SOLUTION TO END OF CHAPTER EXERCISES FOR: Introduction to Econometrics, 3rd Edition James H. Stock, and Mark W. Watson. CHAPTER 1-18 Questions And Answers

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,TABLE OF CONTENTS
PART I: INTRODUCTION AND REVIEW
 Economic Questions And Data
 Review Of Probability
 Review Of Statistics
PART II: FUNDAMENTALS OF REGRESSION ANALYSIS
 Linear Regression With One Regressor
 Regression With A Single Regressor: Hypothesis Tests And Confidence
Intervals
 Linear Regression With Multiple Regressors
 Hypothesis Tests And Confidence Intervals In Multiple Regression
 Nonlinear Regression Functions
 Assessing Studies Based On Multiple Regression
PART III: FURTHER TOPICS IN REGRESSION ANALYSIS
 Regression With Panel Data
 Regression With A Binary Dependent Variable
 Instrumental Variables Regression
 Experiments And Quasi-Experiments
 Prediction With Many Regressors And Big Data
PART IV: REGRESSION ANALYSIS OF ECONOMIC TIME SERIES DATA
 Introduction To Time Series Regression And Forecasting
 Estimation Of Dynamic Causal Effects
 Additional Topics In Time Series Regression
PART V: THE ECONOMIC THEORY OF REGRESSION ANALYSIS
 The Theory Of Linear Regression With One Regressor
 The Theory Of Multiple Regression




©2011 Pearson Education, Inc. Publishing as Addison Wesley

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Chapter 2
REVIEW OF PROBABILITY

2.1. (A) Probability Distribution Function For Y

Outcome (Number Of Heads) Y0 Y1 Y2
Probability 0.25 0.50 0.25
(B) Cumulative Probability Distribution Function For Y

Outcome (Number Of Y0 0Y1 1Y2 Y2
Heads)
Probability 0 0.25 0.75 1.0

(C)  = E(Y )  (0  0.25)  (1 0.50)  (2  0.25)  1.00 . F Fq, .
DY
Using Key Concept 2.3: Var(Y )  E(Y 2 ) [E(Y )]2 ,
And
(Ui |Xi
)
So That

Var(Y )  E(Y 2 ) [E(Y )]2  1.50  (1.00)2 
0.50.

2.2. We Know From Table 2.2 That Pr (Y  0)  Pr (Y  1)  Pr ( X  0)  030,
022, 078,
Pr( X  1)  070. So
(A) Y  E(Y )  0  Pr (Y  0)  1 Pr (Y  1)
 0  022  1 078  078,
X  E( X )  0  Pr ( X  0)  1 Pr ( X  1)
 0  030  1 070  070
(B)   E[( X   ) ]
2 2

X X




2

,  (0  0.70)2  Pr ( X  0)  (1  0.70)2  Pr ( X  1)
 (070)2  030  0302  070  021,
 Y2  E[(Y  Y )2 ]
 (0  0.78)2  Pr (Y  0)  (1  0.78)2  Pr (Y  1)
 (078)2  022  0222  078  01716




©2011 Pearson Education, Inc. Publishing as Addison Wesley