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Instructor’s Manual For Principles of Econometrics, Fourth Edition

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Solutions to Exercises in: Probability Primer 1 Chapter 2 The Simple Linear Regression Model 21 Chapter 3 Interval Estimation and Hypothesis Testing 54 Chapter 4 Prediction, Goodness of Fit and Modeling Issues 97 Chapter 5 The Multiple Regression Model 132 Chapter 6 Further Inference in the Multiple Regression Model 178 Chapter 7 Using Indicator Variables 225 Chapter 8 Heteroskedasticity 271 Chapter 9 Regression with Time Series Data: Stationary Variables 308 Chapter 10 Random Regressors and Moment Based Estimation 360 Chapter 11 Simultaneous Equations Models 387 Chapter 12 Regression with Time Series Data: Non-Stationary Variables 424 Chapter 13 Vector Error Correction and Vector Autoregressive Models 448 Chapter 14 Time-Varying Volatility and ARCH Models 472 Chapter 15 Panel Data Models 489 Chapter 16 Qualitative and Limited Dependent Variable Models 527 Appendix A Mathematical Tools 576 Appendix B Probability Concepts 586 Appendix C Review of Statistical Inference 6041 PROBABILITY PRIMER Exercise SolutionsProbability Primer, Exercise Solutions, Principles of Econometrics, 4e 2 EXERCISE P.1 (a) X is a random variable because attendance is not known prior to the outdoor concert. Before the concert, attendance is uncertain because the weather is uncertain. (b) Expected attendance is given by ( ) ( ) 500 0.2 1000 0.6 2000 0.2 1100 x E X x f x u  u  u ¦ (c) Expected profit is given by E Y E X E X ( ) (5 2000) 5 ( ) 3500   u  (d) The variance of profit is given by var( ) var(5 2000) 5 var( ) 25 240,000 6,000,000 Y X X 

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Subido en
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2024/2025
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Instructor’s Manual
For Principles of Econometrics, Fourth Edition

,Instructor’s Manual
For Principles of Econometrics, Fourth Edition


WILLIAM E. GRIFFITHS
University of Melbourne


R. CARTER HILL
Louisiana State University


GUAY C. LIM
University of Melbourne


SIMON YUNHO CHO
University of Melbourne


SIMONE SI-YIN WONG
University of Melbourne




JOHN WILEY & SONS, INC
New York / Chichester / Weinheim / Brisbane / Singapore / Toronto

,PREFACE

This Instructor’s Manual contains solutions to the Exercises in the Probability Primer, Chapters 2-
16 and Appendices A, B and C in Principles of Econometrics, 4th edition, by R. Carter Hill,
William E. Griffiths and Guay C. Lim (John Wiley & Sons, 2011).
There are several other resources available for both students and instructors. Full details can be
found on the Web page http://principlesofeconometrics.com/poe4/poe4.htm. These resources
include:
Answers to Selected Exercises. These answers are available to both students and
instructors. They are shortened versions of the solutions in this Manual for exercises that
are marked in POE4 with a *.
Supplementary computer handbooks designed for students to learn software at the same
time as they are using Principles of Econometrics to learn econometrics. These
handbooks are available for the following software packages:
EViews
Stata
GRETL
Excel
SAS
Data files for all text examples and exercises. The following types of files are available:
Data definition files (*.def) are text files containing variable names, definitions
and summary statistics.
Text files (*.dat) containing only data. Variable names are in *.def files.
EViews workfiles (*.wf1) compatible with EViews Versions 6 or 7.
Stata data sets (*.dta) readable using Stata Version 9 or later.
Excel spreadheets (*.xlsx) for Excel 2007 or 2010.
GRETL data sets (*.gdt).
SAS data sets (*.sas7bdat) compatible with SAS Version 7 or later.
We welcome any comments on this manual. Please feel free to contact us if you discover errors or
have suggestions for improvements.



William E. Griffiths Simon Yunho Cho

R. Carter Hill Simone Si-Yin Wong

Guay C. Lim
October 1, 2011

, CONTENTS

Solutions to Exercises in:
Probability Primer 1
Chapter 2 The Simple Linear Regression Model 21
Chapter 3 Interval Estimation and Hypothesis Testing 54
Chapter 4 Prediction, Goodness of Fit and Modeling Issues 97
Chapter 5 The Multiple Regression Model 132
Chapter 6 Further Inference in the Multiple Regression Model 178
Chapter 7 Using Indicator Variables 225
Chapter 8 Heteroskedasticity 271
Chapter 9 Regression with Time Series Data: Stationary Variables 308
Chapter 10 Random Regressors and Moment Based Estimation 360
Chapter 11 Simultaneous Equations Models 387
Chapter 12 Regression with Time Series Data: Non-Stationary Variables
424
Chapter 13 Vector Error Correction and Vector Autoregressive Models 448
Chapter 14 Time-Varying Volatility and ARCH Models 472
Chapter 15 Panel Data Models 489
Chapter 16 Qualitative and Limited Dependent Variable Models 527
Appendix A Mathematical Tools 576
Appendix B Probability Concepts 586
Appendix C Review of Statistical Inference 604
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