Econ 399 A1/A3 Topics covered by date
Date
September 1
September 3
September 8
September 10
September 15
September 17
September 22
Topics Covered
Course outline, course aims, “What is
Econometrics?”, examined class example data
sets, discussed basic ‘basketball ticket price’
and ‘commodity price’ models.
setting up a model for econometrics
(dependent variable, explanatory variables,
coefficients, random error term);
review of functional forms for econometric
models (including slopes and elasticities);
discussion of preliminary basketball model;
introduction to using Stata for graphs,
summary statistics and correlations.
Estimation Methods
- “Fitline” example with 10 data points
- Derivation of the OLS estimator for a model
with only one explanatory variable
Using OLS for models with no X’s, one X,
multiple X’s
Using Stata’s regress comand for OLS with
multiple X’s, using Stata’s predict command to
find “y-hat” and “u-hat”
- Algebraic Properties of OLS
- Finding Total, Explained (Model) and
Residual Sum of Squares in Stata
- Derivation and interpretation of R2 and
adjusted-R2
- Introduction to statistical properties of OLS:
random and non-random components of Y;
assumptions about u
- statistical properties of u, Y and OLS
estimators
̂1 ) in a model with only one explanatory
- (E(𝛽
variable;
- unbiased estimators
̂1 ) : formula developed for simple case
-- Var(𝛽
of only one explanatory variable
- Gauss Markov Theorem
- omitted variable bias
- adding the assumption of normality for the
error term
- t-tests
Readings
Chapter 1 of Textbook
Chapter 1 of Textbook;
functional form handout from
web page; First Stata example
from webpage.
Fitline handout from web
page;
-Chapter 2.1, 2.2, Appendix
2A in Textbook
Chapter 2.3, 3.1, 3.2 in
Textbook
First Stata example from
webpage
- Chapter 2.3, 3.1, 3.2, 3.3 in
Textbook
- Class handout: Review of
summation, expectation and
variance operators
First Stata example from
webpage
- Chapter 2.5, 3.3, 3.4, 3.5
- Chapter 4.1, 4.2
- illustrations from Stata
examples 1 and 2
September 24
September 29
October 1
October 6
- review of t-test procedure
- using the lincom command in Stata for t-tests
- interpreting p-values
- confidence intervals: formula and how to
calculate in Stata
- introduction to F tests: restricted and
unrestricted models
- F test of overall significance
- proof that R-square is zero when there are
no X’s in the model
- F tests continued
- the relationship between t and F tests
- F tests for whether or not a subset of X’s
belong in the model
- An introduction to dummy (binary) variables
- Allowing for different intercepts across 2
groups
October 8
- F tests for seasonal effects
- Chow test (with and without the use of
dummy variables)
October 13
- F tests for seasonal effects (same results
regardless of “base” period or drop intercept
and put in all 4 quarterly dummies)
- Reset test for functional form
- Using a dummy variable for your “Y”: OLS
- Detailed answers to Part B of Assignment #1
including review of determining whether or
not an estimator is unbiased, deriving the OLS
estimator for various models, properties of
Cobb-Douglas production functions; overview
of Part A answers
- Discussed sample mid-term
- Discussed answers to Assignment #2
- Assignment # 1 returned
MIDTERM
- Writing Across the Curriculum Presentation
(Daniel Harvey)
- Review of using OLS with a dummy variable Y
(see Oct. 13)
- Measuring prediction success with a dummy
variable Y
- Probit and Logit as alternatives to OLS
October 15
October 20
October 22
October 27
October 29
- Chapter 4.2, 4.3, 4.4
- Stata example 2
- Chapter 4.5
- Stata example 2
- Chapter 4.5
- Stata example 2
- Chapter 7.1, 7,2
- Stata example 3
- Excel version of example 3
data
- Chapter 7.3, 7,4, Chapter
10.5 (see sub-section on
seasonality)
- Stata example 3
- Stata example 6
- Stata example 6
- Chapter 9.1
- Chapter 7.5
- Part A answers are posted
on website;
- If you missed class, you will
need to get the Part B
answers from a classmate
- sample midterm on website
- Assignment 2 answer key on
website
- link to presentation slides
has been posted on course
web page
- Stata example 4
- Chapter 17.1
November 3
November 26
- Midterm returned.
(Answers will not be posted. If you missed
class you will should get the answers from a
classmate.)
- Heteroskedasticity (definition, consequence,
intro to testing strategies)
-Weighted Least Squares
- Heteroskedasticity: how do we detect it
(what tests can we use)? (White and BP tests)
- Heteroskedasticity: correcting the standard
errors, t-tests and F test (using the vce(robust)
option in Stata)
- Autocorrelation: Introduction: what is it?
what are the consequences?
- Rewriting the model (quasi-differencing) to
obtain a new equation with no autocorrelation
- Autocorrelation tests (BG and DurbinWatson)
- Remedies for autocorrelation (using Stata’s
PRAIS and NEWEY commands)
- Empirical Report Workshop
December 1
- Dynamic Models
December 3
- Multicollinearity and VIFs
November 5
November 17
November 19
November 24
- Chapter 8.1, 8.3, 8.4
- Chapter 8.2, 8.3
- Example 7
Chapter 12.1, 12.3
- Chapter 12.2, 12.3
- Example 8
- Sample Template posted on
class website
Chapter 9.2 (crime rate lagged
dependent variable example),
10.2;
- Example 9
Chapter 3.4, 4.2
- Example 9