DESCRIPTION:
ABOUT THE AUTHOR
TABLE OF CONTENTS
Part I Introduction and the Linear Regression Model CHAPTER 1 What is Econometrics?
1.1 What Is Econometrics?
1.2 Economic and Econometric Models
1.3 The Aims and Methodology of Econometrics
1.4 What Constitutes a Test of an Economic Theory?
Summary and an Outline of the Book
2 Statistical Background and Matrix Algebra
2.1 Introduction
2.2 Probability
2.3 Random Variables and Probability Distributions
2.4 The Normal Probability Distribution and Related Distributions
2.5 Classical Statistical Inference 21
2.6 Properties of Estimators 23
2.7 Sampling Distributions for Samples from a Normal Population 26
2.8 Interval Estimation 27
2.9 Testing of Hypotheses 28
2.10 Relationship Between Confidence Interval Procedures and Tests of Hypotheses 32
Summary 33
Exercises 34
Appendix to Chapter 2 41
3 Simple Regression 59
3.1 Introduction
3.2 Specification of the Relationships
3.3 The Method of Moments
3.4 Method of Least Squares
3.5 Statistical Inference in the Linear Regression Model
3.6 Analysis of Variance for the Simple Regression Model
3.7 Prediction with the Simple Regression Model
3.8 Outliers
3.9 Alternative Functional Forms for Regression Equations
*3.10 Inverse Prediction in the Least Squares Regression Model
*3.11 Stochastic Regressors
*3.12 The Regression Fallacy
Summary
Exercises
Appendix to Chapter 3
4 Multiple Regression
4.1 Introduction
4.2 A Model with Two Explanatory Variables
4.3 Statistical Inference in the Multiple Regression Model
4.4 Interpretation of the Regression Coefficients
4.5 Partial Correlations and Multiple Correlation
4.6 Relationships Among Simple, Partial, and Multiple Correlation Coefficients
4.7 Prediction in the Multiple Regression Model
4.8 Analysis of Variance and Tests of Hypotheses
4.9 Omission of Relevant Variables and Inclusion of Irrelevant Variables
4.10 Degrees of Freedom and R2
4.11 Tests for Stability
*4.12 The LR, W, and LM Tests
Summary
Exercises
Appendix to Chapter 4
Data Sets
5 Heteroskedasticity 201
5.1 Introduction
5.2 Detection of Heteroskedasticity
5.3 Consequences of Heteroskedasticity
5.4 Solutions to the Heteroskedasticity Problem
5.5 Heteroskedasticity and the Use of Deflators
*5.6 Testing the Linear Versus Log-Linear Functional
Form
Summary
Exercises
Appendix to Chapter 5
6 Autocorrelation
6.1 Introduction
6.2 Durbin-Watson Test
6.3 Estimation in Levels Versus First Differences
6.4 Estimation Procedures with Autocorrelated Errors
6.5 Effect of AR(1) Errors on OLS Estimates
6.6 Some Further Comments on the DW Test
6.7 Tests for Serial Correlation in Models with Lagged Dependent Variables 248X CONTENTS
6.8 A General Test for Higher-Order Serial Correlation: The LM Test
6.9 Strategies When the DW Test Statistic Is Significant
6.10 Trends and Random Walks
*6.11 ARCH Models and Serial Correlation
Summary 265
Exercises 267
7 Multicollinearity 269
7.1 Introduction
7.2 Some Illustrative Examples
7.3 Some Measures of Multicollinearity
7.4 Problems with Measuring Multicollinearity
7.5 Solutions to the Multicollinearity Problem:
Ridge Regression
7.6 Principal Component Regression
7.7 Dropping Variables
7.8 Miscellaneous Other Solutions
Summary
Exercises
Appendix to Chapter 7
8 Dummy Variables and Truncated Variables
8.1 Introduction
8.2 Dummy Variables for Changes in the Intercept Term
8.3 Dummy Variables for Changes in Slope Coefficients
8.4 Dummy Variables for Cross-Equation Constraints
8.5 Dummy Variables for Testing Stability of
Regression Coefficients
8.6 Dummy Variables Under Heteroskedasticity and Autocorrelation
8.7 Dummy Dependent Variables
8.8 The Linear Probability Model and the Linear Discriminant Function
8.9 The Probit and Logit Models
8.10 Illustrative Example
8.11 Truncated Variables: The Tobit Model
Summary
Exercises
9 Simultaneous Equations Models
9.1 Introduction
9.2 Endogenous and Exogenous Variables
9.3 The Identification Problem: Identification Through Reduced Form
9.4 Necessary and Sufficient Conditions for Identification
9.5 Methods of Estimation: The Instrumental Variable Method
9.6 Methods of Estimation: The Two-Stage Least Squares Method
9.7 The Question of Normalization
*9.8 The Limited-Information Maximum Likelihood Method
*9.9 On the Use of OLS in the Estimation of Simultaneous-Equations Models
*9.10 Exogeneity and Causality
Summary 395
Exercises 397
Appendix to Chapter 9 400
10 Models of Expectations 405
10.1 Introduction
10.2 Naive Models of Expectations
10.3 The Adaptive Expectations Model
10.4 Estimation with the Adaptive Expectations Model
10.5 Two Illustrative Examples
10.6 Expectational Variables and Adjustment Lags
10.7 Partial Adjustment with Adaptive Expectations
10.8 Alternative Distributed Lag Models: Polynomial Lags
10.9 Rational Lags
10.10 Rational Expectations
10.11 Tests for Rationality
10.12 Estimation of a Demand and Supply Model Under Rational Expectations
10.13 The Serial Correlation Problem in Rational Expectations Models
Summary
Exercises
11 Errors in Variables
11.1 Introduction
11.2 The Classical Solution for a Single-Equation Model with One Explanatory Variable
11.3 The Single-Equation Model with Two Explanatory Variables
11.4 Reverse Regression
11.5 Instrumental Variable Methods
11.6 Proxy Variables
11.7 Some Other Problems
Summary
Exercises
12 Diagnostic Checking, Model Selection, and Specification Testing
12.1 Introduction
12.2 Diagnostic Tests Based on Least Squares Residuals
12.3 Problems with Least Squares Residuals
12.4 Some Other Types of Residuals 481
12.5 DFFITS and Bounded Influence Estimation
12.6 Model Selection
12.7 Selection of Regressors
12.8 Implied F-Ratios for the Various Criteria
12.9 Cross-Validation
12.10 Hausman’s Specification Error Test
12.11 The Plosser-Schwert-White Differencing Test
12.12 Tests for Nonnested Hypotheses
Summary
Exercises
Appendix to Chapter 12
13 Introduction to Time-Series Analysis 525
13.1 Introduction
13.2 Two Methods of Time-Series Analysis: Frequency
Domain and Time Domain
13.3 Stationary and Nonstationary Time Series
13.4 Some Useful Models for Time Series
13.5 Estimation of AR, MA, and ARMA Models
13.6 The Box-Jenkins Approach
13.7 R2 Measures in Time-Series Models
Summary
Exercises
Data Sets
14 Vector Autoregressions, Unit Roots, and Cointegration
14.1 Introduction
14.2 Vector Autoregressions
14.3 Problems with VAR Models in Practice
14.4 Unit Roots
14.5 Unit Root Tests
14.6 Cointegration
14.7 The Cointegrating Regression
14.8 Vector Autoregressions and Cointegration
14.9 Cointegration and Error Correction Models
14.10 Tests for Cointegration
14.11 Cointegration and Testing of the REH and MEH
14.12 A Summary Assessment of Cointegration
Summary 602
Exercises 603
APPENDIX: Tables 609
Author Index 623
Subject Index 627
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