Principles of Econometrics, 4 Edition

1 Principles of econometrics , 4th Edition Table of Contents Preface Chapter 1 An Introduction to econometrics Why Study econometrics ? What is econometrics About? Some Examples The Econometric Model How Are Data Generated? Experimental Data Nonexperimental Data Economic Data Types Time Series Data Cross Section Data Panel or Longitudinal Data The Research Process Writing an Empirical Research Paper Writing a Research Proposal A Format for Writing a Research Report Sources of Economic Data Links to Economic Data on the Internet Interpreting Economic Data Obtaining the Data Probability Primer Learning Objectives Keywords Random Variables Probability Distributions Joint.

2 Marginal and Conditional Probabilities Marginal Distributions Conditional Probability Statistical Independence Digression: Summation Notation Properties of Probability Distributions Expected Value of a Random Variable Conditional Expectation Rules for Expected Values Variance of a Random Variable Expected Values of Several Random Variables Covariance Between Two Random Variables The Normal Distribution Exercises Chapter 2 The Simple Linear Regression Model Learning Objectives Keywords An Economic Model An Econometric Model Introducing the Error Term Estimating the Regression Parameters

3 The Least Squares Principle Estimates for the Food Expenditure Function Interpreting the Estimates Elasticities Prediction Computer Output Other Economic Models Assessing the Least Squares Estimators The Estimator b2 The Expected Values of b1 and b2 Repeated Sampling The Variances and Covariance of b1 and b2 The Gauss Markov Theorem The Probability Distributions of the Least Squares Estimators Estimating the Variance of the Error Term Estimating the Variances and Covariances of the Least Squares Estimators Calculations for the Food Expenditure Data Interpreting the Standard Errors Estimating Nonlinear Relationships Quadratic Functions Using a Quadratic Model A Log Linear Function Using a Log Linear Model Choosing a Functional Form Regression with Indicator Variables Exercises Algebraic Exercises Computer Exercises Appendix 2A Derivation of the Least Squares Estimates Appendix 2B Deviation From the Mean Form of b2 Appendix 2C b2 is a

4 Linear Estimator Appendix 2D Derivation of Theoretical Expression for b2 Appendix 2E Deriving the Variance of b2 Appendix 2F Proof of the Gauss Markov Theorem Appendix 2G Monte Carlo Simulation The Regression Function The Random Error Theoretically True Values Creating a Sample of Data Monte Carlo Objectives Monte Carlo Results Chapter 3 Interval Estimation and Hypothesis Testing Learning Objectives Keywords Interval Estimation The t distribution Obtaining Interval Estimates An Illustration The Repeated Sampling Context Hypothesis Tests The Null Hypothesis The Alternative Hypothesis The Test Statistic The Rejection Region A Conclusion Rejection Regions for Specific Alternatives One tail Tests with Alternative Greater Than (>) One tail Tests with Alternative Less Than (<) Two tail Tests with Alternative Not Equal To ( )

5 Examples of Hypothesis Tests Right tail Tests A One tail Test of Significance A One tail Test of an Economic Hypothesis A Left tail Test Two tail Tests A Two tail Test of an Economic Hypothesis Two tail Test of Significance The p value p value for a Right tail Test p value for a Left tail Test p value for a Two tail Test p value for a Two tail Test of Significance Linear Combinations of Parameters Estimating Expected Food Expenditure An Interval Estimate of Expected Food Expenditure Testing a Linear Combination of Parameters Testing Expected Food Expenditure Exercises Problems Computer Exercises Appendix 3A Derivation of the t distribution Appendix 3B Distribution of the t statistic under H1 Appendix 3C Monte Carlo Simulation Repeated Sampling Properties of Interval Estimators Repeated Sampling Properties of Hypothesis Tests Choosing the Number of Monte Carlo Samples Chapter 4 Prediction.

6 Goodness of Fit and Modeling Issues Learning Objectives Keywords Least Squares Prediction Prediction in the Food Expenditure Model Measuring Goodness of Fit Correlation Analysis Correlation Analysis and 2R The Food Expenditure Example Reporting the Results Modeling Issues The Effects of Scaling the Data Choosing a Functional Form A Linear Log Food Expenditure Model Using Diagnostic Residual Plots Heteroskedastic Residual Pattern Detecting Model Specification Errors Are the Regression Errors Normally Distributed?

7 Polynomial Models Quadratic and Cubic Equations Log Linear Models A Growth Model A Wage Equation Prediction in the Log Linear Model A Generalized 2R Measure Prediction Intervals in the Log Linear Model Log Log Models A Log Log Poultry Demand Equation Exercises Problems Computer Exercises Appendix 4A Development of a Prediction Interval Appendix 4B The Sum of Squares Decomposition Appendix 4C The Log Normal Distribution Chapter 5 The Multiple Regression Model Learning Objectives Keywords Introduction The Economic Model The Econometric Model The General Model The Assumptions of the Model Estimating the Parameters of the Multiple Regression Model Least Squares Estimation Procedure Least Squares Estimates Using Hamburger Chain Data Estimation of the Error Variance 2 Sampling Properties of the Least Squares Estimator The Variances and Covariances of the Least Squares Estimators The Distribution of the Least Squares Estimators

8 Interval Estimation Interval Estimation for a Single Coefficient Interval Estimation for a Linear Combination of Coefficients Hypothesis Testing for a Single Coefficient Testing the Significance of a Single Coefficient One Tail Hypothesis Testing for a Single Coefficient Testing For Elastic Demand Testing Advertising Effectiveness Hypothesis Testing for a Linear Combination of Coefficients Polynomial Equations Cost and Product Curves Extending the Model for Burger Barn Sales The Optimal Level of Advertising.

9 Inference for a Nonlinear Combination of Coefficients Interaction Variables Log Linear Models Measuring Goodness of Fit Exercises Problems Computer Exercises Appendix 5A Derivation of Least Squares Estimators Appendix 5B Large Sample Analysis Consistency Asymptotic Normality Monte Carlo Simulation The Delta Method Nonlinear Functions of a Single Parameter The Delta Method Illustrated Monte Carlo Simulation of the Delta Method The Delta Method Extended The Delta Method Illustrated: Continued Monte Carlo Simulation of the Extended Delta Method Chapter 6 Further Inference in the Multiple Regression Model Learning Objectives Keywords Testing Joint Hypotheses Testing the Effect of Advertising.

10 The F Test Testing the Significance of the Model The Relationship Between t and F Tests More General F Tests A One Tail Test Using Computer Software The Use of Nonsample Information Model Specification Omitted Variables Irrelevant Variables Choosing the Model Model Selection Criteria The Adjusted Coefficient of Determination Information Criteria An Example RESET Poor Data, Collinearity, and Insignificance The Consequences of Collinearity An