This course focuses on:
Quantitative Questions with Quantitative
Answers
Business and Economics suggest interesting
relations (stories), often with (important) policy
implications, but it would be nice to quantify the
magnitudes of causal eects.
4
3
Stock
ECON2P91: Business Econometrics with Applications
Mid-term Examination
NAME:_
STUDENT NO: _
Instructions: Answer all questions from Sections A and B
The Standard Normal Distribution table is provided
THIS BOX IS FOR MARKERS ONLY:
Section A _/40
Section B
1ECON2P91: Review Questions No. 1
1. Complete the following formulas (read text)
SE ( Y ) = . 90% confidence interval for the mean: Y . 95% confidence interval for the mean: Y . 99% confidence interval for the mean: Y .
Standard error of the mean:
t=
th
ECON 2P91: Business Econometrics with Applications Winter 2011 Lab4 SOLUTIONS 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 elasticity of d
ECON2P91: Business Econometrics with Applications
Mid-term Examination
NAME:_
STUDENT NO: _
Instructions: Answer all questions from Sections A and B
The Standard Normal Distribution table is provided
THIS BOX IS FOR MARKERS ONLY:
Section A _/40
Section B
ECON 2P91: Business Econometrics with Applications Fall 2010 Assignment 3 (Due Date: Friday November 26th 2010) - SOLUTIONS In assignments1 and 2, you investigated the relationship between residential housing price and lot size by constructing hedonic pri
Review Questions: Chapter 10 Regression with Panel Data Multiple Choice 1) The notation for panel data is ( X it , Yit ), i = 1,., n and t = 1,., T because a. we take into account that the entities included in the panel change over time and are replaced b
Review Questions: Chapter 10 Regression with Panel Data Multiple Choice 1) The notation for panel data is ( X it , Yit ), i = 1,., n and t = 1,., T because a. we take into account that the entities included in the panel change over time and are replaced b
Review Questions: Chapters 4 and 5 Linear Regression with One Regressor
1)
The regression R 2 is defined as follows: ESS a. TSS RSS b. TSS
(Y Y )( X
c. SSR d. n2 2)
a. b. c. d.
i =1 i n 2 n i =1 i i =1
n
i
X)
i
(Y Y ) ( X
X )2
Which of the following s
REGRESSIONS WITH PANEL DATA
Read (Stock and Watson, Chapter 10)
Panel data notes (see additional notes folder
on Sakai)
TYPES OF ECONOMETRIC
DATA SETS
Cross Section Data: Relate to a Single Point in
Time e.g. number of traffic fatalities in 10
Canadian p
INTRODUCTION TO TIME SERIES
REGRESSION AND FORECASTING
Read (Stock and Watson, Chapter 14)
n Time series notes (see additional notes
folder on Sakai)
n
WHAT IS A TIME SERIES MODEL?
n
We regress current values of a particular
variable (e.g., inflation) on
NONLINEAR REGRESSION FUNCTIONS
n
n
n
n
Read (Stock and Watson, Chapter 8)
Linear regression can be used to capture nonlinear
relationships
In such cases, the interpretation of the slope
coefficients changes slightly
We shall consider the following cases:
REGRESSION
WITH SINGLE REGRESSOR
Simple linear regression
n Stock and Watson (Chapter 5)
n
HYPOTHESIS TESTING:STEP 1
Specify null hypothesis and alternative hypothesis
H0: the null hypothesis
H1: the alternative Hypothesis
Choose one of the three
HYPOTHES
MULTIPLE REGRESSION
Read (Chapters 6 and 7)
n Has two or more regressors (independent
variables)
n May solve the omitted variable bias
problem
n
SPECIFICATION ERROR
(1) Omission of relevant independent
variables (Hence omitted variable bias)
n (2) Inclusi
MULTIPLE REGRESSION
Read (Chapters 6 and 7)
Has two or more regressors (independent
variables)
May solve the omitted variable bias
problem
SPECIFICATION ERROR
(1) Omission of relevant independent
variables (Hence omitted variable bias)
(2) Inclusion of
REGRESSIONS WITH PANEL DATA
Read (Stock and Watson, Chapter 10)
Panel data notes (see additional notes folder
on Sakai)
WHAT IS PANEL DATA?
Panel data is a combination of cross section
and time series data
Panel data is also called longitudinal data
EXA
MULTIPLE REGRESSION
Read (Chapters 6 and 7)
Has two or more regressors (independent
variables)
May solve the omitted variable bias
problem
SPECIFICATION ERROR
(1) Omission of relevant independent
variables (Hence omitted variable bias)
(2) Inclusion of
Chapter3ReviewofStatistics
3.1EstimatorsandEstimates
Anestimatorisafunctionofsampledatatobedrawnrandomlyfromapopulation
o Isarandomvariablebecauseoftherandomnessinselectingthesample.
o Anestimatorshouldbeunbiased,consistent,andefficient.
Anestimateisthenu
Chapter1:EconomicQuestionsandData
o Broaddefinition:Econometricsisthescienceandartofusingeconomictheoryand
statisticaltechniquestoanalyzeeconomicdata.
o Economicsusedineconomics,micro/macroeconomics,marketing,andeconomicpolicy
o Alsoinsocialsciencessuchas
LINEAR REGRESSION: ONE
REGRESSOR
Simple linear regression
Stock and Watson (Chapters 4 and 5)
LINEAR REGRESSION
More informative than correlation or covariance.
Recall a positive covariance/correlation indicates a
positive relationship; a negative
covari
NONLINEAR REGRESSION
FUNCTIONS
Read (Stock and Watson, Chapter 8)
Linear regression can be used to capture nonlinear
relationships
In such cases, the interpretation of the slope
coefficients changes slightly
We shall consider the following cases: (1)
Poly
BUSINESS ECONOMETRICS
WITH APPLICATIONS
Stock and Watson (Chapters 1-3)
n Introduction and Review of Probability and
Statistics
n
ECON2P91: BUSINESS
ECONOMETRICS WITH
BUSINESS ECONOMETRICS
n
Course focuses on QUANTITATIVE
QUESTIONS WITH QUANTITATIVE
ANSWE
LINEAR REGRESSION:
ONE REGRESSOR
Simple linear regression
n Stock and Watson (Chapter 4)
n
LINEAR REGRESSION
n
n
n
More informative than correlation or covariance.
Recall a positive covariance/correlation indicates a
positive relationship; a negative
cova
ECON 2P91: Business Econometrics with Applications
Fall 2011
Assignment 1 SOLUTIONS (Due Date: Friday October 14th 2011)
Wine is a highly differentiated product. For example, Jackson-Triggs Vintners, a large Canadian
wine producer, produces 12 different w
1ECON2P91: Review Questions No. 1
1.
Complete the following formulas
Standard error of the mean:
SE ( Y ) = .
90% confidence interval for the mean: Y .
95% confidence interval for the mean: Y .
99% confidence interval for the mean: Y .
t=
(Y
. . . . . .
)
GTA Corporation Case
John Emerson - 5618400
Aayushi Prajapati- 5640230
Case: Data Access and Facility Controls for Information Technology
The GTA Corporation has recently experienced a significant growth and considers upgrading it
information system (IS)
BROCK UNIVERSITY
Final Examination: April, 2015
Course: Earth Sciences ERSC 1F01
Date of Examination: 9th April, 2015
Time of Examination: 9.00am- 12.00pm
\
'
Examination Time: 3 Hours
Instructor: Dr. J. Menzies
1.
Answer ALL questions in PART A and PART
ERSC 1P92 Extreme Earth
Assignment 3. Example questions of material covered for the final exam.
Note that some of these questions will likely turn up on the final exam.
Important: Answer all questions on the SCANTRON sheet provided in class beginning Nove