Introduction to Econometric Modelling provides an introduction to econometrics for undergraduate students. In this book, Gary Koop provides a broader set of models than is offered in existing textbooks and places greater focus on models (e.g. the regression model) than the methods that are used to analyze the models.

Preface ix Chapter 1 An Overview of Econometrics 1 1.1 The importance of econometrics 1 1.2 Types of economic data 2 1.3 Working with data: graphical methods 6 1.4 Working with data: descriptive statistics and correlation 11 1.5 Chapter summary 26 Exercises 26 Chapter 2 A Non-technical Introduction to Regression 29 2.1 Introduction 29 2.2 The simple regression model 30 2.3 The multiple regression model 42 2.4 Chapter summary 55 Exercises 57 Chapter 3 The Econometrics of the Simple Regression Model 59 3.1 Introduction 59 3.2 A review of basic concepts in probability in the context of the regression model 60 3.3 The classical assumptions for the regression model 64 3.4 Properties of the ordinary least-squares estimator of β 67 3.5 Deriving a confidence interval for β 75 3.6 Hypothesis tests about β 77 3.7 Modifications to statistical procedures when σ2 is unknown 78 3.8 Chapter summary 81 Exercises 82 Appendix 1: Proof of the Gauss–Markov theorem 84 Appendix 2: Using asymptotic theory in the simple regression model 85 Chapter 4 The Econometrics of the Multiple Regression Model 91 4.1 Introduction 91 4.2 Basic results for the multiple regression model 92 4.3 Issues relating to the choice of explanatory variables 96 4.4 Hypothesis testing in the multiple regression model 102 4.5 Choice of functional form in the multiple regression model 109 4.6 Chapter summary 115 Exercises 116 Appendix: Wald and Lagrange multiplier tests 117 Chapter 5 The Multiple Regression Model: Freeing Up the Classical Assumptions 121 5.1 Introduction 121 5.2 Basic theoretical results 122 5.3 Heteroskedasticity 124 5.4 The regression model with autocorrelated errors 138 5.5 The instrumental variables estimator 149 5.6 Chapter summary 164 Exercises 165 Appendix: Asymptotic results for the OLS and instrumental variables estimators 168 Chapter 6 Univariate Time Series Analysis 173 6.1 Introduction 173 6.2 Time series notation 175 6.3 Trends in time series variables 177 6.4 The autocorrelation function 179 6.5 The autoregressive model 181 6.6 Defining stationarity 195 6.7 Modeling volatility 197 6.8 Chapter summary 205 Exercises 207 Appendix: MA and ARMA models 210 Chapter 7 Regression with Time Series Variables 213 7.1 Introduction 213 7.2 Time series regression when X and Yare stationary 214 7.3 Time series regression when Y and X have unit roots 217 7.4 Time series regression when Y and X have unit roots but are NOTcointegrated 227 7.5 Granger causality 227 7.6 Vector autoregressions 233 7.7 Chapter summary 247 Exercises 248 Appendix: The theory of forecasting 251 Chapter 8 Models for Panel Data 255 8.1 Introduction 255 8.2 The pooled model 256 8.3 Individual effects models 256 8.4 Chapter summary 271 Exercises 272 Chapter 9 Qualitative Choice and Limited Dependent Variable Models 277 9.1 Introduction 277 9.2 Qualitative choice models 278 9.3 Limited dependent variable models 296 9.4 Chapter summary 304 Exercises 306 Chapter 10 Bayesian Econometrics 309 10.1 An overview of Bayesian econometrics 309 10.2 The normal linear regression model with natural conjugate prior and a single explanatory variable 315 10.3 Chapter summary 326 Exercises 326 Appendix: Bayesian analysis of the simple regression model with unknown variance 328 Appendix A: Mathematical Basics 333 Appendix B: Probability Basics 338 Appendix C: Basic Concepts in Asymptotic Theory 348 Appendix D: Writing an Empirical Project 353 Tables 359 Table 1. Area under the standard normal distribution Pr(0 ≤ Z ≤ z) 359 Table 2. Area under the Student t distribution for different degrees of freedom (DF), Pr(Z ≥ z) = α 360 Table 3. Percentiles of the chi-square distribution 361 Table 4a. Area under the F-distribution for different degrees of freedom, ν1 and ν2, Pr(Z ≥ z) = 0.05 362 Table 4b. Area under the F-distribution for different degrees of freedom, ν1 and ν2, Pr(Z ≥ z) = 0.01 363 Bibliography 364 Index 365

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. Key Features: 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. URL: www.wileyeurope.com/college/koop

“An introductory text offering econometric methodology for quantifying and managing this variety of risk, illustrated by empirical examples.” (Times Higher Education Supplement, Thursday 28th February)

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 1.6 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 2.5 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 µ2 is Unknown 3.8 Chapter Summary 3.9 Exercises Appendices 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 4.7 Exercises Appendix 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 5.7 Exercises Appendix 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 6.9 Exercises Appendix 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 7.8 Exercises Appendix Chapter 8 Models for Panel Data 8.1 Introduction 8.2 The Pooled Model 8.3 Individual Effects Models 8.4 Chapter Summary 8.5 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 9.5 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