Columbia University
Department of Economics
Fall 2010
ECON W3412, Section 1
Introduction to Econometrics
Professor: Seyhan Arkonac, PhD [email protected]
Office hours: Tues and Thurs 11:00am - 12:00pm
Office: 1002A IAB
Class meets on Tuesdays and Thursd
Review for Final Exam
Prof. Seyhan Erden Arkonac
Final Exam is CUMULATIVE
This review will cover those chapters after the
Midterm Exam only.
(i.e. Chapters 10, 11, 12, 13.1, 13.2, 14.1, 14.2, 14.3, 14.4,
15, 16.1, 16.2)
For earlier chapters please refer b
Time Series Regression II
(Fall 2010)
Lecture 23
Seyhan Erden Arkonac, PhD
Solution to Problem Set 8 is posted
Problem Set 9 is due at the
beginning of class on Thursday
December 9th.
1
Autoregressions
(SW Section 14.3)
A natural starting point for a fore
Regression with a Binary
Dependent Variable II
(cont) & III
(Fall 2010)
Lecture 18
Prof: Seyhan Erden Arkonac, PhD
Solutions to Problem Set 6 is posted.
1
Logit Regression
Logit regression models the probability of Y=1 as the
cumulative standard logistic
Instrumental Variable
Regression I
(Fall 2010)
Lecture 19
Seyhan Erden Arkonac, PhD
Problem Set 7 is due NOW!
Solutions will be posted tomorrow.
1
Instrumental Variables Regression
(SW Chapter 12)
Three important threats to internal validity are:
omitted
Instrumental Variable
Regression II
(Fall 2010)
Lecture 20
Seyhan Erden Arkonac, PhD
Problem Set 8 is posted, it is due on
Tuesday, November 23rd
1
The General IV Regression Model
(SW Section 12.2)
So far we have considered IV regression with a single
end
Time Series Regression I
(Fall 2010)
Lecture 22
Seyhan Erden Arkonac, PhD
Problem Set 9 is (last one) is
posted.
th
It is due on Tuesday, Dec. 7 .
1
Introduction to Time Series
Regression and Forecasting
(SW Chapter 14)
Time series data are data collected
Panel Data
Lectures 15 & 16
Seyhan Erden, PhD
Columbia University
PS#6 is posted. It is due on
Nov. 17th for TR section
Nov. 18th for MW section
1
Regression with Panel Data
A panel dataset contains observations on multiple
entities (individuals), where e
Experiments and Quasi
Experiments
Lecture 22
Seyhan Erden, PhD
Columbia University
Problem Set 8 is posted.
Review for Final Exam is on Dec 15th at 2pm
301 Pupin
1
Experiments and QuasiExperiments
Why study experiments?
Ideal randomized controlled experi
Time Series I
Lecture 23
Seyhan Erden, PhD
Columbia University
Problem Set 8 is due on
Dec. 8th for TR sec.
Dec. 9th for MW sec.
1
Introduction to Time Series
Regression and Forecasting
Time series data are data collected on the same observational
unit at
Time Series
Regression II and III
Lecture 24
Seyhan Erden, PhD
Columbia University
Review for Final Exam is on Dec 15th at
2pm 301 Pupin
1
Estimation of Dynamic Causal
Effects
A dynamic causal effect is the effect on Y of a change in X over
time.
For exam
Linear Regression with One
Regressor
Fall 2015
Lecture 4
Seyhan Erden, PhD
Problem Set 1 is due at the beginning of the class
on Tues Sept 22nd for TR section
on Wed Sept 23rd for MW section
1
Recitation times:
Thursday 8:45-10:00pm ENG by MeeRoo
Friday
Regression with a single
Regressor
(Bivariate Regression III)
Lecture 5 and 6
Seyhan Erden, PhD
Problem Set 1 is due NOW!
Solutions will be posted Wed night
at 9pm.
1
The Least Squares Assumptions
What, in a precise sense, are the properties of the
OLS es
Multiple Regression I and II
(Heteroskedasticity,
Multicollinearity)
(Fall 2015)
Lecture 7 and 8
Seyhan Erden, PhD
Problem Set 2 is due NOW.
1
Some Additional Theoretical
Foundations of OLS
We have already learned a very great deal about
OLS: OLS is unbia
Introduction to Econometrics
Third Edition
James H. Stock
Mark W. Watson
The statistical analysis of economic (and related) data
1/2/3-1
1/2/3-2
Brief Overview of the Course
Economics suggests important relationships, often with policy
implications, but v
Multiple Regression III and
Nonlinear Regression and
Assesment
Lectures 9 through 12
Seyhan Erden, PhD
Columbia University
1
Testing Single Restrictions on
Multiple Coefficients
Yi = 0 + 1X1i + 2X2i + ui, i = 1,n
Consider the null and alternative hypothes
Regression with a Binary
Dependent Variable I & II
(Fall 2010)
Lecture 17
Prof: Seyhan Erden Arkonac, PhD
Problem Set 6 is due NOW!
Solutions will be posted tomorrow
evening.
1
. list year state vfrall beertax y83
+-+
| year
state
vfrall
beertax
y83 |
|-|
Panel Data I & II
(Fall 2010)
Lecture 15
Prof: Seyhan Erden Arkonac, PhD
1
Regression with Panel Data
A panel dataset contains observations on multiple entities
(individuals), where each entity is observed at two or more
points in time.
Hypothetical examp
Nonlinear Regression Functions
Lecture 13
Prof: Seyhan Erden Arkonac, PhD
Problem Set 5 is due NOW!
Solutions will be posted tomorrow evening.
Midterm Exam is on Tuesday Oct. 26th
Exam from last semester is posted as practice
exam.
1
A Framework for Asses
Time Series Regression IV
Lecture 24
Seyhan Erden Arkonac, PhD
Please take 2 minutes to complete teacher
evaluation on Courseworks (deadline is
Dec
th)
15
1
The Orange Juice Data
(reminder)
Data
Monthly, Jan. 1950 Dec. 2000 (T = 612)
Price = price of froz
Review for Midterm
Prof: Seyhan Erden Arkonac, PhD
Midterm Exam is on Tuesday
March 26th in class at 9:10am.
(1) You may bring one A4 size paper of formulas
(2) Bring a simple calculator (IT83 or IT89). You
may not use your cell phone as calculator in
the
Instrumental Variable
Regression III, Experiments
(Fall 2010)
Lecture 21
Seyhan Erden Arkonac, PhD
Problem Set 8 is due on Tuesday
November 30th!
1
Application to the Demand for
Cigarettes (SW Section 12.4)
Why are we interested in knowing the elasticity
Introduction to Econometrics
W3412, Fall 2010
Lecture 1
Prof: Seyhan Arkonac, PhD
What is Econometrics?
Why do I need to take this course?
Is this a hard course?
Why am I here?
Will I learn any thing useful here?
Will I have to study a lot?
How hard/easy
Linear Regression with One
Regressor
(Intro to Econometrics)
Lecture 4
Prof: Seyhan Arkonac, PhD
Prolem Set 1 is due on Sept 21st at the
beginning of the class.
1
TA Information: Naihobe Gonzalez
E-mail: [email protected]
Office Hours: Thurs 12-1 (Uris
Regression with a single
Regressor: Hypothesis Testing
and Confidence Intevals
Lecture 5
Prof: Seyhan Erden Arkonac, PhD
Problem Set 1 is due NOW!
Problem Set 2 is due on Sept 28th.
1
TA Information: Naihobe Gonzalez
E-mail: [email protected]
Office Ho
Regression with a single
Regressor: Hypothesis Testing
and Confidence Intervals
Lecture 6
Prof: Seyhan Erden Arkonac, PhD
Solutions to Problem Set 1 are posted.
Problem Set 2 is due on September 28th.
1
TA Information: Naihobe Gonzalez
E-mail: [email protected]
Multiple Regression I (cont) & II
(Fall 2010)
Lecture 7
Prof: Seyhan Erden Arkonac, PhD
Problem Set 2 is due TODAY!
Answers to PS#2 will be posted on Wed Sept
29th.
Problem Set 3 will be posted today, it is due on
Tues. Oct. 5th.
1
TA Information: Naihobe
Multiple Regression III
(Fall 2010)
Lecture 8
Prof: Seyhan Erden Arkonac, PhD
Problem set #3 is posted, it is due on Tues
Oct. 5th (Warning: This ps is longer than
the previous ones!).
Answer to Problem set #2 is posted.
1
TA Information: Naihobe Gonzalez