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• Contents
• Preface
• Chapter 1: An Overview of Econometrics
• 1.1 The importance of econometrics
• 1.2 Types of economic data
• 1.2.1 Time series data
• 1.2.2 Cross-sectional data
• 1.2.3 Panel data
• 1.2.4 Obtaining data
• 1.2.5 Data transformations: levels and growth rates
• 1.3 Working with data: graphical methods
• 1.3.1 Time series graphs
• 1.3.2 Histograms
• 1.3.3 XY plots
• 1.4 Working with data: descriptive statistics and correlation
• 1.4.1 Expected values and variances
• 1.4.2 Correlation
• 1.4.3 Population Correlations and Covariances
• 1.5 Chapter summary
• Exercises
• Endnotes
• Chapter 2: A Non-technical Introduction to Regression
• 2.1 Introduction
• 2.2 The simple regression model
• 2.2.1 Regression as a best-¢tting line
• 2.2.2 Interpreting OLS estimates
• 2.2.3 Measuring the ¢t of a regression model
• 2.2.4 Basic statistical concepts in the regression model
• 2.2.5 Hypothesis testing involving R²: The F-statistic
• 2.3 The multiple regression model
• 2.3.1 Ordinary least squares estimation of the multiple regression model
• 2.3.2 Statistical aspects of multiple regression
• 2.3.3 Interpreting OLS estimates in the multiple regression model
• 2.3.4 Which explanatory variables to choose in a multiple regression model?
• 2.3.5 Multicollinearity
• 2.3.6 Multiple regression with dummy variables
• 2.3.7 What if the dependent variable is a dummy?
• 2.4 Chapter summary
• Exercises
• Endnotes
• Chapter 3: The Econometrics of the Simple Regression Model
• 3.1 Introduction
• 3.2 A review of basic concepts in probability in the context of the regression model
• 3.3 The classical assumptions for the regression model
• 3.4 Properties of the ordinary least-squares estimator of β
• 3.5 Deriving a confidence interval for β
• 3.6 Hypothesis tests about β
• 3.7 Modifications to statistical procedures when σ² is unknown
• 3.8 Chapter summary
• Exercises
• Appendix 1: Proof of the Gauss^Markov theorem
• Appendix 2: Using asymptotic theory in the simple regression model
• Endnotes
• Chapter 4: The Econometrics of the Multiple Regression Model
• 4.1 Introduction
• 4.2 Basic results for the multiple regression model
• 4.3 Issues relating to the choice of explanatory variables
• 4.3.1 Omitted variables bias
• 4.3.2 Inclusion of irrelevant explanatory variables
• 4.3.3 Multicollinearity
• 4.4 Hypothesis testing in the multiple regression model
• 4.4.1 F-tests
• 4.4.2 Likelihood ratio tests
• 4.5 Choice of functional form in the multiple regression model
• 4.5.1 Non-linearity in regression
• 4.5.2 How to decide which non-linear form?
• 4.6 Chapter summary
• Exercises
• Appendix:Wald and Lagrange multiplier tests
• Endnotes
• Chapter 5: The Multiple Regression Model: Freeing Up the Classical Assumptions
• 5.1 Introduction
• 5.2 Basic theoretical results
• 5.3 Heteroskedasticity
• 5.3.1 Some theoretical results assuming σ²ω²i is known
• 5.3.2 Heteroskedasticity: Estimation when error variancesare unknown
• 5.3.3 Testing for heteroskedasticity
• 5.3.4 Recommendations for empirical practice
• 5.4 The regression model with autocorrelated errors
• 5.4.1 Properties of autocorrelated errors
• 5.4.2 The GLS estimator for the regression model withautocorrelated errors
• 5.4.3 Testing for autocorrelated errors
• 5.5 The instrumental variables estimator
• 5.5.1 Case 1: The explanatory variable is random butindependent of the error
• 5.5.2 Case 2: The explanatory variable is correlatedwith the error term
• 5.5.3 Why might the explanatory variable be correlatedwith error?
• 5.6 Chapter summary
• Exercises
• Appendix: Asymptotic results for the OLS and instrumental variables estimators
• Endnotes
• Chapter 6: UnivariateTime Series Analysis
• 6.1 Introduction
• 6.2 Time series notation
• 6.3 Trends in time series variables
• 6.4 The autocorrelation function
• 6.5 The autoregressive model
• 6.5.1 The AR(1) model
• 6.5.2 Extensions of the AR(1) model
• 6.5.3 Testing in the AR(p) with deterministic trend model
• 6.6 Defining stationarity
• 6.7 Modeling volatility
• 6.7.1 Volatility in asset prices: introduction
• 6.7.2 Autoregressive conditional heteroskedasticity (ARCH)
• 6.8 Chapter summary
• Exercises
• Appendix: MA and ARMA models
• Endnotes
• Chapter 7: Regression withTime SeriesVariables
• 7.1 Introduction
• 7.2 Time series regression when X and Yare stationary
• 7.3 Time series regression whenYand X have unit roots
• 7.3.1 Spurious regression
• 7.3.2 Cointegration
• 7.3.3 Estimation and testing with cointegrated variables
• 7.3.4 Time series regression when Y and X are cointegrated:the error correction model
• 7.4 Time series regression whenYand X have unit roots but are NOTcointegrated
• 7.5 Granger causality
• 7.5.1 Granger causality in the ADL model
• 7.5.2 Granger causality with cointegrated variables
• 7.6 Vector autoregressions
• 7.6.1 Forecasting withVARs
• 7.6.2 Vector Autoregressions with cointegrated variables
• 7.6.3 UsingVARs: impulse response functionsand variance decompositions
• 7.7 Chapter summary
• Exercises
• Appendix: The theory of forecasting
• Endnotes
• Chapter 8: Models for Panel Data
• 8.1 Introduction
• 8.2 The pooled model
• 8.3 Individual e¡ects models
• 8.3.1 The fixed e¡ects model
• 8.3.2 The random e¡ects model
• 8.3.3 Extensions to individual e¡ects models
• 8.4 Chapter summary
• Exercises
• Endnotes
• Chapter 9: Qualitative Choice and Limited Dependent Variable Models
• 9.1 Introduction
• 9.2 Qualitative choice models
• 9.2.1 Binary choice models
• 9.2.2 Multinomial choice models
• 9.3 Limited dependent variable models
• 9.3.1 Tobit
• 9.3.2 Working with count data
• 9.3.3 Extensions
• 9.4 Chapter summary
• Exercises
• Endnotes
• Chapter 10: Bayesian Econometrics
• 10.1 An overview of Bayesian econometrics
• 10.2 The normal linear regression model with natural conjugate prior and a single explanatory variab
• 10.2.1 The likelihood function
• 10.2.2 The prior
• 10.2.3 The posterior
• 10.2.4 Model comparison in the simple regression model
• 10.3 Chapter summary
• Exercises
• Appendix: Bayesian analysis of the simple regression model with unknown variance
• Endnotes
• Appendix A: Mathematical Basics
• Functions and the equation of a straight line
• Logarithms
• Summation and product notation
• Appendix B: Probability Basics
• Basic concepts of probability
• Definition B.1: Experiments and events
• Definition B.2: Discrete and continuous variables
• Definition B.3: Random variables and probability (informal definition)
• Definition B.4: Independence
• Definition B.5: Conditional probability
• Definition B.6: Probability and distribution functions
• Definition B.7: Probability density and distribution functions
• Definition B.8: Expected value, variance, covariance, and correlation
• Definition B.9: Joint probability density functions
• Definition B.10: The normal distribution
• Definition B.11: The chi-square distribution
• Definition B.12: The Student t distribution
• Definition B.13: The F-distribution
• Definition B.14: Other statistical terminology
• Advanced material used only in the appendix to Chapter 10
• Definition B.15: The gamma distribution
• Definition B.16: The t-distribution
• Definition B.17: The normal–gamma distribution
• Appendix C: Basic Concepts in Asymptotic Theory
• Convergence in probability
• A basic law of large numbers
• Another law of large numbers
• Convergence in distribution
• A basic central limit theorem
• Other useful theorems
• Appendix D: Writing an Empirical Project
• Description of a typical empirical project
• General considerations
• Project topics
• Project 1: The equity underpricing puzzle
• Project 2: What moves the stock and bond markets?
• Tables
• Table 1. Area under the standard normal distribution Pr(0 ≤ Z ≤ z)
• Table 2. Area under the Student t distribution for di¡erent degrees of freedom (DF), Pr(Z ≥ z) =
• Table 3. Percentiles of the chi-square distribution
• Table 4a. Area under the F-distribution for di¡erent degrees of freedom, n1 and n2, Pr(Z ≥ z) = 0
• Table 4b. Area under the F-distribution for di¡erent degrees of freedom, n1 and n2, Pr(Z ≥ z) = 0
• Bibliography
• Index