, the dividend yield only model rarely provides a significant forecast improvement on the historical mean model with any gain in reducing the forecast error is minimal. Second, incorporating US-based variables improves the forecast power (as in Rapach et al., 2013). Although our results are relatively modest compared to theirs, our sample of markets differs. Third, when we utilise the principal components approach, which allows separation of both local and global information as well as information that relates to stock return and dividend growth predictability, then the forecast improvement is substantial. Finally, the results demonstrate some differences between the rolling and recursive approaches in terms of statistical significance, which is stronger under the rolling scheme.

Summary and conclusion
This paper has sought to examine the influence of global and local factors on stock return predictability for a range of international markets. Evidence of global factors within stock market predictability would speak to the literature on market integration, while evidence of predictability is important in asset pricing models. We consider three basic approaches in identifying predictability. First, as a baseline model, we estimated own country dividend-price ratio predictive regressions. Second, to capture global information we include US-based variables, stock returns and then returns with the dividend-price ratio. Third, we attempt to capture global information more widely (than just US variables) through a principal components approach. In addition to examining the in-sample predictive power of these different models, we consider an out-of-sample forecast exercise and compare the results with the historical mean model.
The results obtained suggest the following conclusions. First, with respect to the home market dividend-price ratio model only, there is very little evidence of predictive power. Second, adding US-based variables enhances the predictive power of the model, with US returns notably significant once we orthogonalise US and local market variables. Third, using principal components analysis we are able to identify a global component for both the dividend-price ratio and stock returns, for the former of which has predictive power for the major financial centres in the US, UK and Asia, while the latter has predictive power across all markets. Interestingly, a second component for the dividend-price ratio appears to relate to dividend growth predictability. Fourth and final, this latter principal components-based model provides the best fit both in-sample and out-of-sample, obtaining the highest in-sample adjusted R 2 for the majority of the markets and the highest out-of-sample R 2 for all markets.
The main implication and contribution of this paper can be stated as follows. The results presented here stress the need to separate different components in the dividend-price ratio to reveal the full predictive power of the ratio. Here, we have identified components that relate to global and local information and identified that different components relate separately to returns and dividend growth predictability. This is important in explaining the mixed nature of the literature. Furthermore,


s2fieldsofstudy: Economics Business

text:
Money Supply as a Conduit of the Consumption in the Saudi Economy: A Co-integration Approach

This paper focuses on exploring the conduit through which wealth, aggregated by broad money supply, affects the real private consumption in the Saudi economy. In other words, the relationship between money supply (proxy of wealth), among other variables, and the real private consumption function. This study covers the period of 1985-2014. The short and long-run relationships are estimated using co-integration techniques. The results support the existence of positive effects of wealth over private consumption. Also, consumption is affected negatively by changes in prices in the long-run.

Introduction
Not recently, there has been a great deal of literature that deals with the role of money in consumption function as an explanatory factor of the behavior of the consumption function. The aim of such inclusion is to capture the real balance effect. However, the existence of the money supply in the consumption function may give some insight especially of less developed countries (LDCs, hereafter). Given the characteristics of LDCs, Saudi Arabia reflects an example that should be examined and tested. Saudi Arabia has specific features similar to those of less developed countries, LDCs: dependence on one exhaustible resource, i.e. oil, great participation by the public sector in economic activities, limited integration between the dominant foreign exchange earner (the oil sector) and the rest of the economy. The quadrupling of oil prices which started 1973/74 has benefitted the economy by providing financial resources for investment and to further economic growth and development. The oil windfall also influenced private consumption in a positive manner. For example, private consumption constituted 53 percent out of total expenditure on GDP in 1985 to about 32 percent of total expenditures on GDP in 2014. (Figure 1). The purpose of this paper is to review and examine empirically the role of money in the private consumption in Saudi Arabia. The co-integration method will be applied. Algahtani, and Alhiyari [3], argue that at least for Saudi Arabia, the money supply M1, was a good measure for wealth and helps to explain the behavior of the private consumption. These authors concluded remarkably that money supply in Saudi Arabia had positively affected the private consumption, contrary to the findings for Iran by Mehdizadeh [15].
The broad hypothesis being tested is that, the inclusion of the money supply among other independent variables, proxy for wealth, in the private consumption function for the Saudi economy spanning the time period from 1985-2014, does a good job in explaining the behavior of real private consumption. It is likely that inflation will exert an influence on private consumption through the effect on real cash balances. Therefore, in order to capture the real effect of the wealth on private consumption, one considers the wealth variable with other independent and relevant variables, such as consumer price index, CPI. The organization of this paper as follows. Section 1 an introduction. Section 2 develops the theory of the macro consumption model. Section 3 presents the statistical results and their discussion for the real private consumption functions with different measurements, and section 4 presents a summary of the results and their implications.

The Theoretical Macro Model
As time passes, the accumulation of wealth involves a considerable macroeconomic adjustment on the aggregate level, such as consumption. Keynesian theory explains the consumer behavior on the premise that consumption depends on income and the interest rates. The assumption of this theory is that, the spending unit is not highly responsive to change in the rate of interest over the economically meaningful range, and that its behavior is especially inelastic at the lower end of the range [16]. Therefore, the main conclusion of the Keynesian macro theory is that, current income is the main determinant of the level of the current consumption expenditure and that the marginal propensity to consume out of income is less than one. "However, there are factors affecting personal consumption besides disposable income, such that taxes and expectations of the relation between present and future income [16]".
Friedman's permanent income hypothesis stresses the constant proportional relationship between personal consumption and permanent income. Observed income can be broken into permanent and transitory components. Moreover, this theory is criticized on the grounds that at least one third of the windfall is consumed. Bodkin [5], points out that the consumption of the windfall is between 0.7 and 0.9. Although permanent income and life-cycle hypotheses tried to reconcile the differences between long and short-run consumption, they stressed the importance of wealth as a factor that influences consumption. Despite the potential important effects of the interest rate as an explanatory variable in the consumption function, numerous studies tend to neglect it in the case of LDCs due to the lack of serious interest rate data.
Lagged income as a determinant of consumption function is discussed extensively in the literature, with different models forwarded to justify its inclusion in the model. These models include the permanent income and life-cycle hypotheses. Economists argue that the purpose of inclusion of the lagged income variable is to represent the permanent income if the time period is too short, to allow for complete adjustment of consumption to the new level of income to occur. Needless to say, the period over which one measures the observations needs to be long enough to allow for complete adjustment [4]. Using the money market equilibrium condition, where Y is income and R is the rate of interest, the actual nominal quantity of money can be expressed as: Assuming a specific functional form and taking log, we have: Ln Mt = ln P + α ln y + γ ln R A rise in price will not increase the demand for money in real term because the actual nominal money rises by the same proportion.
Following the standard literature as a starting point, the private consumption function is specified as follows: We can rewrite it in the following form: Where g 1 and g 2 are the marginal propensities out of income and aggregate money supply (there are no government bonds). The total change in consumption consists of two parts: the direct change in consumption as income changes, and the change in consumption due to the change in wealth represented by M t .
Liquid assets symbolize the portfolio balance or wealth effect. Households need a certain liquid asset, and if the real value of liquid assets diverges from the desired level, saving will tend to adjust, and hence, consumption. In this case, liquid assets serve as a buffer stock for households. The proxy for the liquid assets (M t ) is a broad definition of the money supply (M 3 ). Furthermore, following the standard money demand process, and following [4], [1]. I assume that the adjustment of the actual to the desired money supply is such that: Where M t * is the desired money supply, and Ø represents the adjustment coefficient which takes a value between zero and one. A further assumption is that, the desired money supply depends on real income and the change in the price level. Assuming a specific functional form, such that: Substitution yields: Or: Where: Inflation is expected to exert two separate influences on saving. It encourages the holding of real assets rather than nominal ones. At the same time, inflation creates a feeling of uncertainty and pessimism about the future which encourages saving [10]. The rationale for including CPI is, to reflect the erosion of the real value of the consumer's total wealth [10]. Moreover, the coherent justification for the inclusion of CPI is that, expectations about increases in price will cause a fear of erosion of the real value of the financial assets shifting the demand for real assets at the expense of financial assets. So, the impact of future inflation on consumption and saving will depend largely on the behavior of households and on the degree of indexation of real income and interest rate. [14]. Another explanation is that, inflation may change the distribution of income among households and affect saving behavior and hence, consumption [10]. Others, suggest that, the price level will reflect the wealth effect. Their main reason is that; price level exerts influence on consumption through the reduction of real balances. If the accumulation of the real assets is the predominant motivation, one would expect a negative effect of expected inflation on consumption. Howard termed it as flight from currency [10].
The ad hoc specification form of the private consumption function, is the following: C p =g 0 +g 1 Y t +g 2 f 0 +g 2 f 1 Y t +g 2 f 2 P t +g 2 f 3 M t-1 +U t (9) Rearranging terms yields the final form: C p =(g 0 +g 2 f 0 )+(g 1 +g 2 f 1 )Y t +(g 2 f 2 )P t +(g 2 f 3 )M t-1 +U t (10)

The Empirical Results
In order to see the effect of money supply (Proxy for wealth) on the private consumption function, one considers the real national income lagged one year. The rationale behind using lagged real national income is, the existence of time lag between receiving income and spending it. However, the use of consumer price index (CPI) is to reflect the power of inflation that exerted on private consumption through the decline of real income. Due to the stationarity of the economic variables, it is necessary to perform the unit root test and error-correction methodology (ECM). Therefore, the determination of the order of the integration of each variable used in this study, tells us whether exists stationarity or the variables include a stochastic trend, i. e., how many times each variable needs to be differenced in order to reach stationarity. Although, time series literature suggests that variables are stationary, but if nonstationary, spurious results are likely to appear. To overcome nonstationarity problem, first differenced I(1) variables are applied [8].

Unit Root Test
Due to the stationarity of time series, it is of interest to perform the unit root tests and the error-correcting methodology. To achieve this task, Augmented Dickey-Fuller (1987), (ADF), and Phillips and Perron (PP) (1990) tests are executed. Results are reported in table 1. Both tests showed that all variables are stationary at the difference in the ADF test. Whereas, lagged money supply is stationary at level only. Furthermore, all considered variables are integrated of order one or I(1). Although, KPSS test is being used, its interesting results were not reported.
Using Engle and Granger, [6], a single equation method is built on the assumption that all variables in the model have to be integrated of the same order. Thus, all variables in the model are integrated of order one, I(1). To do so, long-run relationships between Y t and X t are of the form: Y t = β 0 + β 1 X t + e t Utilizing OLS the above equation is estimated and the residual saved and tested for stationary. Moreover, if residual e t is stationary, then Y t and X t are co-integrated and have long-run relationships. Applying ECM, all short run variables are significant at 5 percent level. The residual (U t-1 ) has been tested for stationarity. The results came up significant at 5 percent level, indicating long-run relationships between real private consumption (RPC) and the rest of the independent variables in this analysis, table 3.   Notes: t-statistics are in parentheses. The *asterisk denotes significance at 0.05% level.  Notes: t-statistics are in parentheses. The *asterisk denotes significance at 0.05% level.

Co-integration Methodology
Stationarity tests are carried out using ADF and PP. All variables in the model are not stationary. However, they became stationary after first difference. From table 2, trace test indicates the existence of 4 co-integrating equations at the 5 percent level. So, null hypothesis of no co-integration is rejected by trace test. Maximum eigen statistics indicates the existence of a 1 co-integrating equation at the 5 percent level. Thus, null hypothesis of no co-integration is rejected too. Furthermore, Johansen test confirms the rejection of the null hypothesis of no co-integration between RCP and the rest of the independent variables, RNI, RMt-1 and CPI.

The Results Discussion
The model results presented in tables 3 and 4. It shows that real private consumption is extremely influenced by real aggregate money supply, real income, and CPI. All variables coefficients are significant at 5 percent level. The short-run results support the findings of Algahtani and Alhayari. [3].
Their estimates confirm that the wealth variable plays a major role in determining private consumption in Saudi Arabia. Furthermore, adding the price level to the equation resulted in an improved understanding of the behavior of the wealth variable. The long-run influence of wealth (lagged one period) or long run marginal propensity to consume out of wealth is about 18 percent, while real income (lagged one period) is about 5 percent. The sign of inflation ought to be negative, in order to justify the argument that inflation exerts influence on consumption through reduction of real balances (M/P). However, the coefficient is positive and significant at 5 percent level. We observed strong long run relationship between private consumption and the broad money supply. The ECT (-1) coefficient shows the percentage in which disequilibrium in the short run be adjusted to its long run equilibria. The more ECM (-1) coefficient, the longer run adjustment. The estimates indicate that the error correction term has a negative sign and significant at 5 percent level. This finding shows that, error correcting term, corrects the disequilibrium of the system at the rate of 97 percent annually. In other words, in each period the adjustment rate of marginal consumption expenditure of private consumption to its long run relation is about 97 percent. Vector error correction estimates which reported in table 4, shows that the coefficient of the co-integrated model, or the error correction term is positive and is not significant at any level. So, there exists no long-run causality running from the independent variables of the model towards real private consumption, RCP. The long-run negative sign of the CPI, indicates that Saudis reduce their consumption once they realize that the price level has risen. In order to avoid the loss in real wealth which is in the form of financial assets, consumption will be reduced and the demand for real assets will go up. Another possible justification for this result, Saudis are hedging against inflation by abstaining from current consumption and accumulating wealth in the form of real assets, such as gold and lands. The private consumption function could be adversely affected by inflation. Thus, as liquid assets decrease, private consumption decreases. Further tests have been done to assure the goodness of fit. The model showed no serial correlations, nor heteroscedasticity. The residuals are normally distributed.

Conclusion and Policy Implications
This paper has sought to investigate the role of broad money supply on real private consumption function in a major oil producing country, Saudi Arabia using real factors, rather than nominal ones. The data were in millions of Saudi Riyals (National currency), and in nominal values. To better understand the role of broad money supply as a conduit of the real private consumption, work has been done to develop and estimate an ad hoc macro econometric model. Real income and consumer price index have been added and tested. The series covered the period of 1985-2014, were integrated in order to evaluate long-run relationships between real private consumption and the rest of independent variables. The above analysis revealed that, the use of broad money supply variable, i.e. M 3 , as a proxy for wealth, is meaningful even if it is used with other relevant variables. It is worthwhile to note that, our findings support the hypothesis that inflation does affect the consumption. [7], [14]. A rise in inflation will cause a reduction in financial assets leading to a reduction in the private consumption in the long-run. Moreover, consumers adjust their consumption in the long run once they correct their expectations. Immense government expenditure, which is transmitted into the stream of money supply should be watched out so as to maintain stable prices, especially if we want to keep the economy on track.


Trends of Seeds in the Era of Climate Change – An Issue of Concern towards Sustainability

Background/Objectives: India is an agro-based country where seed is the heart of agriculture. Agood quality seed makes a good crop resulting in high yield and better quality for better economic growth. The objective of the study is to explore the seed trends and critically assess the trends with the changing climatic conditions of Indian agriculture. Methods/Statistical Analysis: The paper has undergone an exhaustive literature to find out the changing policies of agriculture seeds. The paper clearly shows that Seed can make agriculture sustainable. With time National Seeds Corporation have come up with several policies which has made promotion of agriculture convenient. The paper is based on secondary data obtained from ministry of agriculture and national seeds corporation. The study shows the trends in seed development since 1740 till 2014. Findings: The paper undergoes to concern issues of several research papers for making seeds sustainable in the era of climate change. The paper shows how the seeds policies have tried to adopt the seeds to climate change. Since 1740 till 2014 India’s climate has shown a drastic change. The paper shows how the seeds policies have tried to adopt the seeds to climate change. These seeds policies have not only tried to improve the productivity of plants but also have tried to move the seeds towards the path of being sustainable. Applications/Improvements: This has been an issue of concern in modern era in terms of economic, environmental and social perspective leading to sustainability.

Introduction
India is an agro-based country where seed is the heart of agriculture. A good quality seed makes a good crop resulting in high yield and better quality for better economic growth. Ministry of Agriculture, Government of India acts as a nodal agency for agricultural activities all across India and is making significant contribution in Indian agriculture since long time. With the development of crops people realized the importance of the quality of seeds with time and save it from deterioration 1 . With this National Seeds Corporation came into existence which undertook the function of production, processing and marketing of agriculture seeds.
National seeds corporation is indulged in protection of all varieties of seeds of all kinds of agricultural crops. The storage, grading and packaging is done by technical machines which gives fine results. The seeds are then tested in laboratories located at New Delhi, Secunderabad, Pune, Bhopal and Kolkata. The seeds are tested in such a way that the quality and viability of the seeds are estimated with changing climate. The objectives of the study are to enlist the trends of the seeds policy in India and how the upcoming trends have led the seeds to survive in the unpredictable climatic conditions of India. The paper also comes out with some limitations in every seed policy which has led it to come with new trends. The current seed policy also has some limitations and needs some improvements in the era of climate change. The present paper explores the seedtrends and critically assess the trends with the changing climatic conditions of Indian agriculture. The paper is based on secondary data and comes up with some limitations in the present seed policy in regards to climate change. The Indian seed industry comprises of a large number of private and public seed sectors. The public seed sectors comprises of a nodal agency National seeds corporation, State Farm Corporation and thirteen state seed corporations. All private and public seed sectors are responsible for storage of the seeds, seed multiplication and marketing of the seeds. All the public seed sectors work under the guidance of Indian Council of Agriculture Research and State Agriculture Universities 2 .

National Seeds Corporation
National Seeds Corporation was established in 1963 and acts as a nodal agency for seed multiplication. Since time N.S.C has been working effectively for seed multiplication. It has produced numerous foundation seeds and certified seeds. It is responsible for proper storage of the seeds. The stored seeds undergo proper certification by crop inspection and labelling. Finally if the seeds are found unfit under proper testing such seeds are separated from the good seeds. The quality of seeds is maintained efficientlyandthe good quality seeds are graded and sorted properly through effective machines. The seeds are processed with proper handling and then packed for marketing. The seeds are distributed under proper sales promotion and communication. In order to make the seeds climate adapted, the seeds are tested for it's genotypes and it's resistance against drought and heat.

Seed Production System in India
The seed development process in India is involved with multiplications of seeds giving rise to three types of seeds namely Breeder seeds, Foundation seeds and Certified seeds as presented in Table 1.

Role of Seed Development Programmes and Policies
One that is directly affected by the changing seeds policies is the farmer 6 . Framers are the ultimate victims of these seed policies. It was found that large farmers had an access to hybrid or breeder seeds while small farmers were restricted to low quality seeds since the price of high quality seeds was too high and the subsidies received was also very low. With time seeds policies began to change every year which acted as a supporting yardstick for the farmers. These seeds policies also acted as a beneficiary for the breeders. The seed policies which evolved since 1740 has been shown in the Table 2.

Seeds Type Characteristics Tag
Breeder Seeds It is also called as nucleus seed because the seed is genetically pure produced by the breeder under I.C.A.R, State agriculture universities, state seed corporation, non-government organizations and KrishiVigyan Kendra. The production of breeder seed is evaluated by I.C.A.R-D.A.C in the seed review meeting held every year. These seeds are lifted directly for production of foundation seeds 3 .

Golden Yellow
Foundation Seeds It is the progeny of breeders' seed entrusted by agriculture departments, private seed producers, state seed corporation, S.F.C.I and N.S.C. It must meet the standards of seed certification. This seed is tested at field and laboratory 4 .

White
Certified Seeds It is the progeny of foundation seeds produced by state governments, S.S.C, Department of agriculture farms, cooperatives etc. This seed is then distributed to a number of channels at block and private agencies, village level, cooperatives and state seed corporations. The production of certified seed is done by farmers rowing under contract agreements in N.S.C. and S.S.C 5 .

Azar blue
Electronic copy available at: https://ssrn.com/abstract=2867313 Breeder seed known as the pure seed came into existence.
The climate was far better than today's so there was very little concern given to the climate.  The Private sector has increased it's role since last few years and the number of companies engaged in private companies has also increased tremendously The Public sector still has a major role in the production of high quality seeds of cereals, pulses and oilseeds.
This sector focus on production of hybrid seeds It is dominant in the production of seeds of oilseeds, cereals and pulses for many years It has a major share in the production of seeds of maize, sunflower and cotton 8 .
The public seed sector has 15 state seed corporation and 2 national level seed corporations namely national seeds corporation and state farm corporation Such firms are heterogenous 9 . Such firms are homogenous Private seed sector is dominant in production of vegetables and horticulture Electronic copy available at: https://ssrn.com/abstract=2867313

Role of Public and Private Seed Sector
Private and public seed sectors play a major role in seed development. The Table 3 gives a comparative picture of private and public seed sectors. The seed sectors in private and public sectors have encouraged seeds viability. Private sector has a major share in seed production and has improved the trends of the upcoming seeds.

Variety Registration
The registration of a new variety undergoes three steps for seed evaluation: • The reaction of the varieties to several diseases is studied by pathologists through testing in Initial Yield Evaluation Trial.
• Then the samples are tested in large locations in large zones fordisease, pests and the traits are studied. This type of test is known as uniform regional trials. • A number of scientists are accumulated in a workshop and the proposal to release a variety is discussed on the basis of the tests.

Protection of Variety in India
Plant protection variety and farmer's right came in existence in 2001 with the objective to encourage high quality seeds, protecting the farmers who try to make available all kinds of resources for development of a new variety leading to development of agriculture. Many intellectual property rights like patents and copyrights are provided to the breeders to protect the novelty, distinctness and stability of their seeds. Such rights are provided to persons who are breeders or assigned to be a breeder, any university and farmers who claim as a breeder of any particular variety of a crop.

Seed Certification System
In seed multiplication and production it is very necessary to have a check on the quality control. For this a legal sanctioned system came into existence known as the seed certification system. The seed certification started in the 20 th century. The Table 4 given below shows the evolution of seed certification. It were the Swedish workers who first initiated seed testing. With time scientists started working and then field testing came into evolution. The seed certification system consists of some minimum certification standards under which seeds are evaluated and their acceptance as well as rejection depends upon these standards. The seeds are tested for their texture, size, texture, moisture percentage, germination percentage, viability and other parameters. Since seeds are very sensitive creature of nature and has to be handled with great care as the yield of a crop is largely dependent on the seeds from which it has been generated.
The seed certification came up with the minimum standards of seed regarding quality and purity. The objectives of seed certification are develop genetically pure seeds and to develop viable seeds. Seed certification requires general, field standards and seed certification agencies. The seed certification involves six phases and involved field inspection, seed processing, seed labelling, seed testing and seed control. In the first phase of the seed certise of the seed cer dependent on the seeds from which it has been generated. great care as the yield oisture percentage, germinfication seeds are verified on the basis of it's texture, size and type. Then in the second phase seeds are sent for application where the seeds are grown for testing their quality. After that the seeds are inspected regarding the minimum seed certification standards evolved by breeders. After the inspection of seeds they are sent for sample analysis. After successful results these seeds are processed and packed. Lastly a certificate is issued with a tag to the seeds which consist of the characteristics of the seeds 11 as shown in Figure 1.

Seeds towards Sustainability in the Era of Climate Change
In the era of climate change there are policies for adopting the seeds to retain the diversity of the seeds. The collection of seeds must include a number of genotypes for each species. National Seeds Corporation stores seeds as buffer stock for supplying the seeds during uncertainties of climate change. Irrespective of the climate change, the population of the country is increasing day to day while the foods are inadequate. Therefore, seeds must be provided with proper care in order to protect them from climate change. New crop varieties and seed banks must be introduced to fulfil the demands for food in the coming future. The diversity of seeds must be encouraged by providing several opportunities to the farmers to face the challenges of climatic change. Due to population rise, pollution control has also become a major cause of concern.
The increase population has resulted into global warming and drastic climate change.
According to meteorology, the year 2014 has been the hottest year of the earth. Now the problem arises how to save the seeds from such high temperatures. Taking this as a major issue, Indian Council of Agriculture Research has been trying to convey the message to the farmers for growing crops which are resistant to drought and heat. National Seeds Corporation has been trying to store such seeds to make it easily available to the farmers. Plant breeders have adopted management practices for their seeds to increase the self-life of the seed. According to state seeds corporation, the approximate life of the seed is 6 months to 2 years. Government has been providing irrigation facilities to the farmers to reduce the dependency on rainfall. In the era of climate change, breeders are trying hard to produce such seeds which can face the threats of climate change. Today the major problem lies in the management of such seeds to have a better crop. Many practices such as tillage, contouring, terracing, bands, green house and many others have been adopted to protectthe crops from erosion and retain the moisture level.
Several policies regarding climate adaptation are encouraged. A variety of crops and seed banks must be introduced. Management practices should be developed, seeds which are resistant to drought and heat must be promoted. Many workshops has been introduced to develop a model of climate on the seeds. The impact of climate on the seeds must be introduced in the seed policy itself 12 . Storing seeds are done by all the seed banks but the genotypes become infeasible. Seed banks are found to beinsufficient to retain the genotypes 13 . According to ISU seed testing laboratory before variety releases seed has to undergo several tests like germination tests which determines the germination capacity of seed during cold or hot temperatures. Accelerated Aging: This is done for estimating the seedlings in the field. Tetrazolium test: Under this test a seeds are placed in the tetrazolium solutions to detect the damages or problems of the seed lot due to flood, frost or other calamities. There are several other tests like herbicides bioassay, fast green, polymerase chain reaction and immunoassay for seed testing in order to make the seeds adopted to climate change 14 .
The seeds of Kharif crops are sown in the month of July which requires a good rainfall. Due to climate change there is spatial and temporal variations in rainfall which ultimately affects the crops. Lack of erratic rainfall results in lower underground water table which leads to reduce the carbon content of the soil making it easier for the pests to survive. Delay in the monsoon season results in low rainfall, less water storage and availability of water for irrigation. This has resulted into shift in the sowing dates of the kharif crops and rescheduling of water and nutrients. Delay in sowing of kharif crops results in late Electronic copy available at: https://ssrn.com/abstract=2867313 harvesting of crops and again late sowing of rabi crops 15 . The unpredictable rainfall in rabi season leaves an impact on critical grain crops. Low availability of water and high temperatures results in low productivity of crops. In the era of climate change water shortage has become problematic for agriculture 16 . Blue water is the indicator of rivers, lakes, ponds and aquifers which is the main source of irrigation for crops. Rain water constitutes of oxides of nitrous and sulphur (acid rain) or constituents of salt with some trace elements of pollutants which again deteriorates the seeds or affects the nutritional content of the crops. The salinity can be destroyed by irrigation which maintains the fertility of the soil 16 .
Today the major issue of concern is the evil impacts of climate change on agriculture and the evil impacts of agricultural and water practices to climate change. This must be dealt through in order to attain sustainability. The reason for increasing global warming is not just pollution but also the agricultural practices which though increases the yield but is leaving an adverse impact on climate. According to Richard M. Adam et al. climate changes strongly affects the yield of the agricultural crops. High temperatures leads to low yield and high yield variances while high precipitation leads to high yield and reduces the variability.

Conclusion
The changing trends in seed policy have done a great job in the seed industry. The seed policies have led to the development of genetically pure seeds. These policies have led the seed to undergo several tests before being registered as a variety. With time breeders are now provided with intellectual property rights which have led to maintain the novelty, stability, distinctness and uniformity of the seed. Seed is the most critical part of the crop and for developing a sustainable agriculture it is very important to develop a good seed. Seed leads to a sustainable agriculture 17 . Seed must be protected from pests, diseases and weeds. The seed policy leads to variety development and improvement of seeds. This has resulted into increase in the production of breeder seeds. The national seeds corporation came up in 1963 with the aim to protect the seeds from deterioration. It is responsible for seed marketing, promotion and distribution. Indian agriculture has a huge amount of crops and several researches are still going on to improve the agriculture scenario. The seed certification has an important role in seed industry. It has led to minimum seed standards which have resulted into pure and viable seeds. With time seeds have been found efficient to adapt themselves in the climate change 18 .
Seed policy must be implemented effectively because ineffective implementation of seed policy results in low production of pure seeds, poor seed certification standards and poor distribution of seeds. Private seed companies have promoted seed sector to a high level but has certain limitations. They have high risks with high costs. There is lack of credit facilities in seed sector which has resulted into poor demand of seeds. The variety control system has resulted into the limitations of new varieties 19 which is again a matter of concern for the climate adaptation. Comparing private sector with public, the government provides subsidies which leads to poor promotion of private seeds. The agencies have poorly implemented the seed certification standards.
Based on the findingsfrom secondary data, an effective implementation of seed policy must be there regarding climate change 20 . The production of breeder seeds must be increased. Measures should be developed to encourage private seed companies. Sustainability of seeds should be given major concern. There should be further research for determining demand and supply of seeds. Seed farmer groups should be formulated for marketing at state levels.