EC 320 Midterm Practice Problems
Practice Problem 1: Consider a discrete random variable that takes a value of 8 with
probability 0.75 and a value of -4 with probability 0.25. Calculate the expected value of
this random variable and its variance.
Practice
Introduction to Regression Analysis
EC 320
Professor Jeremy Piger
1/35
Introduction to Regression Analysis
Readings: Dougherty Chapter 1
Regression Analysis
Simple Linear Regression Model
Estimating the Parameters of the Linear Regression Model
Ordina
Homework 6
EC 320
Professor Piger
Fall 2014
Due: Wednesday, November 19
Analytical Exercises
1) Consider the following multiple linear regression model:
Yi = 1 + 2 X 2i + 3 X 3i + 4 X 4i + 5 X 5i + ui
(Equation 1)
You want to test the null hypothesis that
Homework 2
EC 320
Professor Piger
Fall 2014
Due: Wednesday, October 15 (in class)
Analytical Exercises
1) Consider a sample of size n = 3 on a dependent variable Y and an independent
variable X:
Observation Number
Value of Y
Value of X
1
4
-4
2
4
2
3
7
2
Midterm Solutions (GREEN)
EC 320
Professor Mastromonaco
Spring 2016
NAME:_
Instructions:
1. Answer all questions below.
2. Write your answers in the space provided.
3. Statistical tables are included at the end of the exam.
4. There are 100 points possibl
Name:
By writing my name I. swear not to give or receive aid on this exam
Midterm Exam.
You~ may only use a nomprogrammable calculator, though you probably do not need one.
Cell phones may not be used for any reason. Please explain your answers brie
Homework 5
EC 320
Professor Piger
Fall 2014
Due: Wednesday, November 12
Analytical Exercises
1) Data was collected from a random sample of 220 home sales from a community in
2003. Let P denote the selling price (in $1000), Bdr denote the number of bedroom
UNIVERSITY OF OREGON
Department of Economics
Introduction to Econometrics
Fall 2014
EC 320, CRN 12030
MW 12:00 a.m. 1:20 a.m. in McKenzie 240A
Instructor: Jeremy Piger
Contact Information: Office: 536 PLC; Email: jpiger@uoregon.edu; Phone: 541-346-6075
Of
Econ 320
Dr. Ralph Mastromonaco
University of Oregon
Spring 2015
SYLLABUS: Introduction to Econometrics
Time M-W 10:00 am 11:20 am
Location 145 Straub
Professor
Ralph Mastromonaco
533 PLC
ralphm@uoregon.edu
Office Hours
Monday & Wednesday 12 1 pm
Course D
Standard Errors
t-tests
Hypothesis Testing 2
Dr. Ralph Mastromonaco
University of Oregon
Hypothesis Testing 2
University of Oregon
Standard Errors
t-tests
Hypothesis Testing
Readings: Dougherty Chapter R & 2
t-tests (R)
Precision of the regression coecien
Homework 2
1. What do the variables EARNINGS and S in EAEF measure? What is the sample mean and
sample standard deviation of these variables? Be sure to include the units of these variables in
your answer
a. Earning means Current hourly earnings in $ repo
Homework 4
1)
H0:20
H1:2<0
Seems like its irrational that having more years of schooling will reduce the income of an individual, so
therefore we only need a one sided hypothesis test.
t= 9.25 tcrit=2.576
reject the null hypothesis at the 1% level.
2)
A)
Condence Intervals
Stata
One-Sided Tests
F -Test
Hypothesis Testing 3
Dr. Ralph Mastromonaco
University of Oregon
Hypothesis Testing 3
University of Oregon
Condence Intervals
Stata
One-Sided Tests
F -Test
Condence Intervals
b1 and b2 are estimates of 1 an
Homework 3
a) b1 = .148535
b2 = 6.1358
R-squared = 0.3262 the goodness of fitting into the data is
0.3262, which is not really good
b) b1 = 2.491499 b2 = -14.15813 R-Squared = 0.1371 the goodness of fitting into the data is
0.1371, which is really bad.
c)
Introduction
Omitted Variable Bias
Examples
Model Specication
Dr. Ralph Mastromonaco
University of Oregon
Model Specication
University of Oregon
Introduction
Omitted Variable Bias
Examples
Regression Model Specication
Readings: Dougherty Chapter 6
Model S
Introduction
Extra Variables
Linear Restrictions
Model Specication
Dr. Ralph Mastromonaco
University of Oregon
Model Specication
University of Oregon
Introduction
Extra Variables
Linear Restrictions
Regression Model Specication
Readings: Dougherty Chapter
Review
Hypothesis Testing
Goodness of Fit
F -test
Multicollinearity
Multiple Linear Regression 2
Dr. Ralph Mastromonaco
University of Oregon
Multiple Linear Regression 2
University of Oregon
Review
Hypothesis Testing
Goodness of Fit
F -test
Multicollinear
OLS Review
Regression Analysis
Beauty
Earnings and Height
Introduction to Regression Analysis
Dr. Ralph Mastromonaco
University of Oregon
Introduction to Regression Analysis
University of Oregon
OLS Review
Regression Analysis
Beauty
Earnings and Height
Si
Introduction
NL in Variables
NL in Parameters
Comparing Linear and Log
Non-linearity
Dr. Ralph Mastromonaco
University of Oregon
Non-linearity
University of Oregon
Introduction
NL in Variables
NL in Parameters
Comparing Linear and Log
Functional Forms and
Classical Assumptions
Random vs. Non Random
Bias and Precision
PDF
Monte Carlo
Statistical Properties of the OLS Estimator
Dr. Ralph Mastromonaco
University of Oregon
Statistical Properties of the OLS Estimator
University of Oregon
Classical Assumptions
R
RVs
Regression & Testing
Econometrics:
Midterm Review
Dr. Ralph Mastromonaco
University of Oregon
Econometrics: Midterm Review
University of Oregon
RVs
Regression & Testing
Review of Class
Readings: Dougherty
Chapter R
Chapter 1
Chapter 2
Econometrics: Mi
Multiple Regression Analysis
Interpretation
Estimation
Frisch-Waugh
Properties
Multiple Linear Regression
Dr. Ralph Mastromonaco
University of Oregon
Multiple Linear Regression
University of Oregon
Multiple Regression Analysis
Interpretation
Estimation
Fr
Dummy Variables
Dr. Ralph Mastromonaco
University of Oregon
Dummy Variables
University of Oregon
Dummy Variables
Readings: Dougherty Chapter 5
Quantitative vs. Qualitative Information
Uses of Dummy Variables
Dummy Variables with Multiple Categories
The Du
Regression Analysis
Simple LRM
Estimation
OLS
Introduction to Regression Analysis
Dr. Ralph Mastromonaco
University of Oregon
Introduction to Regression Analysis
University of Oregon
Regression Analysis
Simple LRM
Estimation
OLS
Introduction to Regression
Introduction
Interactive Variables
Nonlinear Regression
Non-linearity 2
Dr. Ralph Mastromonaco
University of Oregon
Non-linearity 2
University of Oregon
Introduction
Interactive Variables
Nonlinear Regression
Functional Forms and Nonlinear Regression
Read
Homework 6
1)
a. I think EXP is better than PWE because in terms of P value, when I run regression in
earning, schooling years and experience, the p value is 0. But when I run regression in
earning, schooling years and potential work experience, the p val
Homework 7
1)
a. The reference group is MALE and ETHWHITE
b. Once the female is selected, the Earning will increase(-.3546829*100)%=-35.4%
c. We run the regression again using EXP and FEMALE as the variable. We get the P value
on EXP = 0 which is lower th
University of Oregon
Department of Economics
October 25, 2016
Rosie Mueller
Fall 2016
Midterm - Version A - SOLUTIONS
EC 320: Econometrics
Instructions:
1. Write your name and Duck ID on the front cover of this examination, and indicate
your lab day and t
University of Oregon
Department of Economics
Rosie Mueller
Fall 2016
Quiz 1 - Version A - SOLUTIONS
EC 320: Econometrics
Instructions:
1. Write your answers neatly in the space provided.
2. Make sure to show ALL of your work.
3. You will be allowed a simp
EC 320
Instructor: Rosie Mueller
Fall 2016
Final Practice Problems - SOLUTIONS
Note: These practice problems are taken from material covered since the midterm
only. Remember to also look at all the problems from the Homeworks, Quiz 1 and 2,
and the midter
University of Oregon
Department of Economics
Rosie Mueller
Fall 2016
Quiz 2 - Version A - SOLUTIONS
EC 320: Econometrics
Instructions:
1. Write your answers neatly in the space provided.
2. Make sure to show ALL of your work.
3. You will be allowed a simp
University of Oregon
Department of Economics
Rosie Mueller
Fall 2016
Homework 7 - SOLUTIONS
EC 320: Econometrics
Due in Lab Week of 11/28
Reminder: There is no lab the week of 11/21
Please type or write neatly your answers to the following questions on a