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| Preface to the Third Edition | |
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| Preface to the Second Edition | |
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| Preface to the First Edition | |
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| Introduction | |
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| Organization of the Book | |
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| Useful Background | |
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| Mathematical Concepts Used in this Book | |
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| Endnote | |
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| References | |
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| Basic Data Handling | |
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| Types of Economic Data | |
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| Obtaining Data | |
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| Working with Data: Graphical Methods | |
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| Working with Data: Descriptive Statistics | |
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| Index Numbers | |
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| Advanced Descriptive Statistics | |
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| Expected Values and Variances | |
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| Endnotes | |
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| Correlation | |
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| Understanding Correlation | |
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| Understanding Why Variables Are Correlated | |
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| Understanding Correlation through XY-plots | |
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| Correlation between Several Variables | |
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| Mathematical Details | |
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| Endnotes | |
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| An Introduction to Simple Regression | |
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| Regression as a Best Fitting Line | |
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| Interpreting OLS Estimates | |
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| Fitted Values and R<sup>2</sup>: Measuring the Fit of a Regression Model | |
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| Nonlinearity in Regression | |
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| Mathematical Details | |
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| Endnotes | |
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| Statistical Aspects of Regression | |
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| Which Factors Affect the Accuracy of the Estimate <$$$>? | |
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| Calculating a Confidence Interval for � | |
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| Testing whether �=0 | |
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| Hypothesis Testing Involving R<sup>2</sup>: The F Statistic | |
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| Using Statistical Tables for Testing whether �=0 | |
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| Endnotes | |
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| References | |
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| Multiple Regression | |
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| Regression as a Best Fitting Line | |
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| Ordinary Least Squares Estimation of the Multiple Regression Model | |
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| Statistical Aspects of Multiple Regression | |
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| Interpreting OLS Estimates | |
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| Pitfalls of Using Simple Regression in a Multiple Regression Context | |
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| Omitted Variables Bias | |
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| Multicollinearity | |
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| Mathematical Interpretation of Regression Coefficients | |
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| Endnotes | |
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| Regression with Dummy Variables | |
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| Simple Regression with a Dummy Variable | |
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| Multiple Regression with Dummy Variables | |
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| Multiple Regression with Dummy and Nondummy Explanatory Variables | |
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| Interacting Dummy and Nondummy Variables | |
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| What if the Dependent Variable is a Dummy? | |
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| Endnotes | |
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| Regression with Time Lags: Distributed Lag Models | |
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| Aside on Lagged Variables | |
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| Aside on Notation | |
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| Selection of Lag Order | |
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| Other Distributed Lag Models | |
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| Endnotes | |
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| Univariate Time Series Analysis | |
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| The Autocorrelation Function | |
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| The Autoregressive Model for Univariate Time Series | |
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| Nonstationary versus Stationary Time Series | |
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| Extensions of the AR(1) Model | |
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| Testing in the AR(p) with Deterministic Trend Model | |
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| Mathematical Intuition for the AR(1) Model | |
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| Endnotes | |
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| References | |
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| Regression with Time Series Variables | |
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| Time Series Regression when X and Y Are Stationary | |
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| Time Series Regression when Y and X Have Unit Roots: Spurious Regression | |
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| Time Series Regression when Y and X Have Unit Roots: Cointegration | |
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| Time Series Regression when Y and X Are Cointegrated: The Error Correction Model | |
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| Time Series Regression when Y and X Have Unit Roots but Are NOT Cointegrated | |
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| Endnotes | |
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| Applications of Time Series Methods in Macroeconomics and Finance | |
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| Volatility in Asset Prices | |
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| Autoregressive Conditional Heteroskedasticity (ARCH) | |
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| Granger Causality | |
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| Vector Autoregressions | |
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| Hypothesis Tests Involving More than One Coefficient | |
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| Endnotes | |
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| Limitations and Extensions | |
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| Problems that Occur when the Dependent Variable Has Particular Forms | |
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| Problems that Occur when the Errors Have Particular Forms | |
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| Problems that Call for the Use of Multiple Equation Models | |
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| Endnotes | |
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| Writing an Empirical Project | |
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| Description of a Typical Empirical Project | |
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| General Considerations | |
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| Project Topics | |
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| References | |
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| Data Directory | |
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| Index | |