Course Description. Course Requirements

Transcription

1 University of Pennsylvania Spring 2007 Econ 721: Advanced Microeconometrics Petra Todd Course Description Lecture: 9:00-10:20 Tuesdays and Thursdays Office Hours: 10am-12 Fridays or by appointment. To arrange an appointment see me after class or contact me at or This course considers parametric, nonparametric and semiparametric estimation techniques that are used in microeconomics. These include cross-section, panel data and duration analysis methods. Topics covered will include (a) classical discrete choice (or qualitative response) modeling, (b) choice-based sampling, (c) simulation estimators, (d) models with censoring and truncation (limited dependent variable models) (e) nonparametric smoothing methods (f) semiparametric discrete models incl. index models, partially linear models, discrete choice and tobit models, (g) models for panel data, (h) methods for analyzing duration data, and (i) program evaluation methods. We begin by considering the econometric problems introduced when the dependent variable is discrete. We discuss the development of classical discrete choice models and their link to utility maximization. Then we consider cases where the dependent variable is truncated or censored, and cases where the measurement process for the dependent variable is not independent of the behavioral process ( sample selection ). The second part of the course considers ways of relaxing parametric assumptions through nonparametric and semiparametric estimation, with a focus on local polynomial methods. We will also discuss some recent developments in how to choose smoothing parameters. We will examine applications of nonparametric methods in the literature on semiparametric modeling. The third part of the course examines both parametric and semiparametric techniques for estimating panel data models and duration models. It also covers recent literature applying these methods in the program evaluation literature. Course Requirements There will be 4 problem sets and a final exam. Problem sets will include empirical assignments that require some programming. You can do the programming in a language of your choice. Matlab, Gauss, Splus, FORTRAN or C are probably the most useful languages for the assignments. A free version of Splus (called R) can be downloaded using instructions available on the class website. Any datasets to be used for these assignments will be distributed over the course of the semester. Also, some of the readings that are most relevant will be distributed in class or be made available for xeroxing. The problem sets will count for 40% of the final grade and the final exam for 60%. 1

2 Recommended Readings General Reference Texts: The first three texts are available for purchase from the Penn bookstore. The chapters from the Manski and McFadden book (which is out of print) can be downloaded from Berkeley s web site Amemiya, T., Advanced Econometrics, Harvard University Press, (selected chapters) Pagan, Adrian and Aman Ullah., Nonparametric Econometrics, CambridgeUniversity Press, Horowitz, Joel., Semiparametric Methods in Econometrics, Springer-Verlag, 1998 Greene, W. Econometric Analysis, MacMillan Publishing Company, Manski, C.F. and D.L. McFadden (eds), Structural Analysis of Discrete Data with Econometric Applications, MIT Press, (selected chapters) Arrelano, Manuel, Panel Data Econometrics, Oxford University Press, Hsiao, C. Analysis of Panel Data, Cambridge University Press, Hardle, W. Applied Nonparametric Regression, Cambridge University Press. Lee, Myoung-jae Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models, Springer-Verlag, New York, Lancaster, T. The Econometric Analysis of Transition Data, Cambridge University Press. Recommended Readings by Topic (A) Classical Discrete Choice Models Amemiya (Chapter 9) Greene (Chapter 20) Manski, C.F. and D.L. McFadden (eds), Structural Analysis of Discrete Data with Econometric Applications, MIT Press, (Chapters 5) Bunch, David S. Estimability in the Multinomial Probit Model in Transportation Research B. Hausman, J. and Wise, D. A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences, in Econometrica, Mar. 1978, 46(2), pp

3 McFadden, D.L. (1984): Econometric Analysis of Qualitative Response Models in Handbook of Econometrics, Volume II, Chapter 24. Yellot, J. The Relationship between Luce s Choice Axiom, Thurstone s Theory of Comparative Judgment, and the Double Exponential Distribution in Journal of Mathematical Psychology, 15, Berry, S.; Levensohn, J. and Pakes, A. Automobile Prices in Market Equilibrium in Econometrica, July 1995, 63(4), pp Havivassiliou, V. and Ruud, P. Classical Estimation Methods for LDV Models using Simulation in Handbook of Econometrics, Volume 4, pp Manski, C.F. and D.L. McFadden (eds), Structural Analysis of Discrete Data with Econometric Applications, MIT Press, (Chapter 1) (B) Models with Censoring, Truncation and Selection McFadden, D.L., Econometric Analysis of Qualitative Response Models, in Handbook of Econometrics (Z. Griliches and M. Intriligator, eds), Vol. II, Amemiya, T., Chapter 10. Amemiya, T., Tobit Models: A Survey, Journal of Econometrics, 24. Heckman, J., Sample Selection Bias as a Specification Error, Econometrica, 47, 1979: Heckman, J., Varieties of Selection Bias, AER, Papers and Proceedings, (C) Nonparametric Estimation Methods Hardle, W. Applied Nonparametric Regression, Cambridge University Press. Hardle, W. and Linton, O. Applied Nonparametric Methods in Handbook of Econometrics, (R. Engle and D. McFadden, eds.), Vol. IV, 1994, p Fan, J. Design Adaptive Nonparametric Regression, in JASA, 87, Fan, J. Local Linear Regression Smoothers and their Minimax Efficiencies, The Annals of Statistics, 21, Jones, M. C., Marron, J. S. and Sheather, S. J. (1996): A Brief Survey of Bandwidth Selection for Density Estimation in Journal of the American Statistical Association, Vol. 91, No. 433, Stoker, T. M. (1989) Consistent Estimation of Scaled Coefficients in Econometrica, 54, pp

4 Powell, J., Stock, J.H. and T. Stoker (1989) Semiparametric Estimation of Weighted Average Derivatives in Econometrica, 57, pp Ichimura, Hidehiko and Todd, Petra (2006) Implementing Nonparametric and Semiparametric Estimators, forthcoming in Handbook of Econometrics, Volume 6. (D) Semiparametric Models General reference: Ichimura, Hidehiko and Todd, Petra (2000) Implementing Nonparametric and Semiparametric Estimators, manuscript under preparation for Handbook of Econometrics, Volume5. Lee, Myoung-jae Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models, Springer-Verlag, New York, Powell, J. (1994): Estimation of Semiparametric Models. Handbook of Econometrics, Volume 4. eds. R.F. Engle and D.L. McFadden, Index Models Ichimura, H. Semiparametric Least Squares (SLS) and Weighted SLS Estimation of Single- Index Models in Journal of Econometrics, 58, pp Ichimura, H. and L. Lee ( 1991): Semiparametric Least Squares Estimation of Multiple Index Models: Single Equation Estimation, in Nonparametric and Semiparametric Methods in Economics and Statistics, ed. by W.A. Barnett, J. Powell, and G. Tauchen (Cambridge, England: Cambridge University Press) Partial Linear Models Robinson, P. (1988): Root-N Consistent Nonparametric Regression. Econometrica, 56, Engle, R.F., Granger, C.W.J., Rice, J. and A. Weiss (1986): Semiparametric Estimates of the Relation between Weather and Electricity Sales in JASA, 81, pp Discrete Choice Models Amemiya, T., Chapter 9. Manski, C.F., Maximum Score Estimation of the Stochastic Utility Model of Choice, Journal of Econometrics, Vol. 3, 1975:

5 Horowitz, J.L., A Smoothed Maximum Score Estimator for the Binary Response Model, Econometrica, Vol. 60, No.3, 1992: Klein, R.W. and R.H. Spady, An Efficient Semiparametric Estimator for Binary Response Models, Econometrica, Vol. 61, No.2, 1993: Ichimura, H. Semiparametric Least Squares Estimation of Single Index Models (SLS) and Weighted SLS Estimation of Single Index Models. Journal of Econometrics, 58, , Tobit Models Powell, J., Least Absolute Deviations Estimation for the Censored Regression Model, Journal of Econometrics, 1984: Powell, J., Symmetrically Trimmed Least Squares Estimation for Tobit Models, Econometrica, 1986: Cosslett, Semiparametric estimation of a regression model with sampling selectivity, in Nonparametric and semiparametric methods in Econometrics and Statistics (Barnett, Powell and Tauchen, eds), Cambridge University Press, Ahn, H. and J.L. Powell, Semiparametric Estimation of Censored Selection Models with a Nonparametric Selection Mechanism, Journal of Econometrics, Vol. 58(1), 1993:3-30. Honore, B.E. and J.L. Powell, Pairwise Difference Estimators of Censored and Truncated Regression Models, Journal of Econometrics, Vol. 64, 1994: Heckman, J. (1980): Addendum to Sample Selection Bias as specification Error, in Evaluation Studies Review Annual, ed. by E. Stromsdorfer and G. Frakas. San Fransisco, Sage. Powell, J. Estimation of Semiparametric Models in Handbook of Econometrics, Volume 4, pp (E) Panel Data Models Chamberlain, G., Panel Data, Handbook of Econometrics, Vol. II, Chapter 22 Arrelano, Manuel, Panel Data Econometrics, Oxford University Press, Heckman, James Statistical Models for Discrete Panel Data in Manski, C.F. and D.L. McFadden (eds), Structural Analysis of Discrete Data with Econometric Applications, MIT Press, (Chapter 3) Heckman, James The Incidental Parameters Problem and the Problem of Initial Conditions in Estimating a Discrete Time-Discrete Data Stochastic Process in Manski,C.F.andD.L. McFadden (eds), Structural Analysis of Discrete Data with Econometric Applications, MIT Press, (Chapter 3) 5

6 (F) Duration Analysis Lancaster, T. The Econometric Analysis of Transition Data, Cambridge University Press. (G) Program Evaluation Angrist, J. and Imbens, G. Identification and Estimation of Local Average Treatment Effects in Econometrica, March, 1994, v62, n2, p467(9). Hahn, Jinyong, Van der Klauww, Wilbert and Todd, Petra (2000): Identification of Treatment Effects by Regression Discontinuity Designs, Econometrica, Heckman, J. J., Lalonde, R. and J. Smith (1999): The Economics and Econometrics of Active Labor Market Programs in, Handbook of Labor Economics, Volume III, eds. O. Ashenfelter and D. Card (Elsevier: Amsterdam). Heckman, J. J., H. Ichimura, J. Smith and P. Todd (1998): Characterizing Selection Bias Using Experimental Data, Econometrica, Vol. 66, Heckman, J.J., H. Ichimura and P. Todd (1997), Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Program Review of Economic Studies, Vol. 64(4). Heckman, J. J., H. Ichimura and P. Todd (1998), Matching As An Econometric Evaluation Estimator, Review of Economic Studies, Vol. 65, Heckman, J. and E. Vytlacil (2005): Local Instrumental Variables, working paper #0252. NBER Technical Heckman, J. and E. Vytlacil (2005): Structual Equations, Treatment Effects and Econometric Policy Evaluation, Econometrica, J. Smith and P. Todd (2001): Does Matching Overcome Lalonde s Critique of Nonexperimental Estimators?, Journal of Econometrics, February, Manski,C.(1997): Monotone Treatment Response, Econometrica, Vol. 65, No. 6. Manski, C. and J. Pepper (2000): Monotone Instrumental Variables: With an Application to the Returns to Schooling, Econometrica, Vol. 68, No. 4. 6