ECON2P91: BUSINESS ECONOMETRICS WITH APPLICATIONS
FALL 2015
QUESTIONS FROM THE TEXTBOOK TO BE COMPLETED
SOLUTIONS
The following carefully selected list of questions from the textbook must be completed at
the very minimum in order to do well in this course

ECON2P91: Business Econometrics with Applications
Date: Saturday November 9, 2013 1:00-2:30 pm.
Section A: Multiple Choice [60 points; 2 points each]
1. Which of the following statements is correct?
(Note that: ESS=Explained Sum of Squares, SSR=Sum of Squ

Chapter1 EconomicQuestionsandData
1.1 MultipleChoice
1) AnalyzingthebehaviorofunemploymentratesacrossU.S.statesinMarchof2006isanexampleofusing
A) timeseriesdata.
B) paneldata.
C) cross-sectionaldata.
D) experimentaldata.
Answer: C
2) Studyinginflationinth

Assignment 1
JACQUELYN NG 4984555
ECON 2P91
Question 1.1
In this Time serious plot, the red line represents unemployment rate less than high school
diploma 25 years and over; the blue line represents unemployment rate with high school diploma
25 years and

ECON 2P91: Business Econometrics with Applications
Fall 2015
Lab7
Week of November 23, 2015
The objective of this weeks labs is to demonstrate the estimation of panel regressions
using both pooled OLS and Least Squares Dummy Variables.
What is the price e

ECON 2P91: Business Econometrics with Applications
Fall 2015 Lab5
Week of November 2, 2015
The objective of this weeks lab is to demonstrate OLS estimation and hypothesis testing in
linear regressions with two or more regressors, which are called multiple

ECON 2P91: Business Econometrics with Applications
Fall 2015 Lab6
Week of November 9, 2015
The objective of this weeks lab is to demonstrate the application of polynomial regressions in
econometrics. As mentioned in class, polynomial regressions, which in

ECON 2P91: Business Econometrics with Applications
Fall 2015
Lab3
Week of October 5, 2015
The objective of this lab is to demonstrate the use of GRETL to transform variables and
use the transformed variables to estimate the parameters of a linear regressi

ECON 2P91: Business Econometrics with Applications
Fall 2015
Lab2 (Week of September 28, 2015)
The purpose of this lab is to provide an overview of the properties of the normal distribution and
some of the applications of the normal distribution in econom

Documentation for CPS04 Data
Each month the Bureau of Labor Statistics in the U.S. Department of Labor
conducts the Current Population Survey (CPS), which provides data on labor force
characteristics of the population, including the level of employment, u

ECON 2P91: Business Econometrics with Applications
Fall 2015
Lab8
th
Week of November 30 , 2015
The objective of this weeks labs is to demonstrate the estimation of time series models.
As mentioned in class, a (univariate) time series model relates a part

Review Questions: Chapter 8
Nonlinear Regression Functions
Multiple Choice
1)
The interpretation of the slope coefficient in the model ln(Yi ) 0 1 ln( X i ) ui
is as follows:
.a a 1% change in X is associated with a 1 % change in Y.
.b a change in X by on

SAMPLING DISTRIBUTION (REFERENCE: TEXTBOOK CHAPTER 3)
Population: a collection of all units of interest in a study (e.g. all students in a ECON2P91)
Sample: part of the population (e.g. 20 students from ECON2P91 class-assumed to be random)
Suppose that we

THE NORMAL DISTRIBUTION and SAMPLING DISTRIBUTIONS
In Week 3, the focus is on two inter-related topics in statistics and econometrics, namely the
normal distribution and sampling distributions.
THE NORMAL DISTRIBUTION
The normal distribution is popular fo

Appendix
The Cumulative Standard Normal Distribution Function, (z) = Pr(Z " z)
TABLE 1
Area = Pr(Z < z)
0
z
Second Decimal Value of z
z
0
2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2
2.1
2.0
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.0019
0.0026
0.0035
0.0047
0.0

Simple linear regression model
i=1,2,n
y i 0 1 xi u i
i denotes the i-th observation
n is the number of observation
x is the independent variable (regressor)
y is the dependent variable (regressand)
u is the error term (captures variables other than x tha

Multiple Regression
Specification of a multiple regression model:
Model: yi 0 1x1i 2x 2i . k xki ui
Note: a total of (k+1) parameters needs to be estimated; hence, the number of degrees of
freedom is n-(k+1).
Interpretation of Coefficients:
Model: yi 0 1x

Y 1.96 SE( Y ) This is my first equation that I did myself.
act
t =
Y y ,0
2
sy
( )
n
=
y , 0
y
sy
n
This is the second equation that I did mostly myself. I only needed help
with the s subscript y squared.
x i X
()
n
i=1
A blah de blah.

Review Questions: Chapters 4 and 5
Linear Regression with One Regressor
1)
The regression R 2 is defined as follows:
ESS
a.
(correct answer)
TSS
RSS
b.
TSS
n
c.
(Yi - Y )( X i - X )
i=
1
n
n
(
(
Y - Y ) X
2
i
i=
1
i
- X )2
i=
1
SSR
d.
n- 2
2)
Which of the

Review Questions: Chapters 6 and 7
Linear Regression with Multiple Regressors
1) In the multiple regression model, the adjusted R2, R 2
.a cannot be negative.
.b will never be greater than the regression R2.
.c equals the square of the correlation coeffic

Review Questions: Chapter 8
Nonlinear Regression Functions
Multiple Choice
1) The interpretation of the slope coefficient in the model ln(Yi ) 0 1 ln( X i ) ui
is as follows:
.a a 1% change in X is associated with a 1 % change in Y.
.b a change in X by on

Econometrics
Streamlined, Applied and e-Aware
Francis X. Diebold
University of Pennsylvania
Edition 2016
Version Thursday 17th March, 2016
Econometrics
Econometrics
Streamlined, Applied and e-Aware
Francis X. Diebold
c 2013-2016
Copyright
by Francis X. D