CHAPTER
14
Advanced Panel Data Methods
I
n this chapter, we cover two methods for estimating unobserved effects panel data models
that are at least as common as first differencing. Although these meth
CHAPTER
4
Multiple Regression Analysis:
Inference
T
his chapter continues our treatment of multiple regression analysis. We now turn
to the problem of testing hypotheses about the parameters in the po
CHAPTER
2
The Simple Regression Model
T
he simple regression model can be used to study the relationship between two
variables. For reasons we will see, the simple regression model has limitations as
CHAPTER
3
Multiple Regression Analysis:
Estimation
I
n Chapter 2, we learned how to use simple regression analysis to explain a dependent
variable, y, as a function of a single independent variable, x
CHAPTER
6
Multiple Regression Analysis:
Further Issues
T
his chapter brings together several issues in multiple regression analysis that we
could not conveniently cover in earlier chapters. These topi
CHAPTER
10
Basic Regression Analysis with
Time Series Data
I
n this chapter, we begin to study the properties of OLS for estimating linear regression
models using time series data. In Section 10.1, we
CHAPTER
7
Multiple Regression Analysis
with Qualitative Information:
Binary (or Dummy) Variables
I
n previous chapters, the dependent and independent variables in our multiple regression
models have h
CHAPTER
8
Heteroskedasticity
T
he homoskedasticity assumption, introduced in Chapter 3 for multiple regression,
states that the variance of the unobservable error, u, conditional on the explanatory
va
CHAPTER
13
Pooling Cross Sections
across Time: Simple Panel
Data Methods
U
ntil now, we have covered multiple regression analysis using pure cross-sectional
or pure time series data. Although these tw
CHAPTER 2
TEACHING NOTES This is the chapter where I expect students to follow most, if not all, of the algebraic derivations. In class I like to derive at least the unbiasedness of the OLS slope coef
CHAPTER 4
TEACHING NOTES At the start of this chapter is good time to remind students that a specific error distribution played no role in the results of Chapter 3. That is because only the first two
CHAPTER 3
TEACHING NOTES For undergraduates, I do not work through most of the derivations in this chapter, at least not in detail. Rather, I focus on interpreting the assumptions, which mostly concer
CHAPTER
5
Multiple Regression Analysis:
OLS Asymptotics
I
n Chapters 3 and 4, we covered what are called finite sample, small sample, or exact
properties of the OLS estimators in the population model
CHAPTER
9
More on Specification
and Data Issues
I
n Chapter 8, we dealt with one failure of the Gauss-Markov assumptions. While heteroskedasticity in the errors can be viewed as a problem with a model
CHAPTER
12
Serial Correlation
and Heteroskedasticity
in Time Series Regressions
I
n this chapter, we discuss the critical problem of serial correlation in the error terms
of a multiple regression mode
CHAPTER
15
Instrumental Variables Estimation
and Two Stage Least Squares
I
n this chapter, we further study the problem of endogenous explanatory variables in
multiple regression models. In Chapter 3,
CHAPTER
1
The Nature of Econometrics
and Economic Data
C
hapter 1 discusses the scope of econometrics and raises general issues that arise in
the application of econometric methods. Section 1.3 examin
CHAPTER
16
Simultaneous Equations Models
I
n the previous chapter, we showed how the method of instrumental variables can
solve two kinds of endogeneity problems: omitted variables and measurement err
Nikhil Patel
September 21 ,2016
HCAD 8517
South End Community Health Center
Vision Statement: Reaching out with Heart in Hand championing the needs of the
underserved and providing healthcare for all,
CHAPTER 16
TEACHING NOTES I spend some time in Section 16.1 trying to distinguish between good and inappropriate uses of SEMs. Naturally, this is partly determined by my taste, and many applications f
CHAPTER 17
TEACHING NOTES I emphasize to the students that, first and foremost, the reason we use the probit and logit models is to obtain more reasonable functional forms for the response probability
CONTENTS
PREFACE
iii
SUGGESTED COURSE OUTLINES
iv
Chapter 1
The Nature of Econometrics and Economic Data
1
Chapter 2
The Simple Regression Model
5
Chapter 3
Multiple Regression Analysis: Estimation
15
CHAPTER 19
TEACHING NOTES
Students should read this chapter if you have assigned them a term paper. I used to allow
students to choose their own topics, but this is difficult in a first-semester cours
CHAPTER 18
TEACHING NOTES
Several of the topics in this chapter, including testing for unit roots and cointegration, are now
staples of applied time series analysis. Instructors who like their course
CHAPTER 14
TEACHING NOTES
My preference is to view the fixed and random effects methods of estimation as applying to the
same underlying unobserved effects model. The name unobserved effect is neutral
CHAPTER 13
TEACHING NOTES
While this chapter falls under Advanced Topics, most of this chapter requires no more
sophistication than the previous chapters. (In fact, I would argue that, with the possib
CHAPTER 12
TEACHING NOTES
Most of this chapter deals with serial correlation, but it also explicitly considers
heteroskedasticity in time series regressions. The first section allows a review of what
CHAPTER 10
TEACHING NOTES
Because of its realism and its care in stating assumptions, this chapter puts a somewhat heavier
burden on the instructor and student than traditional treatments of time seri
CHAPTER 11
TEACHING NOTES
Much of the material in this chapter is usually postponed, or not covered at all, in an introductory
course. However, as Chapter 10 indicates, the set of time series applicat
APPENDIX D
SOLUTIONS TO PROBLEMS
0 1 6
2 1 7
20
D.1 (i) AB =
1 8 0 =
4 5 0
5
3 0 0
6
12
36 24
(ii) BA does not exist because B is 3 3 and A is 2 3.
D.2 This result is easy to visualize. I