Analysis of Economic Data, 3rd Edition

by Gary Koop (University of Strathclyde)

ISBN13: 9780470713891

ISBN10: 0470713895

Format: Paperback

Pub. Date: 2009-04-01

Publisher(s): Wiley

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Summary

Econometrics is concerned with the tasks of developing and applying quantitative or statistical methods to the study and elucidation of economic principles.Analysis of Economic Data teaches methods of data analysis to readers whose primary interest is not in econometrics, statistics or mathematics. It shows how to apply econometric techniques in the context of real-world empirical problems, and adopts a largely non-mathematical approach relying on verbal and graphical intuition. The book covers most of the tools used in modern econometrics research e.g. correlation, regression and extensions for time-series methods and contains extensive use of real data examples and involves readers in hands-on computer work.

Author Biography

Gary Koop is Professor of Economics at the University of Strathclyde, UK. He has published numerous articles in econometrics journals such as the Journal of Business and Economic Statistics and the Journal of Applied Econometrics. Gary is the associate editor of several journals including the Journal of Econometrics and the Journal of Empirical Finance. 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	p. xi
Preface to the Second Edition	p. xii
Preface to the First Edition	p. xiii
Introduction	p. 1
Organization of the Book	p. 3
Useful Background	p. 4
Mathematical Concepts Used in this Book	p. 4
Endnote	p. 7
References	p. 7
Basic Data Handling	p. 8
Types of Economic Data	p. 8
Obtaining Data	p. 13
Working with Data: Graphical Methods	p. 15
Working with Data: Descriptive Statistics	p. 20
Index Numbers	p. 23
Advanced Descriptive Statistics	p. 29
Expected Values and Variances	p. 30
Endnotes	p. 33
Correlation	p. 34
Understanding Correlation	p. 34
Understanding Why Variables Are Correlated	p. 39
Understanding Correlation through XY-plots	p. 41
Correlation between Several Variables	p. 45
Mathematical Details	p. 46
Endnotes	p. 46
An Introduction to Simple Regression	p. 48
Regression as a Best Fitting Line	p. 49
Interpreting OLS Estimates	p. 53
Fitted Values and R²: Measuring the Fit of a Regression Model	p. 56
Nonlinearity in Regression	p. 60
Mathematical Details	p. 65
Endnotes	p. 66
Statistical Aspects of Regression	p. 68
Which Factors Affect the Accuracy of the Estimate <$$$>?	p. 69
Calculating a Confidence Interval for ß	p. 73
Testing whether ß=0	p. 79
Hypothesis Testing Involving R²: The F Statistic	p. 84
Using Statistical Tables for Testing whether ß=0	p. 87
Endnotes	p. 88
References	p. 89
Multiple Regression	p. 90
Regression as a Best Fitting Line	p. 92
Ordinary Least Squares Estimation of the Multiple Regression Model	p. 92
Statistical Aspects of Multiple Regression	p. 92
Interpreting OLS Estimates	p. 93
Pitfalls of Using Simple Regression in a Multiple Regression Context	p. 96
Omitted Variables Bias	p. 98
Multicollinearity	p. 100
Mathematical Interpretation of Regression Coefficients	p. 106
Endnotes	p. 107
Regression with Dummy Variables	p. 109
Simple Regression with a Dummy Variable	p. 111
Multiple Regression with Dummy Variables	p. 112
Multiple Regression with Dummy and Nondummy Explanatory Variables	p. 115
Interacting Dummy and Nondummy Variables	p. 118
What if the Dependent Variable is a Dummy?	p. 119
Endnotes	p. 121
Regression with Time Lags: Distributed Lag Models	p. 122
Aside on Lagged Variables	p. 124
Aside on Notation	p. 126
Selection of Lag Order	p. 130
Other Distributed Lag Models	p. 133
Endnotes	p. 134
Univariate Time Series Analysis	p. 136
The Autocorrelation Function	p. 139
The Autoregressive Model for Univariate Time Series	p. 143
Nonstationary versus Stationary Time Series	p. 146
Extensions of the AR(1) Model	p. 148
Testing in the AR(p) with Deterministic Trend Model	p. 153
Mathematical Intuition for the AR(1) Model	p. 158
Endnotes	p. 159
References	p. 160
Regression with Time Series Variables	p. 161
Time Series Regression when X and Y Are Stationary	p. 162
Time Series Regression when Y and X Have Unit Roots: Spurious Regression	p. 166
Time Series Regression when Y and X Have Unit Roots: Cointegration	p. 166
Time Series Regression when Y and X Are Cointegrated: The Error Correction Model	p. 173
Time Series Regression when Y and X Have Unit Roots but Are NOT Cointegrated	p. 177
Endnotes	p. 179
Applications of Time Series Methods in Macroeconomics and Finance	p. 181
Volatility in Asset Prices	p. 182
Autoregressive Conditional Heteroskedasticity (ARCH)	p. 188
Granger Causality	p. 193
Vector Autoregressions	p. 199
Hypothesis Tests Involving More than One Coefficient	p. 215
Endnotes	p. 218
Limitations and Extensions	p. 220
Problems that Occur when the Dependent Variable Has Particular Forms	p. 221
Problems that Occur when the Errors Have Particular Forms	p. 222
Problems that Call for the Use of Multiple Equation Models	p. 225
Endnotes	p. 229
Writing an Empirical Project	p. 231
Description of a Typical Empirical Project	p. 231
General Considerations	p. 233
Project Topics	p. 234
References	p. 238
Data Directory	p. 239
Index	p. 243
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