Econ 122 Introduction to
Econometrics
Spring 2005
Course Description: This course will introduce
statistical analysis of linear models, as applied to economic data. Though much
of the course will be devoted to derivation of econometric theory, applications
of this theory to particular problems in the analysis of economic data will
also be discussed.
Prerequisites: Economics 121. The first few lectures
will review the pertinent material from statistics; students should be
comfortable with these basic concepts of probability theory and statistical
inference, and, more generally, with mathematical derivations.
Course Requirements: The grade in this course will be based on a midterm
exam (worth 25% of the grade), a research paper (25%), (approximately)
bi-weekly problem sets (15%), and a final exam (35%). There will be no makeup
exams - scheduling conflicts should be discussed with me at least a week prior
to the exam. Regular attendance at the tutorial sections is important; in
addition to reviewing the answers to the problem sets, the TAs will also cover
the essentials of EVIEWS, the computer package needed for the problem sets and
the research paper. Some introductory
notes on how to use EVIEWS have been kindly written by (Prof Dan
Westbrook).
Required
Text: R.S.
Pindyck and D.L. Rubinfeld, Econometric Models and Economic Forecasts, Fourth
Edition (McGraw-Hill, 1997).
Final Exam (all
classes): Friday May 6 12:30 - 2:30, WAL 394
Office
Hours: Monday 2:00 – 4:00 pm (x7-1570)
Email: evansm1@georgetown.edu
Class Times and
Recitations:
01 LEC MW 10:15-11:30 ICC 108 REC. R 5:40 - 6:55 REI 282 02 LEC MW 11:40-12:55 ICC 106 REC. F 4.15 - 5:30 STM G40
TA:
Zaki Zahran
Email:
zz4@georgetown.edu
Office
Hours: Friday 11:40 – 12:55 pm STM G40
1. Review of Probability
and Statistical Inference
·
Introduction
·
Review
of simple probability - random variables, expectations (bus/bike example)
·
Measurement
error model (point estimation, expectation and variance)
·
Sampling
properties of the sample mean (interval estimation, univariate hypothesis
tests)
(P&R Chapter 2,
including appendices)
2. Simple Linear Regression
·
Introduction
(examples); curve fitting by least squares
o
Auto sales example data (from P& R)
o
Auto sales example Eviews File
o
Problem Set 2 Data Answer Key
·
Probability model and Gauss-Markov theorem
(P&R Chapter 3.1-3.2,
Appendix 3.1)
Statistical properties
·
Hypothesis
testing
(P&R Chapter 3.3-3.4,
Appendix 3.2 )
·
Evaluating
the fit of the least squares estimator
o
3. Multiple Regression
·
Introduction
to Multiple Regression (examples); probability model
·
Matrix
algebra version
o
Lecture notes on matrix algebra
o
Problem Set 3 Data Answer Key
(P&R Chapters 4.1-4.4,
App. 4.1,4.2,4.3 )
·
Dummy
variable estimation and F-tests: age-earnings
·
More on F-tests; examples
(P&R Chapters 5.1-5.3,
5.5, App. 5.1)
4. Departures from Basic
Assumptions
·
Heteroskedasticity
·
Serial Correlation
·
Serial Correlation and review
(P&R Chapter 6)
·
Measurement
error (errors in variables)
·
Instrumental variable estimation
·
IV and intro to simultaneous equations
(P&R Chapter 7 (except 7.4.2), Appendix
7.1)
5. Advanced Topics (As time
permits)
·
Simultaneous
equations (P&R Chapter 12.1-12.4)
·
Lecture notes 21 and 22 CAPM Example
·
Binary
Models
·
Panel
Data
·
More
on forecasting.
OLD EXAMS
2000 Mid-Term Exam Outline
Answers
2002 Mid-Term Exam Outline
Answers
2003 Mid-Term Exam Outline Answers
2005 Mid-Term Exam Outline
Answers
2002 Final Exam Data File Outline Answers
2003 Final Exam Outline Answers
Handout Data Excel Data ASCI EVIEWS Data Used in Class