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Contents

Preface to the Fourth Edition xi 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 ߈ ? 68 Calculating a Confidence Interval for ß 72 Testing whether ß = 0 78 Hypothesis Testing Involving R2: The F-Statistic 82 Appendix 5.1: Using Statistical Tables to Test Whether ß = 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

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