|
TOPICS
IN ECONOMETRICS I |
G6427 |
|
Department
of Economics |
Autumn
2004 |
|
Columbia University |
Rajeev H. Dehejia |
Contact information:
807B IAB
420 W. 118th
Street
Telephone: 854-4659
E-mail: dehejia@columbia.edu
Course information:
Lectures: Monday 6.10
– 8.00 pm, 501 IAB
Office Hours: MW 1 -
2 p.m.
Who should be interested:
This course will present
selected topics in econometrics aimed at students interested in working with
micro-level data. The techniques discussed are relevant
for students working in fields such as labor economics, public economics,
international, finance, development, industrial organization… You get the
idea. Second year students will learn a range of useful techniques. Students
in the third year or higher will be encouraged to present their work in progress.
Course description:
The course has two themes:
causal inference and Bayesian methods.
Causal inference refers
to the body of theory that helps us to understand when a causal
claim can be made. A huge proportion of applied papers in economics purport
to make causal claims: education increases earnings, training programs increase
employment, globalization increases income volatility, rules of corporate
governance affect bankruptcy rates, access to credit affects child labor,
etc. The course will present a framework within which to evaluate the legitimacy
of these types of claims. The course will also revisit the issue of sample
selection bias, presenting some additional techniques.
The second, overlapping
theme is Bayesian econometrics. There has been a traditional divide in econometrics
between the Bayesians and frequentists. This distinction is increasingly
becoming irrelevant. We will present and understand Bayesian methods through
the lens of simulation-based likelihood techniques. This is a powerful class
of tools that is finding wide application in a range of fields including
labor economics, finance, and industrial organization. Bayesian applications
we will consider range from traditional tools like regression and IV, to
causal inference, to extended topics such as portfolio choice.
Students are welcome
to attend only one of the two modules of the course, but those planning to
do so should attend the first class for organizational reasons.
Prerequisites: This course is intended for second- or higher-year
Ph.D. students in economics. Full knowledge of the first-year Ph.D. sequence
will be presupposed.
Course evaluation:
There will be no exams.
There are two grading options. Students can present a paper that they are
working on, or they can present a paper that they will select with my guidance.
Auditors are welcome.
Readings
A course packet will
be made available with copies of many articles. Several chapters from Andrew
Gelman, John Carlin, Hal Stern, and Donald Rubin, Bayesian Data
Analysis (London: Chapman and Hall) will be used (but will not be part
of the packet).
COURSE OUTLINE
Lecture 1: A definition
of causality; potential outcomes versus predictability.
Cox, D.R. (1992), “Causality:
Some Statistical Aspects,” Journal of the Royal Statistical Society,
Series A, part 2, 291-301.
*Holland, P. (1986),
“Statistics and Causal Inference” (with discussion), Journal
of the American Statistical Association, 81, 945-970.
Neyman, J. (1923), “On
the Applicability of Probability Theory to Agricultural Experiments. Essay
on Principles. Section 9,” translated in Statistical Science
(with discussion), Vol. 5 (1990), No. 4, 465-80.
Rubin, D. (1974), “Estimating
Causal Effects of Treatments in Randomized and Non-Randomized Studies,” Journal of Educational Psychology, 66, 688-701.
Pratt, J.W., and R. Schlaifer
(1988), “On the Interpretation and Observation of Laws,” Journal
of Econometrics, Vol. 39, No.1/2, 23-53.
Sims, C. (1972), “Money,
Income, and Causality,” American Economic Review, 540-552.
Lecture 2: Randomized
Experiments, Randomization Inference, and Blocking
Cox, D.R. (1958), Planning of Experiments, New York, Wiley, Chapters 1-3.
*Fisher, R.A. (1935),
The Design of Experiments, Chapter 2, “The Principles of
Experimentation, Illustrated by a Psycho-Physical Experiment”.
Neyman, J, “Statistical
Problems in Agricultural Experimentation” (with discussion), Journal
of the Royal Statistical Society, Supplement, Vol. II, No. 2.
Lecture 3: Application: Natural Experiments:
*Meyer, B., W.K. Viscusi,
and D. Durbin (1995), “Worker’ Compensation and Injury Duration: Evidence
from a Natural Experiment,” American Economic Review, Vol.
85, 322-40.
Meyer, Bruce (1989),
“A Quasi-Experimental Approach to the Effects of Unemployment Insurance,”
NBER Working Paper No. 3159.
Meyer, Bruce (1994),
“Natural and Quasi- Experiments in Economics,” NBER Technical Working Paper
No. 170.
Observational Studies
with Ignorable Treatment Assignment
*Rubin, D.B. (1977),
“Assignment to a Treatment Group on the Basis of a Covariate,” Journal of Educational Statistics, 2, 1-26.
Rubin, D.B. (1990a),
“Comment: Neyman (1923) and Causal Inference in Experiments and Observational
Studies,” Statistical Science, 5, 472-480.
Rubin, D.B. (1984), “William
G. Cochran’s Contributions to the Design, Analysis and Evaluation of Observational
Studies,” in Poduri and Rao (eds.), W.G. Cochran’s Impact on
Statistics.
Rubin, D.B. (1991), “Practical
Implications of Modes of Statistical Inference for Causal Effects and the
Critical Role of the Assignment Mechanism,” Biometrics, Vol.
47, 1213-1234.
Lee, David (2004), “Economic
Impacts of New Unionization on Private Sector Employers: 1984-2001”, with
John DiNardo, Forthcoming in Quarterly Journal of Economics Working
paper version: Economic Impacts of Unionization on Private Sector Employers:
1984-2001, NBER Working Paper #10598, July 2004.
Lee, David, “Randomized Experiments from Non-random Selection in U.S. House Elections”, revised September 2003. Previous version: The Electoral Advantage to Incumbency and Voters' Valuation of Politicians' Experience: A Regression Discontinuity Analysis of Elections to the U.S. House, NBER Working Paper #8441, August 2001
Hahn, Jinyong, Petra
Todd, and van der Klaauw, Wilbert (1999), “Identification and Estimation
of Treatment Effects with a Regression-Discontinuity Design”.
*Lalonde, Robert (1986),
“Evaluating the Econometric Evaluation of Training Programs,” American Economic Review, Vol. 76, 604-20.
Rosenbaum, P., and D.
Rubin (1983), “The Central Role of the Propensity Score in Observational Studies
for Causal Effects,” Biometrics, 70 (1), 41-55.
Rosenbaum, P., and D.
Rubin (1984), “Reducing Bias in Observational Studies Using Subclassification
on the Propensity Score,” Journal of the American Statistical
Association, Vol. 79, 516-524.
Rosenbaum, P., and D.
Rubin (1985), “Constructing a Control Group Using Multivariate Matched Sampling
Methods that Incorporate the Propensity Score,” American Statistician,
Vol. 39, 33-38.
*Dehejia, Rajeev
H. and Sadek Wahba (1999), “Causal Effects in Non-Experimental
Studies: Re-Evaluating the Evaluation of Training
Programs,” Journal of the American Statistical Association, Volume
94, Number 448 (December 1999), pp. 1053-1062.
Hirano, Kei, Guido Imbens, and
Geert Ridder (2003), "Efficient Estimation of Average
Treatment Effects using the Estimated Propensity Score," (with G. Imbens
and G. Ridder), 2003, Econometrica 71, 1161-1189. Earlier
version appeared as NBER Technical
Working Paper 251.
Abadie, Alberto, and Guido Imbens (2003). "Large Sample Properties of Matching
Estimators for Average Treatment Effects." Manuscript.
Application: Semi-parametric Diffs-in-Diffs
Alberto Abadie, “Semiparametric
Difference-in-Differences Estimators,” Harvard University Working Paper also
in Review of Economic Studies.
Lecture 6: Instrumental
Variables
*Imbens, Guido, and J.
Angrist, “Identification and Estimation of Local Average Treatment Effects,”
Econometrica, Vol. 62, 467-75.
*Angrist, J., G. Imbens,
and D. Rubin, “Identification of Causal Effects Using Instrumental Variables”
(with discussion), Journal of the American Statistical Association,
91, 444-72.
Angrist, J. (1990), “Lifetime
Earnings and the Vietnam Era Draft Lottery: Evidence
from Social Security Administrative Records,” American Economic
Review, 80, 313-36.
Application: Estimating
a Demand Function
*Graddy, K. (1995), “Testing
for Imperfect Competition at the Fulton Fish Market,” RAND Journal
of Economics, 26, 75-92.
Application: Connection to Latent Variable Models
Heckman, J. (1979), “Sample
Selection Bias as a Specification Error,” Econometrica, 47,
153-61.
*Barrow, Burt, Glen Cain,
and Arthur Goldberger (1983?), “Issues in the Analysis
of Selectivity Bias,” Evaluation Studies, Vol. 5., 43-59.
Lecture 1: Statistical
decision theory and the role of Bayes rules.
Readings: Lecture notes
1 and 2.
Lecture 2: The normal
likelihood and classical regression. Posterior simulation. The Predictive
Distribution.
Readings: Lecture note
3. Gelman, et al., chapters 2, 3, and 8.
Lecture 3: Markov
chain Monte Carlo: Gibbs sampling and data augmentation.
Readings: Lecture note
4.
Applications: The
Tobit model. The probit model.
Chib, Siddhartha (1992). “Bayes Inference in the Tobit Censored Regression Model,”
Journal of Econometrics, 51, 79-99.
Albert, J. and S. Chib
(1993). “Bayesian Analysis of Binary and Polychotomous Response Data,” Journal of the American Statistical Association, 88, 669-679.
Lecture 4: Panel data,
random effects, and hierarchical models.
Readings: Lecture note
5. Gelman et al., chapter 13.
Dehejia, Rajeev H. (2000),
“Was There a Riverside Miracle? A Framework for Evaluating Multi-Site Programs,”
National Bureau of Economic Research Working Paper No. 7844 (August 2000).
Lecture 5: Bayesian
models of causal inference.
Imbens, Guido, and Donald
Rubin (1997), “Estimating Outcome Distributions for Compliers in Instrumental
Variable Models,” Review of Economic Studies, October 1997.
Imbens, Guido, and Donald
Rubin (1997), “Bayesian Inference for Causal Effects in Randomized Experiments
with Noncompliance,” Annals of Statistics, 25, 305-327.
Lecture 6: Decisions,
Program Evaluation
*Dehejia, Rajeev H. (1997), “Program Evaluation as a Decision Problem,” National Bureau of Economic Research Working Paper No. 6954 (February 1999).
Lecture 7: Applications
to Financial Economics
Kandel, S., and R. Stambaugh
(1995), “Predicting Returns in the Stock and Bond Markets,” Journal
of Finance, 17, 357-90.
*Barberis, N. (2000)
“ Investing for the Long Run when Returns are Predictable,” Journal
of Finance, 55, 225-64.
Stambaugh, R (1997).
“Predictive Regressions,” Journal of Financial Economics,
54, 375-421.
Aguilar, Omar, and M.
West (2000), “Bayesian Dynamic Factor Models and Portfolio Allocation,” Journal of Business and Economic Statistics, 18, 8-57.
Polson, N. and B. Tew
(2000), “Bayesian Porfolio Selection: An Empirical Analysis of the S&P
500 Index, 1970-1996,” Journal of Business and Economic Statistics,
18, 164-73.
Vrontos, I., P. Dellaportas,
and D. Politis (2000), “Full Bayesian Inference for GARCH and EGARCH Models,”
Journal of Business and Economic Statistics, 18, 187-98.
Watanabe, Toshiaki (2000),
“Bayesian Analysis of Dynamic Bivariate Mixture Models: Can They Explain
the Behavior of Returns and Trading Volume,” Journal of Business
and Economic Statistics, 18, 199-210.
Waggoner, Daniel F. and
Tao Zha (1999), “Conditional Forecasts in Dynamic Multivariate Models,” Review of Economics and Statistics, 81, 639-51.