Introduction to Econometrics
Professor Tara Sinclair
FINAL EXAM
SUGGESTED SOLUTIONS
December, 2014
Important Information:
Please remember to put your name on the top of this page.
You have 120 minutes to complete the exam, so budget your time accordingl
Introduction to Econometrics
Professor Tara Sinclair
FINAL EXAM SOLUTIONS
December 12, 2013
IMPORTANT INFORMATION
Please remember to put your name on the top of this page.
This exam consists of 5 parts with a total value of 100 points.
The particular p
Name _
Introduction to Econometrics
Professor Tara Sinclair
FINAL EXAM
December 11, 2014
Important Information:
Please remember to put your name on the top of this page.
You have 120 minutes to complete the exam, so budget your time accordingly.
This e
Name: _
Introduction to Econometrics
Professor Tara Sinclair
FINAL EXAM
December 12, 2013
IMPORTANT INFORMATION
Please remember to put your name on the top of this page.
This exam consists of 5 parts with a total value of 100 points.
The particular poi
Introduction to Econometrics
Professor Tara Sinclair
FINAL EXAM VERSION 1
December 15, 2011
This exam consists of 5 parts with a total value of 100 points (partial credit will be awarded for
correct work shown in parts 2 through 5). Note that the differen
Introduction to Econometrics
Professor Tara Sinclair
FINAL EXAM VERSION 1
SUGGESTED SOLUTIONS
December 15, 2011
This exam consists of 5 parts with a total value of 100 points (partial credit will be awarded for
correct work shown in parts 2 through 5). No
Spring 2016
Ethics case studies
This is an extra credit assignment. In this booklet, you will find 38 separate case studies. You
are free to respond to any or all of these cases.
You may earn up to 5 extra credit points per question, based on the complexi
W k 11
Week
Decision Sciences 274
Modeling and Analysis
Using Statistical Computing Packages
Philip W. Wirtz
The George Washington University
Page 240
Forecasting
The process of making statements about events whose actual
outcomes (typically) have not yet
W k 10
Week
Decision Sciences 274
Modeling and Analysis
Using Statistical Computing Packages
Philip W. Wirtz
The George Washington University
Extending Interaction to
More Than Two Groups
Context:
180 alcoholics presenting for treatment were randomly
ass
Week 13
Management Science 225
Modeling and Analysis
Using Statistical Computing Packages
Philip W. Wirtz
The George Washington University
Cohens Article
The problem that Cohen is citing is that when we "reject the null hypothesis", we
are doing so becaus
W k 12
Week
Decision Sciences 274
Modeling and Analysis
Using Statistical Computing Packages
Philip W. Wirtz
The George Washington University
Statistical Power:
Single Sample t-Test
t Test
Distribution of
t when =0
Distribution of
t when =true
H0: =0
HA:
A Few Definitions
Hypothesis: Statement about relationship between variables
that is specific and exact.
Variable: Characteristic, trait, or property that can change values.
a) Independent: Cause. Called X.
b) Dependent: Effect. Called Y.
Alternative/Rese
How to Operationalize the Following Concepts?
Educational attainment for Head Start participants.
Answer: Achievement scores on Iowa Test of Basic Skills.
Efficiency of fund-raising firm.
Answer: Money raised divided by costs paid by firm.
Whether convict
Sampling Terminology
Terms
Population: Total set of units you are interested in.
Sample: Subset of population selected.
Parameter: Characteristic of entire population (typically unknown).
Statistic: Characteristic of the sample (typically known).
Example
The Islamic University of Gaza
Faculty of Commerce
Department of Economics & Applied Statistics
Course: Time Series Analysis Dr. Samir Safi
Date: 18/4/2010
Midterm Examination
Question #1:
Describe the important characteristics of the autocorrelation func
April, 25, 2008. Exam 2 for S156: Applied time series analysis
Name:
1. (a) The following AR(2) model was fitted to a time series of size 30. Explain why the fitted
model is stationary.
ar1 ar2 intercept
Estimate 0.51 0.07
0.47
SE 0.18 0.18
0.29
(b) Const
May, 16, 2002. Final Exam for S156: Applied time series analysis
Name:
Below, cfw_at denotes a sequence of iid random variables with zero mean and finite variance a2 > 0.
1. Let Z1 , Z2 , , Z100 be an MA(2) process: Zt = (1 + 0.5B 0.2B 2 )at . Listed bel
Apr., 2, 2001. Exam 2 for S156: Applied time series analysis
Name:
Below, cfw_at denotes a sequence of white noise with zero mean and finite variance a2 = 1.
1. For each of the following ARIMA(p,d,q) model, what are the values of p,d and q. Furthermore,
March, 4, 2008. Exam 1 for S156: Applied time series analysis
Name:
1. In this question, we consider an annual series of hare abundance from 1905 to 1935. The data
are square root transformed.
10
(a) The data appear to be cyclical. Visually estimate the p
Final Examination for S156: Applied Time Series Analysis, Spring,94.
Name:
Instructions: This is a CLOSED book exam but you are allowed to have a sheet of paper with formulas,
definitions,.,etc., written on both sides. Examination time is two hours. There
Feb., 25, 2002. Exam 1 for S156: Applied time series analysis
Name:
Below, cfw_at denotes a sequence of iid random variables with zero mean and finite variance a2 = 1.
1. Let (Zt )t=0,1,2, be a stationary time series with zero mean, variance equal to 2 a
March, 13, 1995. Exam 2 for S156: Applied time series analysis
Name:
1. State whether each of the following model is stationary and/or invertible. Explain your answer briefly. (It suffices to verify the conditions for stationarity and invertibility for th
Solo'lToqs 4w— Ezww Q
Statistics 6201: Fall 2015: Test 2 Statistics 6201: Name
University ID
When taking this quiz i agree to follow the Honor Code of the George Washington University
(signature)
1.(15) Let U be a Uniform random variable on [0,1]. Fin
:
1: 10
2: 10
3: 10
4: 10
6: 10
7: 10
8: 10
9: 10
: 100
5: 10
10: 10
1. Let X and Y be random variables with nite means. Find a function g (X) such
that
min E (Y g(X)2 = E (Y g (X)2 ,
g(X)
where g(x) ranges over all functions.
:
E[Y g(X)]2 = E[(Y E[Y |X]
Statistics 6201: Fall 2015
Homework 2
From the problems at the end of Chapter 1 in the textbook:
Problems: 1.26, 1.27, 1.33, 1.38, 1.39 and 1.41
Additional Problems:
1. A single card is randomly selected from a standard deck of 52 cards. From the remainin