ARE 213-Imbens

ARE 213- APPLIED ECONOMETRICS

Spring 2006

Professor Guido Imbens

GSI: Hendrik Wolff

Three hours of lecture per week. Prerequisites: 211 and 212 or equivalent or consent of instructor.

We will discuss standard and advanced econometric techniques that are used in agricultural and resource economics. These techniques include methods for analysis of discrete choice, panel data and methods for dealing with endogeneity. The emphasis will be on learning how to apply these techniques and interpret the results. There will be exercises requiring analyzing real data on computers.

Course Syllabus [updated version January 16]

The class meets Tuesdays & Thursdays 11:00-12:30, 2301 Tolman.

The discussion section meets Thursdays 10:00-11:00, 2311 Tolman.

Professor Imbens Office Hours: Tuesday, 2:00–3:00pm in 661 Evans Hall and by appointment.

Hendrik Office Hours: Wednesday, 2:00-3:00 in 306 Giannini Hall.

Lecture Notes

Lecture Notes 1 OLS1: Estimation, Inference and Predicting Outcomes

Lecture Notes 2 OLS2: Variance Estimation and the Bootstrap

Lecture Notes 3 OLS3: Omitted Variable Bias and Proxy Variables

Lecture Notes 4 OLS4: Clustering and Variance Estimation

Lecture Notes 5 OLS5: Measurement Error

Lecture Notes 6 MLE1: Basics and Likelihood Functions

Lecture Notes 7 MLE2: Computational Issues Updated version from February 11th

Lecture Notes 8 MLE3: Consistency, Asymptotic Normality and Efficiency

Lecture Notes 9 MLE4: Classical Testing

Lecture Notes 10 MLE5: Truncation, Censoring and Corner Solutions

Lecture Notes 11 Discrete Choice Models 1: Binary Response

Some figs for Lecture Notes 11

Lecture Notes 12 Discrete Choice Models 2: Ordered Multinomial Response Models

Lecture Notes 13 Discrete Choice Models 3: Multinomial, Conditional and Nested Logit Models

Goldberg For Lecture Notes on Nested Logit

Lecture Notes 14 Discrete Choice Models 4: Nested Logit Models

Travel mode nests, McFadden Appendix for Nested Logit Models

Fig. Extreme Value vs. Normal distribution

Lecture Notes 15 Discrete Choice Models 5: McFadden’s Conditional Logit Model for Gas/Electric Dryer Purchases

Lecture Notes 16 Discrete Choice Models 6: Random-Effects Logit Models: Berry-Levinsohn-Pakes (BLP)

Lecture Notes 17 Endogeneity I: Linear Models and Indirect Least Squares

Lecture Notes 18 Endogeneity II: TSLS, Control Functions, and LIML

Lecture Notes 19 Endogeneity III: Selection Models

Lecture Notes 20 Endogeneity IV: Heterogeneity and LATE

Lecture Notes 21 Panel I: Random Effects

Lecture Notes 22 Panel II: Fixed Effects

Problems Sets

Problem Set 1 , Data1 and Solutions1

Problem Set 2 , Data2 and Solutions2

Problem Set 3 , Data3 and Solutions3

Codebook for PS3

Problem Set 4 , Data4 and Solutions4

Problem Set 5 , Data5

Problem Set 6 , Data6

Problem Set 7 , Data7

Problem Set 8 , Data8 as .mat file In case you have problems opening .mat files try the ascii version: Data8 as .dia file

Problem Set 9

Problem Set 10 , Data10 as .mat file

Problem Set 11 , Data11 as .mat file

Problem Set 12 , Data12 as .dat file

Codebook for PS12

Final , Data for Final as .mat file and as an alternative Data for Final as .xls file

Files discussed in Section

Matlab Primer Introduction into MATLAB by Kermit Sigmon

Matlab file test.m In addtion you need the function:

Matlab function ols.m This file has to be copied into the same folder as test.m

Section2.pdf This pdf file contains some of the Matlab syntax discussed in Section from January 26

Section4.pdf This pdf file contains some of the Matlab syntax from February 9th

HW4Solution.m This .m file contains the solution to HW4; in addtion you need the functions
- fder.m computes first dervivative of the log likelihood function
- sder.m computes second dervivative of the log likelihood function and
- loglik.m computes level value of the log likelihood function
Table on Classical Tests From Section 6 & 7

OLS vs. Censored ML Howards Simulation in Section 8: use the file generateddata.m

Handbook of Econometrics Vol. 1 to Vol. 4

Bayesian Econometrics I: Introduction Combining Sample & Nonsample Info

Bayesian Econometrics IIa: Summary of Geweke et al. paper Probit with many Endogenous Regressors as an example of the Geweke et al. paper

Geweke Et Al. 2003 Hospital Quality - Selection Model, Econometrica 2003

Entropy Econometrics Introduction The Golan, Judge and Miller 1996 Approach

Entropy Simple Dice Example for MaxEnt and Cross Entropy XLS file with ME and CE Solver