Principles of Econometrics, 5th Ed.

Description

Principles of Econometrics, Fifth Edition, is an introductory book for undergraduate students in economics and finance, as well as first-year graduate students in a variety of fields that include economics, finance, accounting, marketing, public policy, sociology, law, and political science. Students will gain a working knowledge of basic econometrics so they can apply modeling, estimation, inference, and forecasting techniques when working with real-world economic problems. Readers will also gain an understanding of econometrics that allows them to critically evaluate the results of others’ economic research and modeling, and that will serve as a foundation for further study of the field. Cover......Page 1 Title Page......Page 3 Copyright......Page 4 Preface......Page 7 Contents......Page 14 List of Examples......Page 23 1.1 Why Study Econometrics?......Page 29 1.2 What Is Econometrics About?......Page 30 1.2.1 Some Examples......Page 31 1.3 The Econometric Model......Page 32 1.4 How Are Data Generated?......Page 33 1.4.2 Quasi-Experimental Data......Page 34 1.5.1 Time-Series Data......Page 35 1.5.2 Cross-Section Data......Page 36 1.6 The Research Process......Page 37 1.7.2 A Format for Writing a Research Report......Page 39 1.8.1 Links to Economic Data on the Internet......Page 41 1.8.3 Obtaining the Data......Page 42 Probability Primer......Page 43 P.1 Random Variables......Page 44 P.2 Probability Distributions......Page 45 P.3.1 Marginal Distributions......Page 48 P.3.3 Statistical Independence......Page 49 P.4 A Digression: Summation Notation......Page 50 P.5 Properties of Probability Distributions......Page 51 P.5.1 Expected Value of a Random Variable......Page 52 P.5.3 Rules for Expected Values......Page 53 P.5.4 Variance of a Random Variable......Page 54 P.5.6 Covariance Between Two Random Variables......Page 55 P.6 Conditioning......Page 57 P.6.1 Conditional Expectation......Page 58 P.6.2 Conditional Variance......Page 59 P.6.3 Iterated Expectations......Page 60 P.6.4 Variance Decomposition......Page 61 P.7 The Normal Distribution......Page 62 P.7.1 The Bivariate Normal Distribution......Page 65 P.8 Exercises......Page 67 Chapter 2: The Simple Linear Regression Model......Page 74 2.1 An Economic Model......Page 75 2.2 An Econometric Model......Page 77 2.2.1 Data Generating Process......Page 79 2.2.2 The Random Error and Strict Exogeneity......Page 80 2.2.3 The Regression Function......Page 81 2.2.4 Random Error Variation......Page 82 2.2.7 Generalizing the Exogeneity Assumption......Page 84 2.2.8 Error Correlation......Page 85 2.2.9 Summarizing the Assumptions......Page 86 2.3 Estimating the Regression Parameters......Page 87 2.3.1 The Least Squares Principle......Page 89 2.3.2 Other Economic Models......Page 93 2.4 Assessing the Least Squares Estimators......Page 94 2.4.1 The Estimator b2......Page 95 2.4.2 The Expected Values of b1 and b2......Page 96 2.4.4 The Variances and Covariance of b1 and b2......Page 97 2.5 The Gauss-Markov Theorem......Page 100 2.6 The Probability Distributions of the Least Squares Estimators......Page 101 2.7.1 Estimating the Variances and Covariance of the Least Squares Estimators......Page 102 2.7.2 Interpreting the Standard Errors......Page 104 2.8.2 Using a Quadratic Model......Page 105 2.8.3 A Log-Linear Function......Page 107 2.8.4 Using a Log-Linear Model......Page 108 2.9 Regression with Indicator Variables......Page 110 2.10.1 Random and Independent x......Page 112 2.10.2 Random and Strictly Exogenous x......Page 114 2.10.3 Random Sampling......Page 115 2.11.1 Problems......Page 117 2.11.2 Computer Exercises......Page 121 Appendix 2A Derivation of the Least Squares Estimates......Page 126 Appendix 2B Deviation from the Mean Form of b2......Page 127 Appendix 2E Deriving the Conditional Variance of b2......Page 128 Appendix 2F Proof of the Gauss-Markov Theorem......Page 130 2G.2 The Random and Independent x Case......Page 131 2G.3 The Random and Strictly Exogenous x Case......Page 133 2H.1 The Regression Function......Page 134 2H.3 Theoretically True Values......Page 135 2H.4 Creating a Sample of Data......Page 136 2H.6 Monte Carlo Results......Page 137 2H.7 Random-x Monte Carlo Results......Page 138 Chapter 3: Interval Estimation and Hypothesis Testing......Page 140 3.1.1 The t-Distribution......Page 141 3.1.2 Obtaining Interval Estimates......Page 143 3.1.3 The Sampling Context......Page 144 3.2.2 The Alternative Hypothesis......Page 146 3.2.4 The Rejection Region......Page 147 3.3.1 One-Tail Tests with Alternative "Greater Than" (>)......Page 148 3.3.2 One-Tail Tests with Alternative "Less Than" ( 2......Page 671 15.2.4 The Least Squares Dummy Variable Model......Page 672 15.3 Panel Data Regression Error Assumptions......Page 674 15.3.1 OLS Estimation with Cluster-Robust Standard Errors......Page 676 15.3.2 Fixed Effects Estimation with Cluster-Robust Standard Errors......Page 678 15.4 The Random Effects Estimator......Page 679 15.4.1 Testing for Random Effects......Page 681 15.4.2 A Hausman Test for Endogeneity in the Random Effects Model......Page 682 15.4.3 A Regression-Based Hausman Test......Page 684 15.4.4 The Hausman-Taylor Estimator......Page 686 15.4.5 Summarizing Panel Data Assumptions......Page 688 15.4.6 Summarizing and Extending Panel Data Model Estimation......Page 689 15.5.1 Problems......Page 691 15.5.2 Computer Exercises......Page 698 Appendix 15A Cluster-Robust Standard Errors: Some Details......Page 705 Appendix 15B Estimation of Error Components......Page 707 Chapter 16: Qualitative and Limited Dependent Variable Models......Page 709 16.1 Introducing Models with Binary Dependent Variables......Page 710 16.1.1 The Linear Probability Model......Page 711 16.2 Modeling Binary Choices......Page 713 16.2.1 The Probit Model for Binary Choice......Page 714 16.2.2 Interpreting the Probit Model......Page 715 16.2.3 Maximum Likelihood Estimation of the Probit Model......Page 718 16.2.4 The Logit Model for Binary Choices......Page 721 16.2.5 Wald Hypothesis Tests......Page 723 16.2.6 Likelihood Ratio Hypothesis Tests......Page 724 16.2.8 Binary Choice Models with a Continuous Endogenous Variable......Page 726 16.2.9 Binary Choice Models with a Binary Endogenous Variable......Page 727 16.2.10 Binary Endogenous Explanatory Variables......Page 728 16.2.11 Binary Choice Models and Panel Data......Page 729 16.3 Multinomial Logit......Page 730 16.3.2 Maximum Likelihood Estimation......Page 731 16.3.3 Multinomial Logit Postestimation Analysis......Page 732 16.4.1 Conditional Logit Choice Probabilities......Page 735 16.4.2 Conditional Logit Postestimation Analysis......Page 736 16.5 Ordered Choice Models......Page 737 16.5.1 Ordinal Probit Choice Probabilities......Page 738 16.5.2 Ordered Probit Estimation and Interpretation......Page 739 16.6.1 Maximum Likelihood Estimation of the Poisson Regression Model......Page 741 16.6.2 Interpreting the Poisson Regression Model......Page 742 16.7.1 Maximum Likelihood Estimation of the Simple Linear Regression Model......Page 745 16.7.3 Censored Samples and Regression......Page 746 16.7.4 Tobit Model Interpretation......Page 748 16.7.5 Sample Selection......Page 751 16.8.1 Problems......Page 753 16.8.2 Computer Exercises......Page 761 16A.1 Standard Error of Marginal Effect at a Given Point......Page 767 16A.2 Standard Error of Average Marginal Effect......Page 768 16B.1 Binary Choice Model......Page 769 16B.2 Probit or Logit?......Page 770 16C.1 Tobit (Tobit Type I)......Page 771 16C.2 Heckit (Tobit Type II)......Page 772 Appendix 16D A Tobit Monte Carlo Experiment......Page 773 Appendix A: Mathematical Tools......Page 776 A.1.3 Scientific Notation......Page 777 A.1.4 Logarithms and the Number e......Page 778 A.1.6 Logarithms and Percentages......Page 779 A.2 Linear Relationships......Page 780 A.3 Nonlinear Relationships......Page 781 A.3.1 Rules for Derivatives......Page 782 A.3.3 Second Derivatives......Page 785 A.3.4 Maxima and Minima......Page 786 A.3.5 Partial Derivatives......Page 787 A.3.6 Maxima and Minima of Bivariate Functions......Page 788 A.4.1 Computing the Area Under a Curve......Page 790 A.5 Exercises......Page 792 Appendix B: Probability Concepts......Page 796 B.1.1 Expected Value of a Discrete Random Variable......Page 797 B.1.2 Variance of a Discrete Random Variable......Page 798 B.1.3 Joint, Marginal, and Conditional Distributions......Page 799 B.1.4 Expectations Involving Several Random Variables......Page 800 B.1.5 Covariance and Correlation......Page 801 B.1.8 Variance Decomposition......Page 802 B.1.9 Covariance Decomposition......Page 805 B.2 Working with Continuous Random Variables......Page 806 B.2.1 Probability Calculations......Page 807 B.2.2 Properties of Continuous Random Variables......Page 808 B.2.3 Joint, Marginal, and Conditional Probability Distributions......Page 809 B.2.4 Using Iterated Expectations with Continuous Random Variables......Page 813 B.2.5 Distributions of Functions of Random Variables......Page 815 B.3 Some Important Probability Distributions......Page 817 B.3.2 The Binomial Distribution......Page 818 B.3.3 The Poisson Distribution......Page 819 B.3.4 The Uniform Distribution......Page 820 B.3.5 The Normal Distribution......Page 821 B.3.6 The Chi-Square Distribution......Page 822 B.3.7 The t-Distribution......Page 824 B.3.8 The F-Distribution......Page 825 B.3.9 The Log-Normal Distribution......Page 827 B.4 Random Numbers......Page 828 B.4.1 Uniform Random Numbers......Page 833 B.5 Exercises......Page 834 Appendix C: Review of Statistical Inference......Page 840 C.1 A Sample of Data......Page 841 C.2 An Econometric Model......Page 842 C.3 Estimating the Mean of a Population......Page 843 C.3.1 The Expected Value of Y......Page 844 C.3.3 The Sampling Distribution of Y......Page 845 C.3.4 The Central Limit Theorem......Page 846 C.4 Estimating the Population Variance and Other Moments......Page 848 C.4.2 Estimating Higher Moments......Page 849 C.5.1 Interval Estimation: σ2 Known......Page 850 C.5.2 Interval Estimation: σ2 Unknown......Page 853 C.6.1 Components of Hypothesis Tests......Page 854 C.6.2 One-Tail Tests with Alternative "Greater Than" (>)......Page 856 C.6.4 Two-Tail Tests with Alternative "Not Equal To" (≠)......Page 857 C.6.5 The p-Value......Page 859 C.6.6 A Comment on Stating Null and Alternative Hypotheses......Page 860 C.6.8 A Relationship Between Hypothesis Testing and Confidence Intervals......Page 861 C.7.2 Testing the Equality of Two Population Means......Page 862 C.7.3 Testing the Ratio of Two Population Variances......Page 863 C.7.4 Testing the Normality of a Population......Page 864 C.8 Introduction to Maximum Likelihood Estimation......Page 865 C.8.1 Inference with Maximum Likelihood Estimators......Page 868 C.8.2 The Variance of the Maximum Likelihood Estimator......Page 869 C.8.3 The Distribution of the Sample Proportion......Page 870 C.8.4 Asymptotic Test Procedures......Page 871 C.9.1 Derivation of Least Squares Estimator......Page 876 C.9.2 Best Linear Unbiased Estimation......Page 877 C.10 Kernel Density Estimator......Page 879 C.11.1 Problems......Page 882 C.11.2 Computer Exercises......Page 885 TableD.1 Cumulative Probabilities for the Standard Normal Distribution ��(z) = P(Z ≤ z)......Page 890 TableD.2 Percentiles of the t-distribution......Page 891 TableD.3 Percentiles of the Chi-square Distribution......Page 892 TableD.4 95th Percentile for the F-distribution......Page 893 TableD.5 99th Percentile for the F-distribution......Page 894 TableD.6 Standard Normal pdf Values ��(z)......Page 895 Index......Page 897 EULA......Page 907