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.

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• Preface to the Fourth Edition xi Preface to the Third Edition xiiiPreface to the Second Edition xivPreface to the First Edition xvChapter 1 Introduction 1Organization of the Book 3Useful Background 4Appendix 1.1: Mathematical Concepts Used in this Book 4Endnote 7References 7Chapter 2 Basic Data Handling 8Types of Economic Data 8Obtaining Data 13Working with Data: Graphical Methods 15Working with Data: Descriptive Statistics 20Appendix 2.1: Index Numbers 23Appendix 2.2: Advanced Descriptive Statistics 28Appendix 2.3: Expected Values and Variances 30Endnotes 32Chapter 3 Correlation 34Understanding Correlation 34Understanding Why Variables Are Correlated 38Understanding Correlation Through XY-Plots 41Correlation Between Several Variables 45Appendix 3.1: Mathematical Details 46Endnotes 46Chapter 4 Introduction to Simple Regression 48Regression as a Best Fitting Line 48Interpreting OLS Estimates 53Fitted Values and R2: Measuring the Fit of a Regression Model 56Nonlinearity in Regression 60Appendix 4.1: Mathematical Details 64Endnotes 66Chapter 5 Statistical Aspects of Regression 67Which Factors Affect the Accuracy of the Estimate βˆ ? 68Calculating a Confidence Interval for β 72Testing whether β = 0 78Hypothesis Testing Involving R2: The F-Statistic 82Appendix 5.1: Using Statistical Tables to Test Whether β = 0 85Endnotes 87References 88Chapter 6 Multiple Regression 89Regression as a Best Fitting Line 91OLS Estimation of the Multiple Regression Model 91Statistical Aspects of Multiple Regression 91Interpreting OLS Estimates 92Pitfalls of Using Simple Regression in a Multiple Regression Context 95Omitted Variables Bias 97Multicollinearity 99Appendix 6.1: Mathematical Interpretation of Regression Coefficients 105Endnotes 105Chapter 7 Regression with Dummy Variables 107Simple Regression with a Dummy Variable 109Multiple Regression with Dummy Variables 110Multiple Regression with Dummy and Non-dummy Explanatory Variables 113Interacting Dummy and Non-dummy Variables 116Chapter 8 Qualitative Choice Models 119The Economics of Choice 120Choice Probabilities and the Logit and Probit Models 121Appendix 8.1: Choice Probabilities in the Logit Model 128References 130Chapter 9 Regression with Time Lags: Distributed Lag Models 131Lagged Variables 133Notation 135Selection of Lag Order 138Appendix 9.1: Other Distributed Lag Models 141Endnotes 143Chapter 10 Univariate Time Series Analysis 144The Autocorrelation Function 147The Autoregressive Model for Univariate Time Series 151Nonstationary versus Stationary Time Series 154Extensions of the AR(1) Model 156Testing in the AR(p) with Deterministic Trend Model 161Appendix 10.1: Mathematical Intuition for the AR(1) Model 166Endnotes 167References 168Chapter 11 Regression with Time Series Variables 169Time Series Regression when X and Y Are Stationary 170Time Series Regression when Y and X Have Unit Roots: Spurious Regression 174Time Series Regression when Y and X Have Unit Roots: Cointegration 174Estimation and Testing with Cointegrated Variables 177Time Series Regression when Y and X Are Cointegrated: The Error Correction Model 181Time Series Regression when Y and X Have Unit Roots but Are Not Cointegrated 184Endnotes 187Chapter 12 Applications of Time Series Methods in Macroeconomics and Finance 189Financial Volatility 190Autoregressive Conditional Heteroskedasticity (ARCH) 196Granger Causality 200Vector Autoregressions 206Appendix 12.1: Hypothesis Tests Involving More than One Coefficient 221Endnotes 225Reference 226Chapter 13 Limitations and Extensions 227Problems that Occur when the Dependent Variable Has Particular Forms 228 Problems that Occur when the Errors Have Particular Forms 229Problems that Call for the Use of Multiple Equation Models 231Endnotes 236Appendix A Writing an Empirical Project 237Description of a Typical Empirical Project 237General Considerations 239Project Topics 240References 244Appendix B Data Directory 246Author Index 249Subject Index 250

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.

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• Preface to the Fourth Edition xi Preface to the Third Edition xiiiPreface to the Second Edition xivPreface to the First Edition xvChapter 1 Introduction 1Organization of the Book 3Useful Background 4Appendix 1.1: Mathematical Concepts Used in this Book 4Endnote 7References 7Chapter 2 Basic Data Handling 8Types of Economic Data 8Obtaining Data 13Working with Data: Graphical Methods 15Working with Data: Descriptive Statistics 20Appendix 2.1: Index Numbers 23Appendix 2.2: Advanced Descriptive Statistics 28Appendix 2.3: Expected Values and Variances 30Endnotes 32Chapter 3 Correlation 34Understanding Correlation 34Understanding Why Variables Are Correlated 38Understanding Correlation Through XY-Plots 41Correlation Between Several Variables 45Appendix 3.1: Mathematical Details 46Endnotes 46Chapter 4 Introduction to Simple Regression 48Regression as a Best Fitting Line 48Interpreting OLS Estimates 53Fitted Values and R2: Measuring the Fit of a Regression Model 56Nonlinearity in Regression 60Appendix 4.1: Mathematical Details 64Endnotes 66Chapter 5 Statistical Aspects of Regression 67Which Factors Affect the Accuracy of the Estimate βˆ ? 68Calculating a Confidence Interval for β 72Testing whether β = 0 78Hypothesis Testing Involving R2: The F-Statistic 82Appendix 5.1: Using Statistical Tables to Test Whether β = 0 85Endnotes 87References 88Chapter 6 Multiple Regression 89Regression as a Best Fitting Line 91OLS Estimation of the Multiple Regression Model 91Statistical Aspects of Multiple Regression 91Interpreting OLS Estimates 92Pitfalls of Using Simple Regression in a Multiple Regression Context 95Omitted Variables Bias 97Multicollinearity 99Appendix 6.1: Mathematical Interpretation of Regression Coefficients 105Endnotes 105Chapter 7 Regression with Dummy Variables 107Simple Regression with a Dummy Variable 109Multiple Regression with Dummy Variables 110Multiple Regression with Dummy and Non-dummy Explanatory Variables 113Interacting Dummy and Non-dummy Variables 116Chapter 8 Qualitative Choice Models 119The Economics of Choice 120Choice Probabilities and the Logit and Probit Models 121Appendix 8.1: Choice Probabilities in the Logit Model 128References 130Chapter 9 Regression with Time Lags: Distributed Lag Models 131Lagged Variables 133Notation 135Selection of Lag Order 138Appendix 9.1: Other Distributed Lag Models 141Endnotes 143Chapter 10 Univariate Time Series Analysis 144The Autocorrelation Function 147The Autoregressive Model for Univariate Time Series 151Nonstationary versus Stationary Time Series 154Extensions of the AR(1) Model 156Testing in the AR(p) with Deterministic Trend Model 161Appendix 10.1: Mathematical Intuition for the AR(1) Model 166Endnotes 167References 168Chapter 11 Regression with Time Series Variables 169Time Series Regression when X and Y Are Stationary 170Time Series Regression when Y and X Have Unit Roots: Spurious Regression 174Time Series Regression when Y and X Have Unit Roots: Cointegration 174Estimation and Testing with Cointegrated Variables 177Time Series Regression when Y and X Are Cointegrated: The Error Correction Model 181Time Series Regression when Y and X Have Unit Roots but Are Not Cointegrated 184Endnotes 187Chapter 12 Applications of Time Series Methods in Macroeconomics and Finance 189Financial Volatility 190Autoregressive Conditional Heteroskedasticity (ARCH) 196Granger Causality 200Vector Autoregressions 206Appendix 12.1: Hypothesis Tests Involving More than One Coefficient 221Endnotes 225Reference 226Chapter 13 Limitations and Extensions 227Problems that Occur when the Dependent Variable Has Particular Forms 228 Problems that Occur when the Errors Have Particular Forms 229Problems that Call for the Use of Multiple Equation Models 231Endnotes 236Appendix A Writing an Empirical Project 237Description of a Typical Empirical Project 237General Considerations 239Project Topics 240References 244Appendix B Data Directory 246Author Index 249Subject Index 250

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.

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• Preface to the Fourth Edition xi Preface to the Third Edition xiiiPreface to the Second Edition xivPreface to the First Edition xvChapter 1 Introduction 1Organization of the Book 3Useful Background 4Appendix 1.1: Mathematical Concepts Used in this Book 4Endnote 7References 7Chapter 2 Basic Data Handling 8Types of Economic Data 8Obtaining Data 13Working with Data: Graphical Methods 15Working with Data: Descriptive Statistics 20Appendix 2.1: Index Numbers 23Appendix 2.2: Advanced Descriptive Statistics 28Appendix 2.3: Expected Values and Variances 30Endnotes 32Chapter 3 Correlation 34Understanding Correlation 34Understanding Why Variables Are Correlated 38Understanding Correlation Through XY-Plots 41Correlation Between Several Variables 45Appendix 3.1: Mathematical Details 46Endnotes 46Chapter 4 Introduction to Simple Regression 48Regression as a Best Fitting Line 48Interpreting OLS Estimates 53Fitted Values and R2: Measuring the Fit of a Regression Model 56Nonlinearity in Regression 60Appendix 4.1: Mathematical Details 64Endnotes 66Chapter 5 Statistical Aspects of Regression 67Which Factors Affect the Accuracy of the Estimate βˆ ? 68Calculating a Confidence Interval for β 72Testing whether β = 0 78Hypothesis Testing Involving R2: The F-Statistic 82Appendix 5.1: Using Statistical Tables to Test Whether β = 0 85Endnotes 87References 88Chapter 6 Multiple Regression 89Regression as a Best Fitting Line 91OLS Estimation of the Multiple Regression Model 91Statistical Aspects of Multiple Regression 91Interpreting OLS Estimates 92Pitfalls of Using Simple Regression in a Multiple Regression Context 95Omitted Variables Bias 97Multicollinearity 99Appendix 6.1: Mathematical Interpretation of Regression Coefficients 105Endnotes 105Chapter 7 Regression with Dummy Variables 107Simple Regression with a Dummy Variable 109Multiple Regression with Dummy Variables 110Multiple Regression with Dummy and Non-dummy Explanatory Variables 113Interacting Dummy and Non-dummy Variables 116Chapter 8 Qualitative Choice Models 119The Economics of Choice 120Choice Probabilities and the Logit and Probit Models 121Appendix 8.1: Choice Probabilities in the Logit Model 128References 130Chapter 9 Regression with Time Lags: Distributed Lag Models 131Lagged Variables 133Notation 135Selection of Lag Order 138Appendix 9.1: Other Distributed Lag Models 141Endnotes 143Chapter 10 Univariate Time Series Analysis 144The Autocorrelation Function 147The Autoregressive Model for Univariate Time Series 151Nonstationary versus Stationary Time Series 154Extensions of the AR(1) Model 156Testing in the AR(p) with Deterministic Trend Model 161Appendix 10.1: Mathematical Intuition for the AR(1) Model 166Endnotes 167References 168Chapter 11 Regression with Time Series Variables 169Time Series Regression when X and Y Are Stationary 170Time Series Regression when Y and X Have Unit Roots: Spurious Regression 174Time Series Regression when Y and X Have Unit Roots: Cointegration 174Estimation and Testing with Cointegrated Variables 177Time Series Regression when Y and X Are Cointegrated: The Error Correction Model 181Time Series Regression when Y and X Have Unit Roots but Are Not Cointegrated 184Endnotes 187Chapter 12 Applications of Time Series Methods in Macroeconomics and Finance 189Financial Volatility 190Autoregressive Conditional Heteroskedasticity (ARCH) 196Granger Causality 200Vector Autoregressions 206Appendix 12.1: Hypothesis Tests Involving More than One Coefficient 221Endnotes 225Reference 226Chapter 13 Limitations and Extensions 227Problems that Occur when the Dependent Variable Has Particular Forms 228 Problems that Occur when the Errors Have Particular Forms 229Problems that Call for the Use of Multiple Equation Models 231Endnotes 236Appendix A Writing an Empirical Project 237Description of a Typical Empirical Project 237General Considerations 239Project Topics 240References 244Appendix B Data Directory 246Author Index 249Subject Index 250