Introduction to Econometrics has been written as a core textbook for a first course in econometrics taken by undergraduate or graduate students. It is intended for students taking a single course in econometrics with a view towards doing practical data work. It will also be highly useful for students interested in understanding the basics of econometric theory with a view towards future study of advanced econometrics. To achieve this end, it has a practical emphasis, showing how a wide variety of models can be used with the types of data sets commonly used by economists. However, it also has enough discussion of the underlying econometric theory to give the student a knowledge of the statistical tools used in advanced econometrics courses.
� A non-technical summary of the basic tools of econometrics is given in chapters 1 and 2, which allows the reader to quickly start empirical work.
� The foundation offered in the first two chapters makes the theoretical econometric material, which begins in chapter 3, more accessible.
� Provides a good balance between econometric theory and empirical applications.
� Discusses a wide range of models used by applied economists including many variants of the regression model (with extensions for panel data), time series models (including a discussion of unit roots and cointegration) and qualitative choice models (probit and logit).
An extensive collection of web-based supplementary materials is provided for this title, including: data sets, problem sheets with worked through answers, empirical projects, sample exercises with answers, and slides for lecturers.
Gary Koop is Professor of Economics at the University of Strathclyde. Gary has published numerous articles econometrics in journals such as the Journal of Econometrics and Journal of Applied Econometrics. Gary has taught econometrics for many years and is the author of following textbooks, all published by John Wiley & Sons Ltd: Analysis of Economic Data 2ed, Analysis of Financial Data and Bayesian Econometrics
Preface. Chapter 1: An Overview of Econometrics. 1.1 The Importance of Econometrics. 1.2 Types of Economic Data. 1.3 Working with Data: Graphical Methods. 1.4 Working with Data: Descriptive Statistics and Correlation. 1.5 Chapter Summary. Exercises. Chapter 2: A Non-technical Introduction to Regression. 2.1 Introduction. 2.2 The Simple Regression Model. 2.3 The Multiple Regression Model. 2.4 Chapter Summary. Exercises. Chapter 3: The Econometrics of the Simple Regression Model. 3.1 Introduction. 3.2 A Review of Basic Concepts in Probability in the Context of the Regression Model. 3.3 The Classical Assumptions for the Regression Model. 3.4 Properties of the Ordinary Least Squares Estimator of . 3.5 Deriving a Confidence Interval for . 3.6 Hypothesis Tests about . 3.7 Modifications to Statistical Procedures when 2 is Unknown. 3.8 Chapter Summary. Exercises. Appendix 1: Proof of the Gauss-Markov theorem. Appendix 2: Using a Asymototic Theory in the Simple Regression Model. Chapter 4: The Econometrics of the Multiple Regression Model. 4.1 Introduction. 4.2 Basic Results for the Multiple Regression Model. 4.3 Issues Relating to the Choice of Explanatory Variables. 4.4 Hypothesis Testing in the Multiple Regression Model. 4.5 Choice of Functional Form in the Multiple Regression Model. 4.6 Chapter Summary. Exercises. Appendix: Wald and Lagrange multiplier tests. Chapter 5: The Multiple Regression Model: Freeing up Classical Assumptions. 5.1 Introduction. 5.2 Basic Theoretical Results. 5.3 Heteroskedasticity. 5.4 The Regression Model with Autocorrelated Errors. 5.5 The Instrumental Variables Estimator. 5.6 Chapter Summary. Exercises. Appendix: Asymptotic Results for the OLS and Instrumental variables Estimators. Chapter 6: Univariate Time Series Analysis. 6.1 Introduction. 6.2 Time Series Notation. 6.3 Trends in Time Series Variables. 6.4 The Autocorrelation Function. 6.5 The Autoregressive Model. 6.6 Defining Stationarity. 6.7 Modelling Volatility. 6.8 Chapter Summary. Exercises. Appendix: MA and ARMA Models. Chapter 7: Regression with Time Series Variables. 7.1 Introduction. 7.2 Time Series Regression when X and Y are Stationary. 7.3 Time Series Regression When Y and X have Unit Roots. 7.4 Time Series Regression when Y and X have Unit Roots but are NOT Cointegrated. 7.5 Granger Causality. 7.6 Vector Autoregressions. 7.7 Chapter Summary. Exercises. Appendix: The Theory of Forecasting. Chapter 8: Models for Panel Data. 8.1 Introduction. 8.2 The Pooled Model. 8.3 Individual Effects Models. 8.4 Chapter Summary. Exercises. Chapter 9: Qualitative Choice and Limited Dependent Variable Models. 9.1 Introduction. 9.2 Qualitative Choice Models. 9.3 Limited Dependent Variable Models. 9.4 Chapter Summary. Exercises. Chapter 10: Bayesian Econometrics. 10.1 An Overview of Bayesian Econometrics. 10.2 The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable. 10.3 Chapter Summary. 10.4 Exercises. Appendix: Bayesian Analysis of the Simple Regression Model with Unknown Variance. Appendix A; Mathematical Basics. Appendix B: Probability Basics. Appendix C: Basic Concepts in Asymptotic Theory. Appendix D: Writing an Empirical Project. Tables. Bibliography. Index.