ECONOMICS 321 (WINTER 2016)
FIRST MID-TERM
SOLUTION
Please write clearly and show all your work. Answers without any explanation will not earn any points.
Good Luck.
Problem 1 (40 Points)
1) Econometrics can be defined as follows with the exception of
A)
ECONOMICS 321 (Winter 2016)
Practice Exam 2
Problem 1 (15 Points)
1) The confidence interval for the sample regression function slope
A) can be used to conduct a test about a hypothesized population regression function
slope.
B) can be used to compare the
All exercises in appendix C and chapters 1 and 2 of the Wooldridge textbook are good practice exercises. Question 1 Suppose that I run an experiment to measure the effect of classroom temperature on final exam scores. I have a total of 300 students and gi
Further Issues Chapter 6
Econ 321 Introduction to Econometrics
Econ 321-Stphanie Lluis
1
Outline
Data scaling More on functional forms More on Goodness of fit More on the effect of adding regressors Interaction effects
Residual analysis
Econ 321-Stphan
Econ 321 Mid-Term February 27th, 2008, 2:30-3:50
Student ID number_
Instructions: *Write your name on top of page 14 or 15. *You can use the other side of the page to complete your answers.
*Problems should be answered as completely as possible. Partial c
Exercises to Help Study for Midterm 1
Part I: Probability
Question 1
Table 1: Joint Distribution of Weather Conditions and Commuting Times
Rain (X=0)
No Rain (X=1)
Total
Long Commute (Y=0) 0.15
0.07
0.22
Short Commute (Y=1) 0.15
0.63
0.78
Total
0.30
0.70
Multiple Regression Analysisa
Multiple Regression Analysis is very similar to Simple Regression
Analysis: the only dierence is that you have additional regressors
in the equation. The models will, hopefully, depict a better picture
of the true model expla
Multiple Regression Analysis: Inferencea
This chapter is about how to determine which regressors should be
included in the regression and how to determine if a regressor has a
statistically signicant (and economically signicant) impact on the
dependent va
Specification and Data
Problems
Chapter 9 (Some parts of)
Outline
1-Missing data
2-Non random samples
3-Functional Form Specification Issues
Review
Misspecification tests
Testing against non-nested alternatives
Specification Criteria:
Hierarchical
Introduction to Time Series
Chapter 10
1
Outline
Definitions and Examples
Finite Distributed Lag Models
Trends and seasonality
Properties of OLS estimators
2
Time Series Data
We have seen: Y -> sample (Y1, Yn)
-> Statistics to estimate either the mean =
The Simple Regression Modela
We use regression analysis to explore the relationship between two
variables, i.e., how y, the dependent variable, varies with x, the
explanatory variable. The linear regression model is a linear
approximation of the relations
Specification and Data
Problems
Chapter 9 (Some parts of)
Outline
1-Missing data
2-Non random samples
3-Functional Form Specification Issues
Review
Misspecification tests
Testing against non-nested alternatives
Specification Criteria:
Hierarchical
Heteroscedasticity
Chapter 8
Econ 321
Introduction to Econometrics
Econ 321-Stphanie Lluis
1
Outline
What is heteroscedasticity
Consequences
Detection (Tests)
Solutions for correcting it
Form of heteroscedasticity is known
Form of heteroscedasticity is
Review of Statistics
Part I
Econ 321
Introduction to
Econometrics
Wooldridge: appendices
Econ 321-Stphanie Lluis
1
Econometrics: Using Data to
Measure Causal Effects
Ideally, we would like an experiment
what would be an experiment to estimate the
effect o
Review of Statistics
Part II
Econ 321
Introduction to
Econometrics
Econ 321-Stphanie Lluis
1
Main Steps in Statistical Analysis
Central Limit Theorem
Y
The sampling distribution of
Other basic distributions
Hypothesis testing
Econ 321-Stphanie Lluis
2
Cen
Review of Statistics
Part III
Econ 321
Introduction to
Econometrics
Wooldridge: appendices
Econ 321-Stphanie Lluis
1
Additional Tools and Issues in
Statistical Analysis
Properties of Estimators
Multivariable analysis
Conditional distribution and condition
Summary Chapter 2
Estimation of a 2 variable relationship
Beta OLS definition and derivation
E( ) and Var( )
Properties of beta:
BLUE
Best linear unbiased estimator
Among the list of all linear unbiased estimators, OLS estimator
has the smallest varian
Multiple Regression
Chapter 3 Part II
Econ 321
Introduction to Econometrics
Econ 321-Stphanie Lluis
1
Outline
Multiple Regression
Interpretation of the Results
Omitting variables
Adding Irrelevant variables
Multicollinearity
Properties of OLS in the
Inference
Chapter 4
Econ 321
Introduction to Econometrics
Econ 321-Stphanie Lluis
1
Outline
Test of single parameter
The F-test for testing multiple joint restrictions
Test of linear combination of parameters
Econ 321-Stphanie Lluis
2
Hypothesis Testin
Chapter 6: Further Issues
(Review of Chapter 3 and +)
Econ 321
Introduction to Econometrics
Econ 321-Stphanie Lluis
1
Outline
More on data scaling
More on functional forms
Residual analysis
Models with Log
More on the effect of adding regressors
Mor
Qualitative Information
Chapter 7
Part II
Econ 321
Introduction to Econometrics
Econ 321-Stphanie Lluis
1
Outline
Interaction effects
F-test for interaction parameters
Chow test
Test for differences between groups/categories
Econ 321-Stphanie Lluis
2
Multiple Regression Analysis: Binary variablesa
Dummy (binary) variables are used to control for observable
qualitative characteristics. Gender is probably the best known
example. One can also dichotomize a continuous variable (e.g.
income can be transfor
Heteroskedasticitya
Homoskedasticity fails when the variance of the errors is not
constant across the entire population. Usually, the variance varies
with one explanatory variable.
As homoskedasticity of the errors is a necessary condition to use F
and t
Assignment #1
ECON 321: Introduction to Econometrics - Winter 2015
Due January 22nd , 2015, in class
Instructions: While cooperating on the assignment is encouraged, plagiarism is not. I will only accept hand written assignment submitted in
person. DO NOT
Economics 321: Intro To Econometrics
Test 1
Wednesday January 29th, 2014
Instructor: Emmanuelle Pierard
*
Name:
*
Instructions:
1. Answer the questions on this paper. All explanations should be brief and
precise (use point form if you wish).
2. In order t