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You are here:Open notes>IowaState>Economics571IntermediateEconometrics
Economics 571: Intermediate Econometrics
How to study this subject
1. Course Description
This course serves as an introduction to econometrics and places particular emphasis on
estimation and interpretation of the standard linear regression model.
Our first series of lectures will be devoted to a quick review of concepts in statistics and
probability. Topics covered include marginal, joint and conditional densities, moments of
scalars and vectors, and notions convergence of random variables.
We then proceed to analyze the linear regression model. In the context of this simple
regression model, we discuss the models’ assumptions, estimation, prediction, and coefficient
interpretation under common transformations. We then move on to the general kvariable
model, and use linear algebra in the context of its discussion. Topics covered include
point estimation, properties of the OLS estimators, omitted variables and multicollinearity.
Additional topics include testing, the use of dummy variables, heteroscedasticity and panel
data. Time permitting, we will also investigate departures from the standard linear model
including discussions of meanindependence violations, instrumental variables estimation
and topics in discrete choice analysis. Students will apply the techniques learned in this
course using STATA (This software package will be made available in the computer labs in
64 Heady).
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2. Grading
The final grade will be based on problem sets and a combined examination score. The
problem sets and exams will constitute 35 and 65 percent of your final grade, respectively.
Three examinations will be given throughout the semester. All three of these exams will
count toward the examination portion of your final grade. However, I will weight these
exams as follows: 45 percent of the weight will be given to the highest of the three scores;
30 percent of the weight will be given to the secondhighest score and 25 percent of the
weight will be given to the lowest score.
3. Lectures
The lectures will generally follow the topics covered in the required textbook for the course,
Introductory Econometrics by Wooldridge. Additional material not found in the text will
also be covered to supplement your understanding of the topics. This is particularly true
for the statistics review at the beginning of the course, and for the linearalgebra based
treatment of the multiple regression model.
4. Course Web Site
The course website, http://www.econ.iastate.edu/classes/econ571/tobias/index.htm will be
a vital tool throughout this class. The course web site will contain: the problem sets for
the course, the problem set solutions and data sets required for completing the problem
sets. A general course outline for current, past and (potentially) future weeks will also be
provided. Finally, the course web site will contain other important announcements such as
due dates for the problem sets.
5. Textbooks
The required textbook is
Introductory Econometrics (2006) by Wooldridge. This is the updated, third edition of the
book.
Other texts that you may find helpful are:
J. Stock and M. Watson, Introduction to Econometrics, 2003.
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Gujarati, D. N. Basic Econometrics, 4th edition, 2003.
Madalla, G. S. Introduction to Econometrics, 2nd edition, 1992.
Kmenta, Jan Elements of Econometrics, 2nd edition, 1986.
Kennedy, Peter. A Guide to Econometrics, 3rd edition, 1992.
Please note that this class will be based exclusively on material covered in the lecture
notes. Use the textbook to support your understanding of class discussion. I will never
ask you questions on an exam or quiz that is contained in the text, but was not covered in
the lectures.
I. Material Covered Prior to First Exam
 Introduction, Course Overview, Data Types, Marginal Densities, Moments of a Scalar Random Variable
 Lecture Notes, Wooldridge: Chapter 1, Appendix B
 Bivariate, Joint and Conditional Distributions, Conditional Expectations, Independence, Iterated Expectations, First Two Moments of a Random Vector
 Lecture Notes, Wooldridge: Chapter 1, Appendix B
 Unbiasedness, Notions of Convergence, Consistency, Convergence in Mean Square, Convergence in Probability, Law of Large Numbers, Central Limit Theorem, Sampling Distributions
 Lecture Notes, Wooldridge Appendix C.1C.3
 Special Distributions, Introduction to the Simple Linear Regression Model, Least Squares Estimation
 Wooldridge, Appendix B.5, Chapter 2.12.2
 Powerpoint Show: Regression Introduction
 Estimated Residuals, Fitted Values, Rsquared, Sampling Distribution of the OLS Estimator
 Wooldridge, 2.2, 2.3, 2.5
II. Material Covered Before Second Exam
 Interpretation of Regression Coefficients Under Common Logarithmic Transformations:
 Wooldridge, 2.4
 The General kVariable Linear Regression Model: Setup and Estimation Using Matrix Algebra
 Wooldridge, Appendix E.1, E.2
 Assumptions of the Linear Model, Properties of the OLS Estimator: Unbiasedness, Consistency, Asymptotic Normality, Efficiency (GaussMarkov Theorem)
 Lecture Notes, Wooldridge, Appendix E.1, E.2, Chapter 3
 Short versus Long Regression and the Interpretation of Multiple Regression Coefficients
 Lecture Notes
 Omitted Variable Bias
 Wooldridge, Chapter 3.3
 Hypothesis Testing: Scalar (one and twosided) tests, pvalues, confidence intervals
 Wooldridge, Chapter 4.14.3
 Powerpoint Show: Confidence Intervals, pvalues, scalar testing
 Joint Hypothesis Testing
 Wooldridge, Chapter 4.44.6
 Dummy Variables and Interactions
 Wooldridge, Chapter 7 (all but 7.5)
III. Material Covered Before Final Exam
 Heteroscedasticity
 Wooldridge, 8.1,8.2,8.4, Lecture Notes
 Multicollinearity
 Wooldridge, 3.4, Lecture Notes
 MeanIndependence Violations:
 Measurement Error (in x), Wooldridge, 9.3, 15.4
 Endogeneity of righthand side variables, Wooldridge, 15.115.4
 Simultaneity, Wooldridge, 16.116.4
 Instrumental Variables Wooldridge, 15.115.4
 Two Stage Least Squares Estimation Wooldridge, 15.115.4
 Models for Discrete Choice
 Binary Choice Models
 Linear Probability Model (LPM), Wooldridge, 7.5, 8.5
 Probit Model, Wooldridge, 17.1
 Logit Model, Wooldridge, 17.1
 Models of Ordered Outcomes: The Ordered Probit Lecture Notes
 Models of Censored Outcomes: The Tobit Model Wooldridge, 17.2
 Binary Choice Models
 Panel Data (Time Permitting)
 Wooldridge, Chapters 13, 14
Official Notes
Problem Sets
Problem Set Number  Due Date  Data Sets 
1/25/08  
Problem Set #2  2/1/08  
Problem Set #3  2/8/08  
Problem Set #4  2/18/08  
Problem Set #5  2/25/08  
Problem Set #6  3/7/08  
Problem Set #7  3/14/08  
Problem Set #8  3/31/08  
Problem Set #9  4/21/08  
Problem Set #10  Recommended, NOT required  

Problem Set Solutions
Problem Set Number  Solutions  Other Needed Files 
#1  PS#1 Solutions  
#2  PS#2 Solutions  
#3  PS#3 Solutions  
#4  PS#4 Solutions  Regression Output 
#5  PS#5 Solutions  Regression Output 
#6  PS#6 Solutions  Regression Output 
#7  PS#7 Solutions  
#8  PS#8 Solutions  Regression Output 
#9  PS#9 Solutions  Regression Output 
 Lab Outline
 California School Test Score Data (.txt file)
 California Test score data  STATA format(.dta)
 Test Score Data Readme File
 Law School Salary Data (.dta file)
Notes from other sources
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