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Háskóli Íslands, Háskólinn á Akureyri, Háskólinn á Bifröst
Íslenska Hugvísindi og tungumál Tölvu- og stærðfræði Viðskipti og hagfræði Raunvísindi Félagsvísindi Heilsugreinar Verkfræði Aðrar greinar Háskólabækur
Háskóli Íslands Félagsvísinda- svið Hagfræði Haust 2017 Vor 2017 Viðskiptafræði Viðskiptafræði með vinnu Alþjóðasamskipti Blaða- og fréttamennska Félagsfræði Félagsráðgjöf Fötlunarfræði Lögfræði Mannfræði Námsráðgjöf Opinber stjórnsýsla Safnafræði Stjórnmálafræði Þjóðfræði Heilbrigðis- vísindasvið Hugvísindasvið Verkfræði- og náttúruvísindasv. Háskólinn á Akureyri Háskólinn á Bifröst Háskólinn á Hólum Háskólinn í Reykjavík Landb. háskóli Íslands
Leikjatölvur, Playstation 4, Playstation 3
Íslensk tónlist, Erlend tónlist, Vínylplötur
0-3 ára, 3-5 ára, 6-8 ára
Klassískar bókmenntir, Almenn skáldverk, Ástarsögur og erótík
Ást og Erótík, Grín og gaman, Handavinna
Ray-Ban, Michael Kors, Emilio Pucci
Vítamín og fæðubótarefni, Heilsuvörur, Brúsar
Borðbúnaður, Pottar og pönnur, Eldhúsáhöld
Gjafabréf, Myndir og veggskraut, Kerti og kertastjakar
Caffitaly hylki, Nespresso hylki, Te
Áhöld fyrir bílþvottinn, Bón og hreinsivörur, Efni og vökvar
Útilegu- og útivistarvörur, Útivistarföt, Sjónaukar
Hjálmar og hlífar, Hlaupahjól og bretti, Barnareiðhjól
Lego Bionicle, Lego City, Lego Creator
Kaffivélar, Brauðristar, Heilsu og samlokugrill
Pappírsvörur, Ritföng og aðrar smávörur, Skjalavarsla
Íslensk tónlist, Erlend tónlist, Vínylplötur
Heimkaup Námsbækur Háskólabækur Háskóli Íslands Félagsvísinda- svið Hagfræði Vor 2017 Econometric Analysis
Námskeið HAG202F Hagrannsóknir . - Útgáfa: 7 - Höfundur: William H Greene
Rafbók til leigu í 365 daga
For first-year graduate courses in Econometrics for Social Scientists.
This title is a Pearson Global Edition.  The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States.
This text serves as a bridge between an introduction to the field of econometrics and the professional literature for graduate students in the social sciences, focusing on applied econometrics and theoretical concepts.
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Part I The Linear Regression Model
Assumptions of the Linear Regression Model
Linearity of the Regression Model
Data Generating Process for the Regressors
The Least Squares Coefficient Vector
Algebraic Aspects of the Least Squares Solution
Partitioned Regression and Partial Regression
Partial Regression and Partial Correlation Coefficients
Goodness of Fit and the Analysis of Variance
The Adjusted R-Squared and a Measure of Fit
R-Squared and the Constant Term in the Model
Minimum Mean Squared Error Predictor
Minimum Variance Linear Unbiased Estimation
Finite Sample Properties of Least Squares
Bias Caused by Omission of Relevant Variables
The Variance of the Least Squares Estimator
The Implications of Stochastic Regressors
Large Sample Properties of the Least Squares Estimator
Consistency of the Least Squares Estimator of ß
Asymptotic Normality of the Least Squares Estimator
Consistency of s2 and the Estimator of Asy. Var[b]
Asymptotic Distribution of a Function of b: The Delta Method
Forming a Confidence Interval for a Coefficient
Confidence Intervals Based on Large Samples
Confidence Interval for a Linear Combination of Coefficients: The Oaxaca Decomposition
Predicting y When the Regression Model Describes Log y
Missing Values and Data Imputation
Hypothesis Tests and Model Selection
Size, Power, and Consistency of a Test
A Methodological Dilemma: Bayesian versus Classical Testing
Two Approaches to Testing Hypotheses
Wald Tests Based on the Distance Measure
Testing a Hypothesis about a Coefficient
The F Statistic and the Least Squares Discrepancy
Testing Restrictions Using the Fit of the Regression
The Restricted Least Squares Estimator
The Loss of Fit from Restricted Least Squares
Testing the Significance of the Regression
Solving Out the Restrictions and a Caution about Using R2
Nonnormal Disturbances and Large-Sample Tests
Choosing between Nonnested Models
Comprehensive Approach—The J Test
Model Building—A General to Simple Strategy
Functional Form and Structural Change
Binary Variables in Regression
Threshold Effects and Categorical Variables
Treatment Effects and Difference in Differences Regression
Nonlinearity in the Variables
Modeling and Testing for a Structural Break
Change in a Subset of Coefficients
Tests of Structural Break with Unequal Variances
Predictive Test of Model Stability
Nonlinear, Semiparametric, and Nonparametric Regression Models
Assumptions of the Nonlinear Regression Model
The Nonlinear Least Squares Estimator
Large Sample Properties of the Nonlinear Least Squares Estimator
Hypothesis Testing and Parametric Restrictions
Computing the Nonlinear Least Squares Estimator
Median and Quantile Regression
Least Absolute Deviations Estimation
Endogeneity and Instrumental Variable Estimation
Assumptions of the Extended Model
The Instrumental Variables Estimator
Motivating the Instrumental Variables Estimator
The Hausman and Wu Specification Tests
A Test for Overidentification
Nonlinear Instrumental Variables Estimation
Natural Experiments and the Search for Causal Effects
Part II Generalized Regression Model and Equation Systems
The Generalized Regression Model and Heteroscedasticity
Inefficient Estimation by Least Squares and Instrumental Variables
Finite-Sample Properties of Ordinary Least Squares
Asymptotic Properties of Ordinary Least Squares
Robust Estimation of Asymptotic Covariance Matrices
Efficient Estimation by Generalized Least Squares
Generalized Least Squares (GLS)
Feasible Generalized Least Squares (FGLS)
Heteroscedasticity and Weighted Least Squares
Ordinary Least Squares Estimation
Inefficiency of Ordinary Least Squares
The Estimated Covariance Matrix of b
Estimating the Appropriate Covariance Matrix for Ordinary Least Squares
The Breusch–Pagan/Godfrey LM Test
Weighted Least Squares with Known O
Estimation When O Contains Unknown Parameters
The Seemingly Unrelated Regressions Model
Seemingly Unrelated Regressions with Identical Regressors
Feasible Generalized Least Squares
A Specification Test for the SUR Model
Seemingly Unrelated Generalized Regression Models
Nonlinear Systems of Equations
Systems of Demand Equations: Singular Systems
Flexible Functional Forms: The Translog Cost Function
A General Notation for Linear Simultaneous Equations Models
The Problem of Identification
Single Equation Estimation and Inference
System Methods of Estimation
Testing in the Presence of Weak Instruments
Models for Panel Data
General Modeling Framework for Analyzing Panel Data
Balanced and Unbalanced Panels
The Pooled Regression Model
Least Squares Estimation of the Pooled Model
Robust Covariance Matrix Estimation
Robust Estimation Using Group Means
Estimation with First Differences
The Within- and Between-Groups Estimators
The Fixed Effects Model
Testing the Significance of the Group Effects
Fixed Time and Group Effects
Time-Invariant Variables and Fixed Effects Vector Decomposition
Feasible Generalized Least Squares When S Is Unknown
Testing for Random Effects
Hausman’s Specification Test for the Random Effects Model
Extending the Unobserved Effects Model: Mundlak’s Approach
Extending the Random and Fixed Effects Models: Chamberlain’s Approach
Nonspherical Disturbances and Robust Covariance Estimation
Robust Estimation of the Fixed Effects Model
Heteroscedasticity in the Random Effects Model
Autocorrelation in Panel Data Models
Cluster (and Panel) Robust Covariance Matrices for Fixed and Random Effects Estimators
Hausman and Taylor’s Instrumental Variables Estimator
Consistent Estimation of Dynamic Panel Data Models: Anderson and Hsiao’s IV Estimator
Efficient Estimation of Dynamic Panel Data Models—The Arellano/Bond Estimators
Nonstationary Data and Panel Data Models
Nonlinear Regression with Panel Data
A Robust Covariance Matrix for Nonlinear Least Squares
The Random Coefficients Model
A Hierarchical Linear Model
Parameter Heterogeneity and Dynamic Panel Data Models
Part III Estimation Methodology
Estimation Frameworks in Econometrics
Parametric Estimation and Inference
Modeling Joint Distributions with Copula Functions
GMM Estimation in Econometrics
Maximum Empirical Likelihood Estimation
Least Absolute Deviations Estimation and Quantile Regression
Comparing Parametric and Semiparametric Analyses
Statistical Properties of Estimators
Assumptions for Asymptotic Properties of Extremum Estimators
Asymptotic Properties of Estimators
Minimum Distance Estimation and the Generalized Method of Moments
Consistent Estimation: The Method of Moments
Random Sampling and Estimating the Parameters of Distributions
Asymptotic Properties of the Method of Moments Estimator
Summary—The Method of Moments
The Generalized Method of Moments (GMM) Estimator
Estimation Based on Orthogonality Conditions
Generalizing the Method of Moments
Properties of the GMM Estimator
Testing Hypotheses in the GMM Framework
Testing the Validity of the Moment Restrictions
GMM Counterparts to the WALD, LM, and LR Tests
GMM Estimation of Econometric Models
Seemingly Unrelated Regression Models
Simultaneous Equations Models with Heteroscedasticity
GMM Estimation of Dynamic Panel Data Models
The Likelihood Function and Identification of the Parameters
Efficient Estimation: The Principle of Maximum Likelihood
Properties of Maximum Likelihood Estimators
Properties of Regular Densities
The Information Matrix Equality
Asymptotic Properties of the Maximum Likelihood Estimator
Estimating the Asymptotic Variance of the Maximum Likelihood Estimator
Conditional Likelihoods, Econometric Models, and the GMM Estimator
Hypothesis and Specification Tests and Fit Measures
The Likelihood Ratio Test
The Lagrange Multiplier Test
An Application of the Likelihood-Based Test Procedures
Comparing Models and Computing Model Fit
Vuong’s Test and the Kullback–Leibler Information Criterion
Two-Step Maximum Likelihood Estimation
Pseudo-Maximum Likelihood Estimation and Robust Asymptotic Covariance Matrices
Maximum Likelihood and GMM Estimation
Maximum Likelihood and M Estimation
Applications of Maximum Likelihood Estimation
The Normal Linear Regression Model
The Generalized Regression Model
Seemingly Unrelated Regression Models
Maximum Likelihood Estimation of Nonlinear Regression Models
ML Estimation of the Linear Random Effects Model
Random Effects in Nonlinear Models: MLE Using Quadrature
Fixed Effects in Nonlinear Models: Full MLE
Latent Class and Finite Mixture Models
A Finite Mixture Model
Measured and Unmeasured Heterogeneity
A Conditional Latent Class Model
Determining the Number of Classes
A Panel Data Application
Simulation-Based Estimation and Inference and Random Parameter Models
Sampling from a Standard Uniform Population
Sampling from Continuous Distributions
Sampling from a Multivariate Normal Population
Sampling from Discrete Populations
Simulation-Based Statistical Inference: The Method of Krinsky and Robb
Bootstrapping Standard Errors and Confidence Intervals
A Monte Carlo Study: Behavior of a Test Statistic
A Monte Carlo Study: The Incidental Parameters Problem
Random Effects in a Nonlinear Model
Halton Sequences and Random Draws for Simulation-Based Integration
Computing Multivariate Normal Probabilities Using the GHK Simulator
Simulation-Based Estimation of Random Effects Models
A Random Parameters Linear Regression Model
Nonlinear Random Parameter Models
Mixed Models and Latent Class Models
Bayesian Estimation and Inference
Bayes Theorem and the Posterior Density
Bayesian Analysis of the Classical Regression Model
Analysis with a Noninformative Prior
Estimation with an Informative Prior Density
Posterior Distributions and the Gibbs Sampler
Application: Binomial Probit Model
Panel Data Application: Individual Effects Models
Hierarchical Bayes Estimation of a Random Parameters Model
Part IV Cross Sections, Panel Data, and Microeconometrics
Models for Binary Outcomes
Random Utility Models for Individual Choice
A Latent Regression Model
Functional Form and Regression
Estimation and Inference in Binary Choice Models
Robust Covariance Matrix Estimation
Marginal Effects and Average Partial Effects
Measuring Goodness of Fit
Endogenous Right-Hand-Side Variables in Binary Choice Models
Binary Choice Models for Panel Data
A Conditional Fixed Effects Estimator
Mundlak’s Approach, Variable Addition, and Bias Reduction
Dynamic Binary Choice Models
A Semiparametric Model for Individual Heterogeneity
Nonresponse, Attrition, and Inverse Probability Weighting
Bivariate and Multivariate Probit Models
Testing for Zero Correlation
A Panel Data Model for Bivariate Binary Response
Endogenous Binary Variable in a Recursive Bivariate Probit Model
Endogenous Sampling in a Binary Choice Model
A Multivariate Probit Model
Discrete Choices and Event Counts
Models for Unordered Multiple Choices
Random Utility Basis of the Multinomial Logit Model
The Multinomial Logit Model
The Conditional Logit Model
The Independence from Irrelevant Alternatives Assumption
The Multinomial Probit Model
The Mixed Logit Model
A Generalized Mixed Logit Model
Application: Conditional Logit Model for Travel Mode Choice
Estimating Willingness to Pay
Panel Data and Stated Choice Experiments
Aggregate Market Share Data—The BLP Random Parameters Model
Random Utility Models for Ordered Choices
The Ordered Probit Model
A Specification Test for the Ordered Choice Model
Bivariate Ordered Probit Models
Ordered Probit Models with Fixed Effects
Ordered Probit Models with Random Effects
Extensions of the Ordered Probit Model
Threshold Models—Generalized Ordered Choice Models
Thresholds and Heterogeneity—Anchoring Vignettes
Models for Counts of Events
The Poisson Regression Model
Measuring Goodness of Fit
Heterogeneity and the Negative Binomial Regression Model
Functional Forms for Count Data Models
Truncation and Censoring in Models for Counts
Robust Covariance Matrices for Pooled Estimators
Two-Part Models: Zero Inflation and Hurdle Models
Endogenous Variables and Endogenous Participation
Limited Dependent Variables—Truncation, Censoring, and Sample Selection
Moments of Truncated Distributions
The Truncated Regression Model
The Stochastic Frontier Model
The Censored Normal Distribution
The Censored Regression (Tobit) Model
Two-Part Models and Corner Solutions
Some Issues in Specification
Models for Duration Data
A Regression-Like Approach: Parametric Models of Duration
Models of the Hazard Function
Nonparametric and Semiparametric Approaches
Incidental Truncation and Sample Selection
Incidental Truncation in a Bivariate Distribution
Regression in a Model of Selection
Two-Step and Maximum Likelihood Estimation
Sample Selection in Nonlinear Models
Panel Data Applications of Sample Selection Models
Common Effects in Sample Selection Models
Regression Analysis of Treatment Effects
Estimating the Effect of Treatment on the Treated
Part V Time Series and Macroeconometrics
The Analysis of Time-Series Data
Characteristics of Disturbance Processes
Some Asymptotic Results for Analyzing Time-Series Data
Convergence of Moments—The Ergodic Theorem
Convergence to Normality—A Central Limit Theorem
Asymptotic Properties of Least Squares
Estimating the Variance of the Least Squares Estimator
Box and Pierce’s Test and Ljung’s Refinement
Testing in the Presence of a Lagged Dependent Variable
Summary of Testing Procedures
Efficient Estimation When O Is Known
Estimation When O Is Known
Application: Estimation of a Model with Autocorrelation
Estimation with a Lagged Dependent Variable
ARCH(q), ARCH-in-Mean, and Generalized ARCH Models
Maximum Likelihood Estimation of the Garch Model
Testing for Garch Effects
Nonstationary Processes and Unit Roots
Integrated Processes and Differencing
Random Walks, Trends, and Spurious Regressions
Tests for Unit Roots in Economic Data
The KPSS Test of Stationarity
Error Correction and VAR Representations
Application: German Money Demand
Cointegration Analysis and a Long-Run Theoretical Model
Testing for Model Instability
Appendix A Matrix Algebra
Algebraic Manipulation of Matrices
A Notation for Rows and Columns of a Matrix
Matrix Multiplication and Scalar Multiplication
A Useful Idempotent Matrix
Linear Combinations of Vectors and Basis Vectors
Rank of a Matrix
Determinant of a Matrix
A Least Squares Problem
Solution of a System of Linear Equations
Systems of Linear Equations
Nonhomogeneous Systems of Equations
Solving the Least Squares Problem
Addition and Multiplication of Partitioned Matrices
Determinants of Partitioned Matrices
Inverses of Partitioned Matrices
Characteristic Roots and Vectors
General Results for Characteristic Roots and Vectors
Diagonalization and Spectral Decomposition of a Matrix
Rank of a Matrix
Condition Number of a Matrix
Trace of a Matrix
Determinant of a Matrix
Powers of a Matrix
The Generalized Inverse of a Matrix
Quadratic Forms and Definite Matrices
Calculus and Matrix Algebra
Differentiation and the Taylor Series
Appendix B Probability and Distribution Theory
Expectations of a Random Variable
Some Specific Probability Distributions
The Chi-Squared, t, and F Distributions
Distributions with Large Degrees of Freedom
Size Distributions: The Lognormal Distribution
The Gamma and Exponential Distributions
The Distribution of a Function of a Random Variable
Representations of a Probability Distribution
Expectations in a Joint Distribution
Distribution of a Function of Bivariate Random Variables
Conditioning in a Bivariate Distribution
Regression: The Conditional Mean
Relationships Among Marginal and Conditional Moments
The Analysis of Variance
The Bivariate Normal Distribution
Sets of Linear Functions
The Multivariate Normal Distribution
Marginal and Conditional Normal Distributions
The Classical Normal Linear Regression Model
Linear Functions of a Normal Vector
Quadratic forms in a Standard Normal Vector
A Full Rank Quadratic Form
Independence of a Linear and a Quadratic Form
Appendix C Estimation and Inference
Samples and Random Sampling
Statistics as Estimators—Sampling Distributions
Point Estimation of Parameters
Estimation in a Finite Sample
Tests Based on Confidence Intervals
Appendix D Large-Sample Distribution Theory
Other forms of Convergence and Laws of Large Numbers
Convergence to a Random Variable
Convergence in Distribution: Limiting Distributions
Asymptotic Distribution of a Nonlinear Function
Sequences and the Order of a Sequence
Appendix E Computation and Optimization
The Standard Normal Cumulative Distribution Function
The Gamma and Related Functions
Approximating Integrals by Quadrature
Aspects of Maximum Likelihood Estimation
Function of One Parameter
Function of Two Parameters: The Gamma Distribution
A Concentrated Log-Likelihood Function
Appendix F Data Sets Used in Applications
Combined Author and Subject Index
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