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 d
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.
Mt1 preparation
Review the concepts
ch2: 2.1, 2.2, 2.3, 2.4, 2.6
ch3: 3.1, 3.3, 3.4, 3.8
ch4: 4.1, 4.2, 4.3
Multiple choice
ch2
1) The sample average is a random variable and
a. is a single number and
San Francisco State University
Department of Economics
Econ 312/Spring 2010
Instructor: Sang-Yeob Lee
Econ 312: Midterm I
Thursday, March 18
Please do not turn this page over until instructed to do so
Chapter 12: Instrumental Variables Regression
Multiple Choice
1)
The rule-of-thumb for checking for weak instruments is as follows: for the case of a
single endogenous regressor,
a.
b.
c.
d.
a first s
Chapter 9: Assessing Studies Based on Multiple Regression
Multiple Choice for the Web
1)
A survey of earnings contains an unusually high fraction of individuals who state their
weekly earnings in 100s
Chapter 8: Nonlinear Regression Functions
Multiple Choice for the Web
1)
The interpretation of the slope coefficient in the model ln(Yi ) 0 1 ln( X i ) ui is as
follows: a
a.
b.
c.
d.
2)
1% change in
Chapter 6: Linear Regression with Multiple Regressors
Multiple Choice for the Web
1)
In the multiple regression model, the adjusted R2, R 2
a.
b.
c.
d.
2)
cannot be negative.
will never be greater tha
Chapter 5: Regression with a Single Regressor: Hypothesis Tests and Confidence
Intervals
Multiple Choice for the Web
1)
The t-statistic is calculated by dividing
a. the OLS estimator by its standard e
Chapter 4: Linear Regression with One Regressor
Multiple Choice for the Web
1)
Binary variables
a.
b.
c.
d.
are generally used to control for outliers in your sample.
can take on more than two values.
Chapter 3: Review of Statistics
Multiple Choice for the Web
1)
An estimator Y of the population value Y is consistent if
p
a. Y Y .
b. its mean square error is the smallest possible.
c. Y is normally
Chapter 2: Review of Probability
Multiple Choice for Web
1)
The expected value of a discrete random variable
a.
b.
c.
d.
2)
For a normal distribution, the skewness and kurtosis measures are as follows
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
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
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 pr
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 d
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 distributi
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
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 t
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 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,
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 equat
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
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 t
3cm2cm1cm4cm
Practice Final
1.
(5 points for each question) Explain briey why you
agree or disagree with the following statements.
(a) We can use ordinary least squares to estimate
the regression equa
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
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:
Off