PhD: Structural + Computation

Author

Moshi Alam

Published

December 31, 2024

Course Description

THis si the second part of a two-part sequence of Applied ecopnometrics at the PhD level. Here I cover structural models and econometric methods with a focus on some types of labor and public economics problems. Having a comprehensive course on structural models is nearly impossible since structural work fine tune the model to the specific theory and context. The key lies in identification, which can ultimatley help us in doing counterfactuals. Hence, my emphasis will be mostly on identification, and primarily with discrete choice models (both static and dynamic) using estimation methods like MLE, GMM, SMM, and primarily indirect inference. I will cover some numerical optimization methods like gradient-based, derivative-free methods but mostly Nelder-Mead simplex algorithm.

I do not find the discussions of whether structural models are better than design-based (reduced form) methods very constructive and hence will not engage in them. However, I will list some papers that discuss these. I like both, and have found both helpful in my work. In my view, both approaches have their own strengths, and it is cool to leverage best of both worlds.

No textbook as such—its mostly papers, but I found Kenneth Train’s Discrete Choice Methods with Simulation very helpful when I was learning discrete choice models for the first time. Depending on time I will also talk about some of my experience in improving computational efficiency in julia, and discuss some of the workflows that have worked for me.

Assessment

Problem Sets — 80%: Four problem sets + presentations. These are to be completed in groups of two.
- Students can use their programming language of choice. I recommend julia, because of its computational advantages. However, R or Python are also fine.
- Students are expected to compile their assignments using LaTeX. The final submission will involve both code and results in a single PDF with all tables properly formatted, variables properly labeled, and figures and tables with complete footnotes as if they are submitting the work to a journal.
- I will also assign students topics for presentations/referee reports.
Class attendance and participation — 20%

Readings

The readings are in not a particular order, although I will talk about identification in general first and then how is strucutral (model-based) approach different from reduced form (design-based) approach. Things that are not in particular order maybe specific topics & estimation routines and papers applying them.

Identification

Heckman, J.J. and Vytlacil, E.J. (2007a). “Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation.” Handbook of Econometrics, Vol. 6B, Ch. 70.
Heckman, J.J. and Vytlacil, E.J. (2007b). “Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments.” Handbook of Econometrics, Vol. 6B, Ch. 71.
French, E. and Taber, C. (2011). “Identification of Models of the Labor Market.” Handbook of Labor Economics, Vol. 4A, Ch. 6: 537-617.
Matzkin, R.L. (2007). “Nonparametric Identification.” Handbook of Econometrics, Vol. 6B, Ch. 73.

Structural vs. Reduced Form Methods

Low, H. and Meghir, C. (2017). “The Use of Structural Models in Econometrics.” Journal of Economic Perspectives, 31(2): 33-58.
Angrist, J.D. and Pischke, J.S. (2010). “The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con out of Econometrics.” Journal of Economic Perspectives, 23(1): 69-90.
Keane, M.P. (2010). “A Structural Perspective on the Experimentalist School of Econometrics.” Journal of Economic Perspectives
Galiani, S. and Pantano, J. (2021). “Structural Models: Inception and Frontier.” NBER Working Paper 28698.

Static Discrete Choice Models

McFadden, D. (1973). “Conditional Logit Analysis of Qualitative Choice Behavior.” In P. Zarembka (ed.), Frontiers in Econometrics. Academic Press.
McFadden, D. (1981). “Econometric Models of Probabilistic Choice.” In C. Manski & D. McFadden (eds.), Structural Analysis of Discrete Data.

Dynamic Discrete Choice Models

Rust, J. (1987). “Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher.” Econometrica, 55(5): 999-1033.
Hotz, V.J. and Miller, R.A. (1993). “Conditional Choice Probabilities and the Estimation of Dynamic Models.” Review of Economic Studies, 60(3): 497-529.

Arcidiacono, P. and Miller, R.A. (2011). “Conditional Choice Probabilities and the Estimation of Dynamic Models with Unobserved Heterogeneity.” Econometrica, 79(6): 1823-1867.

Estimation Methods

MLE, GMM, SMM, II

Ruud, P.A. (1991). “Extensions of Estimation Methods Using the EM Algorithm.” Journal of Econometrics, 49(3): 305-341.

Hansen, L.P. (1982). “Large Sample Properties of Generalized Method of Moments Estimators.” Econometrica, 50(4): 1029-1054.

McFadden, D. (1989). “A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration.” Econometrica, 57(5): 995-1026.

Gourieroux, C., Monfort, A., and Renault, E. (1993). “Indirect Inference.” Journal of Applied Econometrics, 8(S1): S85-S118.

Applications in Labor Economics

Stated Preferences

Wiswall, M. and Zafar, B. (2018). “Preference for the Workplace, Investment in Human Capital, and Gender.” Quarterly Journal of Economics, 133(1): 457-507.
Alam, M.M.U., Mookerjee, M., and Roy, S. (2021) “Employee-Side Discrimination: Beliefs and Preferences.”
Adams, A., and Andrew, A. (2025) “Revealed Beliefs and the Marriage Market Return to Education“ Quarterly Journal of Economics.
Delavande, A. and Zafar, B. (2019). “University Choice: The Role of Expected Earnings, Nonpecuniary Outcomes, and Financial Constraints.” Journal of Political Economy, 127(5): 2343-2393.

Monopsony and Firm Effects

Card, D., Cardoso, A.R., Heining, J., and Kline, P. (2018). “Firms and Labor Market Inequality: Evidence and Some Theory.” Journal of Labor Economics, 36(S1): S13-S70.

Lamadon, T., Mogstad, M., and Setzler, B. (2022). “Imperfect Competition, Compensating Differentials, and Rent Sharing in the US Labor Market.” American Economic Review, 112(1): 169-212.
Kroft, K., Luo, Y., Mogstad, M., and Setzler, B. (2025). “Imperfect Competition and Rents in Labor and Product Markets: The Case of the Construction Industry.” American Economic Review, 115(9): 2926-2969.
Kline, P. and Petkova, K. (2025). “Labor Market Monopsony: Fundamentals and Frontiers.” Handbook of Labor Economics, Vol. 6: 655-728.

Empirical school choice

Agarwal, N., & Somaini, P. (2018). “Demand Analysis Using Strategic Reports: An Application to a School Choice Mechanism.” Econometrica
Kapor, A., Neilson, C., & Zimmerman, S. (2020). “Heterogeneous Beliefs and School Choice Mechanisms.” American Economic Review

Optimal Policies, Career Decisions, Human Capital, Household consumption, Migration, Search

Alam, Davis & Gregory (2025) “Optimal Place-Based Redistribution using Geographic Variation in the Marginal Utility of Income”
Fu & Gregory (2019) “Estimation of an Equilibrium Model with Externalities: Post-Disaster Neighborhood Rebuilding” Econometrica
Postel-Vinay, F. and Robin, J.M. (2002). “Equilibrium Wage Dispersion with Worker and Employer Heterogeneity.” Econometrica, 70(6): 2295-2350.
Keane, M.P. and Wolpin, K.I. (1997). “The Career Decisions of Young Men.” Journal of Political Economy, 105(3): 473-522.
Eckstein, Z. and Wolpin, K.I. (1989). “Dynamic Labour Force Participation of Married Women and Endogenous Work Experience.” Review of Economic Studies, 56(3): 375-390.
Todd, P.E. and Wolpin, K.I. (2006). “Assessing the Impact of a School Subsidy Program in Mexico: Using a Social Experiment to Validate a Dynamic Behavioral Model of Child Schooling and Fertility.” American Economic Review, 96(5): 1384-1417.
Voena, A. (2015). “Yours, Mine, and Ours: Do Divorce Laws Affect the Intertemporal Behavior of Married Couples?” American Economic Review, 105(8): 2295-2332.
Kennan, J. and Walker, J.R. (2011). “The Effect of Expected Income on Individual Migration Decisions.” Econometrica, 79(1): 211-251.

Applications in Public Economics

Alam, M.M.U., Davis, M, and Gregory, J. (2025). “Optimal Place-Based Redistribtuion using Geographic Variation in Marginal Utility of Income”
Chetty, R. (2009). “Sufficient Statistics for Welfare Analysis: A Bridge Between Structural and Reduced-Form Methods.” Annual Review of Economics, 1: 451-488.

Applications in Development Economics

Attanasio, O., Meghir, C., and Santiago, A. (2012). “Education Choices in Mexico: Using a Structural Model and a Randomized Experiment to Evaluate PROGRESA.” Review of Economic Studies, 79(1): 37-66.
Duflo, E., Hanna, R., and Ryan, S.P. (2012). “Incentives Work: Getting Teachers to Come to School.” American Economic Review, 102(4): 1241-1278.

Survey Articles

Todd, P.E. and Wolpin, K.I. (2010). “Structural Estimation and Policy Evaluation in Developing Countries.” Annual Review of Economics, 2: 21-50.
Keane, M.P., Todd, P.E., and Wolpin, K.I. (2011). “The Structural Estimation of Behavioral Models: Discrete Choice Dynamic Programming Methods and Applications.” Handbook of Labor Economics, Vol. 4, Ch. 4: 331-461.
Aguirregabiria, V. and Mira, P. (2010). “Dynamic Discrete Choice Structural Models: A Survey.” Journal of Econometrics, 156(1): 38-67.
Keane, M.P. and Wolpin, K.I. (2009). “Empirical Applications of Discrete Choice Dynamic Programming Models.” Review of Economic Dynamics, 12(1): 1-22.
Blundell, R. and MaCurdy, T. (1999). “Labor Supply: A Review of Alternative Approaches.” Handbook of Labor Economics, Vol. 3A, Ch. 27.