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* Treats basic econometric methods as well as more advanced topics * New material is presented in a simple and accessible way * Numerous empirical illustrations and exercises at the end of each chapter
This book is intended for a first year graduate course in econometrics. However, the first six chapters have no matrix algebra and can be used in an advanced undergraduate class. This can be supplemented by some of the material in later chapters that do not require matrix algebra, like the first part of Chapter lIon simultaneous equations and Chapter 14 on time-series analysis. This book teaches some of the basic econometric methods and the underlying assumptions behind them. Estimation, hypotheses testing and prediction are three recurrent themes in this book. Some uses of econometric methods include (i) empirical testing of economic theory, whether it is the permanent income consumption theory or purchasing power parity, (ii) forecasting, whether it is GNP or unemployment in the U.S. economy or future sales in the computer industry. (iii) Estimation of price elasticities of demand, or returns to scale in production. More importantly, econometric methods can be used to simulate the effect of policy changes like a tax increase on gasoline consumption, or a ban on advertising on cigarette consumption. It is left to the reader to choose among the available econometric software to use, like TSP, SHAZAM, PcGive, HUMMER, LIMDEP, SAS, STATA, GAUSS and EViews. The empirical illustrations in the book utilize a variety of these software packages. Of course, these packages have different advantages and disadvantages.
Content Level »Graduate
Keywords »cointegration - econometrics - integration - panel data - regression - regression analysis - statistics - time series
Preface.- What is Econometrics?: Introduction; A Brief History; Critiques of Econometrics.- A Review of Some Basic Statistical Concepts: Introduction; Methods of Estimation; Properties of Estimators; Hypothesis Testing; Confidence Intervals. Problems. References. Appendix.- Simple Linear Regression: Introduction; Least Squares Estimation and the Classical Assumptions; Statistical Properties of the Least Squares Estimators; Estimation of o2; Maximum Likelihood Estimation. A Measure of Fit; Prediction; Residual Analysis; Numerical Example; Empirical Example. Problems. References. Appendix.- Multiple Regression Analysis: Introduction; Least Squares Estimation; Residual Interpretation of Multiple Regression Estimates; Overspecification and Underspecification of the Regression Equation; R-Squared versus R-Bar-Squared; Testing Linear Restrictions; Dummy Variables. Problems. References. Appendix.- Violations of the Classical Assumptions: Introduction; The Zero Mean Assumption; Stochastic Explanatory Variables; Normality of the Disturbances; Heteroskedasticity; Autocorrelation. Problems. References.- Distributed Lags and Dynamic Models: Introduction; Infinite Distributed Lags; Estimation and Testing of Dynamic Models with Serial Correlation; Autoregressive Distributed Lag. Problems. References.- The General Linear Model: The Basics: Introduction; Least Squares Estimation; Partitioned Regression and the Frisch-Waugh Lovell Theorem; Maximum Likelihood Estimation; Prediction; Confidence Intervals and Test of Hypotheses; Joint Confidence Intervals and Test of Hypotheses; Restricted MLE and Restricted Least Squares; Likelihood Ratio, Wald and Lagrange Multiplier Tests. Problems. References. Appendix.- Regression Diagnostics and Specification Tests: Influential Observations; Recursive Residuals; Specification Tests; Non-Linear Least Squares and the Gauss-Newton Regression;Testing Linear Versus Log-Linear Functional Form. Problems. References.- Generalized Least Squares: Introduction; Generalized Least Squares; Special Forms of O; Maximum Likelihood Estimation; Test of Hypotheses; Prediction; Unknown O; The W, LR, and LM Statistics Revisited. Problems. References.- Seemingly Unrelated Regressions: Introduction; Feasible GLS Estimation; Testing Diagonality of the Variance-Covariance Matrix; Seemingly Unrelated Regressions with Unequal Observations; Empirical Example. Problems. References.- Simultaneous Equations Model: Introduction; Single Equation Estimation: Two-Stage Least Squares; System Estimation: Three-Stage Least Squares; The Identification Problem Revisited: The Rank Condition of Identification; Test for Over-Identification Restrictions; Hausman`s Specification Test; Empirical Example. Problems. References.- Pooling Time-Series of Cross-Section Data: Introduction; The Error Components Procedure;. Time-Wise Autocorrelated and Cross-Sectionally Heteroskedastic Procedures; A Comparison of the Two Procedures. Problems. References.- Limited Dependent Variables: Introduction; The Linear Probability Model; Functional Form: Logit and Probit; Grouped Data; Individual Data: Probit and Logit; The Binary Response Model Regression; Asymptotic Variances for Predictions and Marginal Effects; Goodness of Fit Measures; Empirical Example; Multinomial Choice Models; The Censored Regression Model; The Truncated Regression Model; Sample Selectivity. Problems. References. Appendix.- Time Series Models: Introduction; Stationarity; The Box and Jenkins Method; Vector Autoregression; Unit Root; Trend Stationary Versus Difference Stationary; Cointegration; Autoregressive Conditional Heteroskedasticity. Problems. References. Index.