Introduction to Applied Econometrics (with CD-ROM) 1e
ISBN-13: 9780534369163 / ISBN-10: 0534369162
INTRODUCTION TO APPLIED ECONOMETRICS puts the "econ" back in econometrics by integrating classic empirical examples and applications into an introductory development of econometrics. This book rethinks the pedagogy of econometrics so as to build toward an introduction to recent developments in time series analysis, as opposed to the traditional approach that culminates in a treatment of simultaneous equations. This permits a focus on a more limited set of theoretical principles and analytical tools than is true of most books. The text is appropriate for both undergraduate and graduate students at four-year colleges and universities. While it does not employ matrix algebra as purely graduate level texts do, it seeks to present a well-developed and better-motivated approach through the use of applications and case studies. Its coverage goes beyond the ordinary by including four chapters on time series techniques.
1. ECONOMIC DATA AND ECONOMIC MODELS.
Economic Data. Economic Models. Descriptive Statistics Versus Statistical Inference.
2. STATISTICAL INFERENCE.
Populations, Samples and Parameters. Statistics and Sampling Distributions. Properties of Estimators. Derivation of Estimators. Hypothesis Testing. Further Topics in Hypothesis Testing. Inference is Conditional on the Model. Econometrics and Statistics. Statistical Methodology and the Philosophy of Science.
3. RELATIONSHIPS BETWEEN VARIABLES.
Covariance and Correlation. Regression. Deviation Form Notation. Conclusions.
4. SIMPLE REGRESSION.
Model Specification. Least Squares Estimation. Sampling Properties of the Least Squares Estimators. The Sampling Distributions of ˆa and ˆB. Hypothesis Testing. Decomposition of Sample Variation. Presentation of Regression Results. Scaling and Units of Measure. Sampling, Numerical, and Invariance Properties. Application: Output and Production Costs.
5. SUPPLEMENTARY TOPICS IN REGRESSION.
Forecasting. Regression Through the Origin. When Regression Goes Wrong.
6. MATTERS OF FUNCTIONAL FORM.
Loglinear Models. Log-Lin Models. Lin-Log Models. Reciprocal Models. Application: Engel Curves. Conclusions.
7. APPLICATIONS TO PRODUCTION FUNCTIONS.
General Features of Production Functions. The Cobb-Douglas Production Function. Technical Change. Testing Marginal Productivity Conditions. Conclusions.
Model Specification. Least Squares Estimation. Properties of Least Squares Estimators. Hypothesis Testing. Decomposition of a Sample Variation. Application: Electricity Demand. Multicollinearity. Application: the Quadratic Cost Function. Model Misspecification. Pre-Test Estimation.
9.APPLICATION TO ECONOMIC GROWTH.
Introduction. The Textbook Solow-Swan Model. Human Capital in the Solow-Swan Model. Summary: Mankiw, Romer, and Weil in a Nutshell. Conclusions.
10. DUMMY VARIABLES AND RESTRICTED COEFFICIENTS.
Dummy Variables. Restricted Coefficients. Identification.
11. APPLICATIONS TO COST FUNCTIONS.
The Cost Function. Deriving the Cost Function. Using the Cost Function. Returns to Scale in Electricity Generation. The Translog Cost Function. Consumer Demand. Further Reading.
12. MODEL DISCOVERY.
Data Mining. Specification Testing. Non-nested Testing. Model Choice. Should the Equation Be Part of a System? Conclusions.
13. NONLINEAR REGRESSION.
Introduction. Nonlinear Least Squares. Computer Numerics. Reparameterization. Identification. Sampling Properties of NLS Estimators. Estimating Sigma 2. Hypothesis Testing. Conclusions.
Consequences for Ordinary Least Squares. Heteroskedasticity-Robust Tests. Weighted Least Squares. Testing for Heteroskedasticity.
15. TIME SERIES: SOME BASIC CONCEPTS.
Introduction. White Noise. Measuring Temporal Dependence. Stationarity and Nonstationarity. Trend Stationary Processes. A Random Walk. A Random Walk with Drift. Key Properties of Random Walks. Conclusions.
Introduction. Moving Average Processes. Autoregressive Processes. The Stationarity Condition. Key Properties of Moving Average and Autoregressive Processes. Autoregressive-Moving Average Processes.
The Constant Growth Model Revisited. Trend and Difference Stationary Processes. Testing for Stochastic Trends. Higher Orders of Integration.
Long Run Relationships Between Variables. Relationships Between Variables. The Arithmetic of Integrated Processes. Cointegration. The Engle-Granger Test for Cointegration. Testing Restrictions on the Cointegrating Vector. Error Correction Models. The ECM of VAR. Cointegrating Rank. Conclusions and Further Reading.
APPENDIX A: LAWS OF SUMMATION AND DEVIATION FORM.
Laws of Summation. Laws of Deviation Form.
APPENDIX B: DISTRIBUTION THEORY.
Random Variables and Probability Distribution. Mathematical Expectation. Expected Value of a Function. Variance. Variance of a Function. Standardized Random Variables. Bivariate Distributions. Conditional Distributions and Expectation. Statistical Independence. Functions of Two Random Variables. Variance of a Linear Combination. Laws of Expectation and Variance: A Summary.
ON CD-ROM C: PORTFOLIO THEORY AND THE CAPM (OPTIONAL).
Introduction. Risky Assets. Portfolios. The Markowitz Frontier. The Tobin Frontier. The Diversification Effect. Portfolio Optimization. Further Reading.
Ken Stewart holds an Honors B.A. in Economics and Mathematics from Dalhousie University, an M.Sc. in Economics from the London School of Economics and Political Science, and an M.A. in Economics, an M.A. in Statistics, and a Ph.D. in Economics from the University of Michigan. This book is the product of many years of teaching statistics and econometrics at the undergraduate and graduate levels. Professor Stewart’s research is in theoretical and applied econometrics. His theoretical work deals with the properties of test statistics, while his applied work is chiefly in the areas of demand analysis and empirical industrial organization. He is currently an Associate Professor of Economics at the University of Victoria in Victoria, British Columbia, where he lives with his wife Rose and their children Benjamin, Alexander, Eleanor, and Andrew.