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 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 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 pr
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 cl
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
a
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
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
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 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
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 cl
CHAPTER 6
TEACHING NOTES
I cover most of Chapter 6, but not all of the material in great detail. I use the example in Table
6.1 to quickly run through the effects of data scaling on the important OLS
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
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)
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
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 betw
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 pa
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 i
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
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
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
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 linea
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 vari
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
u
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
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 pr
CHAPTER 9
TEACHING NOTES
The coverage of RESET in this chapter recognizes that it is a test for neglected nonlinearities,
and it should not be expected to be more than that. (Formally, it can be shown
ECON0701/2280 Introductory Econometrics
Tutorial 1
1. X is a random variable. Simplify E(5 + 6X).
2. X and Y are random variables. Simplify var(2X + 3Y + 4) in each of the following situation:
(a) whe