Analysis of Economic Data

Analysis of Economic Data has, over three editions, become firmly established as a successful textbook for students studying data analysis whose primary interest is not in econometrics, statistics or mathematics. 

It introduces students to basic econometric techniques and shows the reader how to apply these techniques in the context of real-world empirical problems. The book adopts a largely non-mathematical approach relying on verbal and graphical inuition and covers most of the tools used in modern econometrics research.  It contains extensive use of real data examples and involves readers in hands-on computer work.

Gary Koop is Professor of Economics at the University of Strathclyde. He previously held professorial positions at the Universities of Toronto, Edinburgh, Glasgow and Leicester. He has also held academic posts at the University of Cambridge, the London School of Economics, Boston University and Queen's University, Canada. Gary is the associate editor of the Journal of Econometrics, Econometrics Reviews, the Journal of Empirical Finance, Studies in Nonlinear Dynamics and Econometrics and the Journal of Applied Econometrics. He is the author of: Introduction to Econometrics, Bayesian Econometrics and Analysis of Financial Data, all of which are published by Wiley.

Preface to the Third Edition xiii

Preface to the Second Edition xiv

Preface to the First Edition xv

Chapter 1 Introduction 1

Organization of the Book 3

Useful Background 4

Appendix 1.1: Mathematical Concepts Used in this Book 4

Endnote 7

References 7

Chapter 2 Basic Data Handling 8

Types of Economic Data 8

Obtaining Data 13

Working with Data: Graphical Methods 15

Working with Data: Descriptive Statistics 20

Appendix 2.1: Index Numbers 23

Appendix 2.2: Advanced Descriptive Statistics 28

Appendix 2.3: Expected Values and Variances 30

Endnotes 32

Chapter 3 Correlation 34

Understanding Correlation 34

Understanding Why Variables Are Correlated 38

Understanding Correlation Through XY-Plots 41

Correlation Between Several Variables 45

Appendix 3.1: Mathematical Details 46

Endnotes 46

Chapter 4 Introduction to Simple Regression 48

Regression as a Best Fitting Line 48

Interpreting OLS Estimates 53

Fitted Values and R2: Measuring the Fit of a Regression Model 56

Nonlinearity in Regression 60

Appendix 4.1: Mathematical Details 64

Endnotes 66

Chapter 5 Statistical Aspects of Regression 67

Which Factors Affect the Accuracy of the Estimate βˆ ? 68

Calculating a Confidence Interval for β 72

Testing whether β = 0 78

Hypothesis Testing Involving R2: The F-Statistic 82

Appendix 5.1: Using Statistical Tables to Test Whether β = 0 85

Endnotes 87

References 88

Chapter 6 Multiple Regression 89

Regression as a Best Fitting Line 91

OLS Estimation of the Multiple Regression Model 91

Statistical Aspects of Multiple Regression 91

Interpreting OLS Estimates 92

Pitfalls of Using Simple Regression in a Multiple Regression Context 95

Omitted Variables Bias 97

Multicollinearity 99

Appendix 6.1: Mathematical Interpretation of Regression Coefficients 105

Endnotes 105

Chapter 7 Regression with Dummy Variables 107

Simple Regression with a Dummy Variable 109

Multiple Regression with Dummy Variables 110

Multiple Regression with Dummy and Non-dummy Explanatory Variables 113

Interacting Dummy and Non-dummy Variables 116

Chapter 8 Qualitative Choice Models 119

The Economics of Choice 120

Choice Probabilities and the Logit and Probit Models 121

Appendix 8.1: Choice Probabilities in the Logit Model 128

References 130

Chapter 9 Regression with Time Lags: Distributed Lag Models 131

Lagged Variables 133

Notation 135

Selection of Lag Order 138

Appendix 9.1: Other Distributed Lag Models 141

Endnotes 143

Chapter 10 Univariate Time Series Analysis 144

The Autocorrelation Function 147

The Autoregressive Model for Univariate Time Series 151

Nonstationary versus Stationary Time Series 154

Extensions of the AR(1) Model 156

Testing in the AR(p) with Deterministic Trend Model 161

Appendix 10.1: Mathematical Intuition for the AR(1) Model 166

Endnotes 167

References 168

Chapter 11 Regression with Time Series Variables 169

Time Series Regression when X and Y Are Stationary 170

Time Series Regression when Y and X Have Unit Roots: Spurious Regression 174

Time Series Regression when Y and X Have Unit Roots: Cointegration 174

Estimation and Testing with Cointegrated Variables 177

Time Series Regression when Y and X Are Cointegrated: The Error Correction Model 181

Time Series Regression when Y and X Have Unit Roots but Are Not Cointegrated 184

Endnotes 187

Chapter 12 Applications of Time Series Methods in Macroeconomics and Finance 189

Financial Volatility 190

Autoregressive Conditional Heteroskedasticity (ARCH) 196

Granger Causality 200

Vector Autoregressions 206

Appendix 12.1: Hypothesis Tests Involving More than One Coefficient 221

Endnotes 225

Reference 226

Chapter 13 Limitations and Extensions 227

Problems that Occur when the Dependent Variable Has Particular Forms 228

Problems that Occur when the Errors Have Particular Forms 229

Problems that Call for the Use of Multiple Equation Models 231

Endnotes 236

Appendix A Writing an Empirical Project 237

Description of a Typical Empirical Project 237

General Considerations 239

Project Topics 240

References 244

Appendix B Data Directory 246

Author Index 249

Subject Index 250

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.