Solutions Manual
Econometric Analysis
Fifth Edition
William H. Greene
New York University
Prentice Hall, Upper Saddle River, New Jersey 07458
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,Contents and Notation
Chapter 1 Introduction 1
Chapter 2 The Classical Multiple Linear Regression Model 2
Chapter 3 Least Squares 3
Chapter 4 Finite-Sample Properties of the Least Squares Estimator 7
Chapter 5 Large-Sample Properties of the Least Squares and Instrumental Variables Estimators 14
Chapter 6 Inference and Prediction 19
Chapter 7 Functional Form and Structural Change 23
Chapter 8 Specification Analysis and Model Selection 30
Chapter 9 Nonlinear Regression Models 32
Chapter 10 Nonspherical Disturbances - The Generalized Regression Model 37
Chapter 11 Heteroscedasticity 41
Chapter 12 Serial Correlation 49
Chapter 13 Models for Panel Data 53
Chapter 14 Systems of Regression Equations 63
Chapter 15 Simultaneous Equations Models 72
Chapter 16 Estimation Frameworks in Econometrics 78
Chapter 17 Maximum Likelihood Estimation 84
Chapter 18 The Generalized Method of Moments 93
Chapter 19 Models with Lagged Variables 97
Chapter 20 Time Series Models 101
Chapter 21 Models for Discrete Choice 1106
Chapter 22 Limited Dependent Variable and Duration Models 112
Appendix A Matrix Algebra 115
Appendix B Probability and Distribution Theory 123
Appendix C Estimation and Inference 134
Appendix D Large Sample Distribution Theory 145
Appendix E Computation and Optimization 146
In the solutions, we denote:
scalar values with italic, lower case letters, as in a or
column vectors with boldface lower case letters, as in b,
row vectors as transposed column vectors, as in b,
single population parameters with greek letters, as in ,
sample estimates of parameters with English letters, as in b as an estimate of ,
sample estimates of population parameters with a caret, as in ˆ
matrices with boldface upper case letters, as in M or ,
cross section observations with subscript i, time series observations with subscript t.
These are consistent with the notation used in the text.
Downloaded by Areeba Fatima ()
,Chapter 1
Introduction
There are no exercises in Chapter 1.
1
Downloaded by Areeba Fatima ()
, Chapter 2
The Classical Multiple Linear
Regression Model
There are no exercises in Chapter 2.
2
Downloaded by Areeba Fatima ()
Econometric Analysis
Fifth Edition
William H. Greene
New York University
Prentice Hall, Upper Saddle River, New Jersey 07458
Downloaded by Areeba Fatima ()
,Contents and Notation
Chapter 1 Introduction 1
Chapter 2 The Classical Multiple Linear Regression Model 2
Chapter 3 Least Squares 3
Chapter 4 Finite-Sample Properties of the Least Squares Estimator 7
Chapter 5 Large-Sample Properties of the Least Squares and Instrumental Variables Estimators 14
Chapter 6 Inference and Prediction 19
Chapter 7 Functional Form and Structural Change 23
Chapter 8 Specification Analysis and Model Selection 30
Chapter 9 Nonlinear Regression Models 32
Chapter 10 Nonspherical Disturbances - The Generalized Regression Model 37
Chapter 11 Heteroscedasticity 41
Chapter 12 Serial Correlation 49
Chapter 13 Models for Panel Data 53
Chapter 14 Systems of Regression Equations 63
Chapter 15 Simultaneous Equations Models 72
Chapter 16 Estimation Frameworks in Econometrics 78
Chapter 17 Maximum Likelihood Estimation 84
Chapter 18 The Generalized Method of Moments 93
Chapter 19 Models with Lagged Variables 97
Chapter 20 Time Series Models 101
Chapter 21 Models for Discrete Choice 1106
Chapter 22 Limited Dependent Variable and Duration Models 112
Appendix A Matrix Algebra 115
Appendix B Probability and Distribution Theory 123
Appendix C Estimation and Inference 134
Appendix D Large Sample Distribution Theory 145
Appendix E Computation and Optimization 146
In the solutions, we denote:
scalar values with italic, lower case letters, as in a or
column vectors with boldface lower case letters, as in b,
row vectors as transposed column vectors, as in b,
single population parameters with greek letters, as in ,
sample estimates of parameters with English letters, as in b as an estimate of ,
sample estimates of population parameters with a caret, as in ˆ
matrices with boldface upper case letters, as in M or ,
cross section observations with subscript i, time series observations with subscript t.
These are consistent with the notation used in the text.
Downloaded by Areeba Fatima ()
,Chapter 1
Introduction
There are no exercises in Chapter 1.
1
Downloaded by Areeba Fatima ()
, Chapter 2
The Classical Multiple Linear
Regression Model
There are no exercises in Chapter 2.
2
Downloaded by Areeba Fatima ()
