Description

Book Synopsis
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.

Table of 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 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

Analysis of Economic Data

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    A Paperback / softback by Gary Koop

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      Publisher: John Wiley & Sons Inc
      Publication Date: Publication Date: 22/02/2013
      ISBN13: 9781118472538, 978-1118472538
      ISBN10: 1118472535
      Also in:
      Economics

      Description

      Book Synopsis
      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.

      Table of 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 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

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