Practice Questions for Midterm
1. Let Y be a random variable. Then var(Y) equals
a.
E[(Y Y ) 2 ] .
b. E[| (Y Y ) |] .
c. E[(Y Y ) 2 ] .
d. E[(Y Y )]
2. Two random variables X and Y are independently distributed if all of the following
conditions hold, wit
Ryerson University
Midterm Exam for ECN 627 Econometrics I
8:10-11am Oct. 19, 2016 at ENG102
I. Multiple choice questions(1 mark for each question, some
questions have more than 1 correct choices)
1. The random variable X represents the number of children
1
Solutions for the empirical exercises in Assignment 3
Empirical Exercise 5.1
Calculations for this exercise are carried out in the STATA file EE_5_1.do.
(a) The estimated regression is
= 512.7 + 707.7Height
(3379.9) (50.4)
The 95% confidential interval
Exercises 57 .l
c. Suppose that p = 0.3. Compute the mean, variance, skewness, and L
kurtosis of X. (Hint: You might find it helpful to use the formulas -
given in Exercise 2.21.)
2.5 In September, Seattles daily high temperature has a mean of 70F and .
a
98 CHAPTER 3 Review of Statistics
a. Show that 16 = Y.
b. Show that 13 is an unbiased estimator of p.
c. Show that varQ) : p(1 p) /n.
a survey of 400 likely voters, 215 responded that they would vote for the
incumbent, and 185 responded that they would vo
Exercises 245
l!-
v
lever, n0 . - Review the Concepts
fstudent . ','.-_ .'
'districts 5f ' 7.1 Explain how you would test the null hypothesis that 31 = 0 in the multiple
not linear '_7" regression model Y,- = .80 + [3an + )3sz + at. Explain how you woul
Ryerson University
Faculty of
o Arts
Department of Econo
omics
Fall 2016
ECN 627 Economeetrics I
8:10p
pm 11:00am
m Wednesdayys at ENG1002
Prerequisites: ECN 329, QMS 4442 or QMS 703
Instructo
or Name:
Office Location
Phone:
Email:
Office ho
our:
Website:
ECN 627
Practice for Midterm (Source: 2013 Midterm)
1. (3 points for each question) Indicate whether you agree or disagree. If you disagree, briefly explain
the reason (within 3 lines). If you dont provide a proper reason to disagree, you will get zero po
Faculty of Arts
Department of Economics
ECN 627 Econometrics I
Fall 2016, Section 04/05/06
ENG / M / MAIN / ENG / ENG101, Wed 08:00-11:00
Pre-Requisites: ECN 329, QMS 442, QMS 703
Instructor Name:
Office Location:
Office Hours:
Phone:
Email:
TA:
Yu Wang
J
Solutions to Assignment 3
Chapter 12
12.83
a
= .50
p z / 2 p(1 p) / n
b
= .33
p z / 2 p(1 p) / n
c
= .10
p z / 2 p(1 p) / n
1.96
= .50
.50 (1 .50 ) / 400
1.96
= .33
.33(1 .33) / 400
1.96
= .10
.10 (1 .10 ) / 400
.0490
.0461
.0294
d The interval narrows.
1
Chapter 1 Solutions
1. (a) Since = c , we have
P ()
= P (c )
1 P ()
=
=
11
=
0.
(b) Since A , we have
P (A)
=
P ()
1.
2. The purpose of assuming P (A) < 1, which implies P (Ac ) > 0, is that
P (A|Ac ) is only defined if P (Ac ) > 0. Since A Ac = , we hav
CECN 627 (2015 Spring)
Answers: Midterm
(1-10) Please answer True or False with brief explanation. (4 points for each question)
1 Suppose that the distribution of Y is N (3, 4). Let Z = (Y 3)/2. The distribution of Z is N (0, 1).
Yes, because
EZ
V arZ
Z
1
CECN 627 (2015 Spring)
Midterm
(1-10) Please answer True or False with brief explanation. (4 points for each question)
1 Suppose that the distribution of Y is N (3, 4). Let Z = (Y 3)/2. The distribution of Z is N (0, 1).
2 Let Yi , i = 1, . . . , n be ran
Chapter 2 Solutions
1. The log-likelihood function is
`(x1 , . . . , xn ; )
=
=
=
=
n
X
xi
1
exp
log
i=1
n
X
1
xi
log
+
i=1
n
X
xi
log(1) log()
i=1
n
X
xi
.
log()
i=1
Differentiating with respect to , we have
n
`(x1 , . . . , xn ; ) X 1
xi
=
+ 2 .
i=1
Ordinary Least Squares (OLS)
Brennan S. Thompson
Department of Economics, Ryerson University
October 7, 2012
Regression Models
I
In general, a regression model is written as
Yi = g (Xi ) + Ui ,
(i = 1, . . . , n)
where
I
I
I
I
Yi is the ith observation on
Errata
Last updated: 7:33pm, October 17, 2012
Chapter 1 (in ecn129-lecture_notes.pdf)
p. 12, 1st line: p(x) should be pX (x).
p. 12, 3rd line: Figure 1.2 refers to the PMF, not the CDF.
p. 26: In the 3rd line of the equation for Ecfw_[X E(X)]2 , E[(X)]
A Brief Introduction to R
Brennan S. Thompson
Department of Economics, Ryerson University
September 6, 2012
What is R?
I
R is an environment for statistical computing and graphics
I
Like S-PLUS, R is an implementation of the S language, the
de facto stand
An Introduction to Bootstrap Methods
Brennan S. Thompson
Department of Economics, Ryerson University
October 5, 2012
Asymptotic Approximations
I
Let Gn () be the CDF of
n(
n )
Sn =
I
d
Since Sn
N(0, 1) we can write
lim Gn (s) = (s),
n
at all continuity
Intrumental Variables (IV)
Brennan S. Thompson
Department of Economics, Ryerson University
October 8, 2012
Endogeneity
I
I
We now consider relaxing the exogeneity condition
Example: Simultaneous equations:
I
Let:
Yi = Xi + Ui
Xi = Zi + Vi ,
I
I
where Cov(
Ryerson University
ECN 627 Econometrics I: Midterm Exam Sample
Questions
I. Multiple choice questions(1 mark for each question, some
questions have more than 1 correct choices)
1. The random variable X represents the number of children per family
in a rur
Chapter 4
1.
(a) The predicted average test score is
TestScore = 520.4 ! 5.82 " 22 = 392.36
(b) The predicted change in the classroom average test score is
!TestScore = ("5.82 # 19) " ("5.82 # 23) = 23.28
(c) Using the formula for ! 0 in Equation (4.8), w