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 moments were
derived under the full set of Gauss-Markov
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 fall into a gray area. But students who are going to lea
CHAPTER 8
TEACHING NOTES This is a good place to remind students that homoskedasticity played no role in showing that OLS is unbiased for the parameters in the regression equation. In addition, you probably should mention that there is nothing wrong with
CHAPTER 5
TEACHING NOTES Chapter 5 is short, but it is conceptually more difficult than the earlier chapters, primarily because it requires some knowledge of asymptotic properties of estimators. In class, I give a brief, heuristic description of consisten
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
3 March 2014
Multiple Regression Analysis with Qualitative Information
Last time, we discussed the method of using binary variables, or dummy variables, to
analyze data where the va
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
Assignment #1: Due February 14, 2014
1. Let 1 2 be independent random variables that all have the same probability distrib1P
ution, with mean and variance 2 . Let = .
=1
(a) Show th
APPENDIX B
SOLUTIONS TO PROBLEMS
B.1 Before the student takes the SAT exam, we do not know nor can we predict with certainty
what the score will be. The actual score depends on numerous factors, many of which we
cannot even list, let alone know ahead of
APPENDIX C
SOLUTIONS TO PROBLEMS
C.1 (i) This is just a special case of what we covered in the text, with n = 4: E( Y ) = and
Var( Y ) = 2/4.
(ii) E(W) = E(Y1)/8 + E(Y2)/8 + E(Y3)/4 + E(Y4)/2 = [(1/8) + (1/8) + (1/4) + (1/2)] = (1 +
1 + 2 + 4)/8 = , which
University of Hong Kong
Introductory Econometrics (ECON0701), Fall 2013
29 November 2013
Serial Correlation and Heteroskedasticity in Time Series
Regressions
Last time, we discussed the consequences of serial correlation for OLS estimates.
Like heterosk
CHAPTER 7
TEACHING NOTES
This is a fairly standard chapter on using qualitative information in regression analysis, although
I try to emphasize examples with policy relevance (and only cross-sectional applications are
included.).
In allowing for different
APPENDIX E
SOLUTIONS TO PROBLEMS
E.1 This follows directly from partitioned matrix multiplication in Appendix D. Write
x1
x
2
X = 2 , X = ( x1 x x ), and y =
n
x
n
Therefore, XX =
n
xx
t =1
t
t
and Xy =
n
x y
t =1
t
t
y1
y2
y
n
. An equivalent
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
27 February 2014
Multiple Regression Analysis with Qualitative Information
Up until now, in our studies of the multiple linear regression model
y 0 1 x1 2 x2 . k xk u
all of the x
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
24 February 2014
Administrative Matters
You are probably wondering about the scope of the midterm examination, which will
be held on Monday, March 17th.
It will cover chapters 2,
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
20 February 2014
Multiple Regression Analysis: Inference
Last time, we started to discuss the concept of confidence intervals, and their use in
inference and hypothesis testing.
T
University of Hong Kong
Introductory Econometrics (ECON0701), Fall 2013
22 November 2013
Further Issues in Using OLS with Time Series Data
Last time, we discussed the assumptions necessary for OLS parameter estimates to be
consistent in a time series con
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
20 January 2014
Administrative Matters
The Web page for the course is available at http:/hkuportal.hku.hk/moodle/guest
You will find lecture notes on this page and also the weekly
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
23 January 2014
Fundamentals of Mathematical Statistics
Last time, we reviewed some basic statistical concepts.
An important application of statistics to economic research is estima
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
27 January 2014
The Simple Regression Model
Last time, we introduced the simple regression model.
The simple regression model takes the form
y 0 1 x u
Where 0 and 1 are chosen so th
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
30 January 2014
Multiple Regression Analysis: Estimation
There are many times when we are interested in the relationship between the
dependent variable and more than one independen
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
10 February 2014
Multiple Regression Analysis: Estimation
Last time, we started to discuss estimating and interpreting the parameters of the
multiple linear regression model.
Just
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
13 February 2014
Multiple Regression Analysis: Estimation
Last time, we examined the sampling variance of the OLS estimator in the multiple
regression case.
In general, an estimat
University of Hong Kong
Introductory Econometrics (ECON0701), Spring 2014
17 February 2014
Multiple Regression Analysis: Inference
Last time, we started to talk about inference and hypothesis testing in the context of a
multiple linear regression model.
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. If A and B are n n diagonal matrices, then AB is an n n
d
CHAPTER 1
TEACHING NOTES
You have substantial latitude about what to emphasize in Chapter 1. I find it useful to talk about
the economics of crime example (Example 1.1) and the wage example (Example 1.2) so that
students see, at the outset, that econometr
CHAPTER 15
TEACHING NOTES
When I wrote the first edition, I took the novel approach of introducing instrumental variables as
a way of solving the omitted variable (or unobserved heterogeneity) problem. Traditionally, a
students first exposure to IV method
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 fall into a gray
area. But students who are going to lea
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. Once we move to a
nonlinear model with a fully specif
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 to be more time series
oriented might cover this chapte
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 course, and places a
heavy burden on instructors or teaching