ECON 414
Solution to Exam 1
Solution to the 1st Midterm: Form B
1
Problem 1.
Y=2
Y=4
X=0
0.2
0.3
X=2
0.2
0.2
X=3
0.1
0.0
a. P (Y = 4 or X = 0) = P (Y = 4) + P (X = 0) P (X = 0, Y = 4) = 0.5 + 0.5 0.3 = 0.7
b.
E(X|Y = 4) =0 P (X = 0|Y = 4) + 2 P (X = 2|Y =
Econ 414
Jing Lu
Chapter 6
6.4
log( wage) log(wage)
= 1 + 2 pareduc . I expect beta2 is positive, because parents education has
educ
educ
(i)
positive effect on childrens education and salary. The higher the parents education, the higher the childrens
ed
Econ 414
Jing Lu
Chapter 7
7.2
(i) The coefficient on cigs is -0.0044. It means 1 more cigarette smoke per day leads to 0.44% decrease
on birth weight. So if the pregnant smoke 10 more cigarettes per day, the birth weight will decrease by
4.4%.
(ii) The c
UNIVERSITY OF SOUTHERN CALIFORNIA
DEPARTMENT OF ECONOMICS
ECON 414: Introduction to Econometrics, Spring 2013
Instructor: Yu-Wei Hsieh
HOMEWORK 4
DUE Wed. Feb. 20th BEFORE 12:00AM in your TAs mailbox
on the 3rd oor, KAP Hall.
Problem 1 Consider the follow
ECON 414
Answers to Homework 2
Answers to Homework 2
Chapter 3
Exercise 3.5
a. False X values being closer to their mean implies that Sxx is smaller. From equations 3.18 and 3.19 in the textbook, we see that a smaller Sxx means a larger variance.
Department of Economics, University of Southern California ECONOMICS 414, Introduction to Econometrics, Spring 2008 #26134R, 12:00-1:50pm TTh, KAP146 Instructor: Manochehr Rashidian Office Hours: Monday and Wednesday 12:00 1:00 pm, and by appointmen
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UNIVERSITY OF SOUTHERN CALIFORNIA
DEPARTMENT OF ECONOMICS
ECON 414: Introduction to Econometrics, Spring 2013
Instructor: Yu-Wei Hsieh
HOMEWORK 3
DUE MONDAY Feb. 11th BEFORE 12:00AM in your TAs
mailbox on the 3rd oor, KAP Hall.
Problem 1 Please do the fol
UNIVERSITY OF SOUTHERN CALIFORNIA
DEPARTMENT OF ECONOMICS
ECON 414: Introduction to Econometrics, Spring 2013
Instructor: Yu-Wei Hsieh
HOMEWORK 2
DUE MONDAY Feb. 4th BEFORE 12:00AM in your TAs
mailbox on the 3rd oor, KAP Hall.
Problem 1 (Sampling Distribu
UNIVERSITY OF SOUTHERN CALIFORNIA
DEPARTMENT OF ECONOMICS
ECON 414: Introduction to Econometrics, Spring 2013
Instructor: Yu-Wei Hsieh
HOMEWORK 1
DUE MONDAY JAN. 28 BEFORE 11:00AM in your TAs
mailbox on the 3rd oor, KAP Hall.
Problem 1. Suppose that a pop
Econ 414 Final Exam FOP/n 8 Name
1- In the following In
ode K
Ln(mcome) = 0.882 » pe 0.00021 xper2 + 0.095Educ + 0.126Ma1e 0.013 ale.Educ,
STE (0.77) (0.12%)}, (0.015 (0.041) (0.033) ( .005)\ I
SSR=2.33 R2=0.767 n=41 (31
Income is annual income, exper
Econ 414 form 8 Exam 2 ' Name
Answer questions 1-6 using the following models: We have observations for 40 different cities on
bustravel (1000 passenger hours), gasprice ($lgallon), income (average per capita income), pop
(population of the city in mil),
Econ 414
Jing Lu
Chapter 7
8.2
var(u | inc, price, educ, female) = 2inc 2 , so we can define the heteroskedasticity function h( x) = inc 2
. Therefore,
h( x) = inc . Thus the transformed equation is obtained by dividing the original equation by inc
beer
1
ECON 414
Answers to Homework 1
Answers to Homework 1
1
Chapter 1
Exercise 1.1
We would expect ice cream consumption to depend on its price and how hot the day is. Income might also be important, but less so than the above variables. Thus, a poss
Homework 8
8.11
2 2 a. t = 1 + 2 St + 3 Dt + 4 St2 + 5 (St Dt ). Note that Dt is not used here because
it is a dummy variable. b. 2 = 3 = 4 = 5 = 0. ^ ^ ^ c. Regress Pt against a constant, St , and Dt and obtain ut = Pt - 1 - 2 St - 3 Dt . ^ Next r
Handout for chapter 9. SERIAL CORRELATION MODEL 4: OLS estimates using the 33 observations 1962-1994 Dependent variable: POP (data 7-21) VARIABLE 0) 1) const YEAR COEFFICIENT -185.3590 0.1221 56.170 4.8768 0.901 283.578 STDERROR 14.3429 0.0073 T STAT
Econ 414
Jing Lu
Chapter 1
1.2
(i) It is supposed that only hours of job training are different, other factors are same. Other factors
include workers education level, years of experience, technology used by the firms, etc. Also ignore
the effect of scale
Econ 414
Jing Lu
Chapter 3
3.4
(i) It makes sense to assume that there is a negative relation between school ranking and new graduates
starting salary. The higher the ranking (lower rank value), the higher the starting salary. So we can expect
the coeffic
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