Topics in Microeconometrics

Course Information

• Meeting Time: Tuesdays and Thursdays, 12:45–2:05 PM.
• Location: Ramseyer Hall 0115.
• Instructor: Jason Blevins (blevins.141@osu.edu)
• Office hours: Tuesdays 2:15–3:15 PM, Wednesdays 12:30–1:30 PM (401 Arps Hall)
• Website: https://jblevins.org/courses/econ8833f14/

Description

The main goals of this course are to introduce students to current research topics in microeconometrics, primarily in the areas of dynamic structural models of strategic interaction and partial identification, and to familiarize students with a set of tools–both applied and theoretical tools–which they can apply to their own dissertation research. The material will be relevant to students writing dissertations in econometrics and/or applied microeconomics.

The course will begin with a general discussion of identification, in which we attempt to understand what features of econometric models we can even theoretically hope to estimate with the data at hand. Once we know what parameters or functionals are identified, we can discuss estimators, the properties of those estimators, and how to implement them. Before considering more specific models, we will briefly review several useful econometric and computational methods that will be useful in implementing many of the estimators we will see throughout the remainder of the course.

The core part of the course will focus on estimating models of strategic interaction. We will start by discussing identification and estimation of static games–both complete information and incomplete information games. This will include models with multiple equilibria, in which certain model features may be only partially identified, requiring the use of set estimation methods, which we will return to in more detail later. Dynamic single-agent models will be introduced next, leading up to a discussion of identification and estimation of dynamic games. Finally, we will end the course with a broader look at topics in partial identification.

Prerequisites

Economics 8732, 8831, and 8832 or equivalent with instructor consent.

Requirements

This course will primarily consist of discussions of individual research papers. These papers will be challenging, and so students should spend time reading them before class in order to better internalize the material and to facilitate discussion. Participation in class discussion will be a significant factor in determining the final grade.

There will be a small number of applied problem sets which will involve implementing estimators discussed in class. Familiarity with a software package such as Matlab or Gauss will be required to complete these assignments.

Each student will be expected to give a one hour presentation on one of the papers on the reading list at some point during the semester.

Students must also complete a final project, which may be either:

• an original research proposal—theoretical or applied—which will become part of the student’s dissertation, or
• a replication of an existing paper, possibly with a different dataset.

In lieu of a final exam, a write-up of the project must be submitted via email by the exam date (Tuesday, December 16, 2014).

Grading

Students will be graded based on the problem sets (40%), final project (30%), and class participation (30%). There will be no in-class exams. Collaboration in small groups of two or three is encouraged, both in discussing the papers and working on the problem sets. However, students must ultimately turn in their own code, write-up, and results.

Outline

1. Identification
2. Computation and Estimation of Nonlinear Models
3. Static Games
4. Single Agent Dynamic Models
5. Dynamic Games
6. Partial Identification

Tentative Schedule

Introduction

• August 28: Introduction.

Identification

• September 2: Koopmans (1949) and Hurwicz (1950).
• September 4: Matzkin (2013).
• September 9: Matzkin (1992).

Computation and Estimation of Nonlinear Models

• September 11: Train (2009, Chapter 10) and Hajivassiliou and Ruud (1994).
• September 16: Ackerberg (2009) (Hanbat Jeong).
• September 18: Ackerberg, Chen, and Hahn (2012) (Kai Yang). Problem Set 1 due.

Static Games

• September 23: Bresnahan and Reiss (1991, JoE).
• September 25: Tamer (2003).
• September 30: Bajari, Hong, and Ryan (2010) (Adam Smith).
• October 2: Ciliberto and Tamer (2009) (Tuo Liu).

Single Agent Dynamic Models

• October 7: Rust (1987, 1994).
• October 9: Hotz and Miller (1993) and Aguirregabiria and Mira (2002).
• October 14: Magnac and Thesmar (2002) (Wei Cheng).
• October 16: No class: reading day
• October 21: Arcidiacono and Miller (2011) (Shengjun Jiang). Project Proposal due.
• October 23: Bajari, Chu, Nekipelov, and Park (2013) (Wei Shi).
• October 28: Blevins (2014, QE).

Dynamic Games

• October 30: Pakes and McGuire (1994) (Arkady Konovalov).
• November 4: Aguirregabiria and Mira (2007) (Junqiushi Ren).
• November 6: No class: reading day
• November 11: No class: Veterans Day
• November 13: Bajari, Benkard, and Levin (2007) (Hyewon Kim).
• November 18: Arcidiacono, Bayer, Blevins, and Ellickson (2012).
• November 20: Blevins (2014).
• November 25: Aguirregabiria and Magesan (2012) (Luyao Zhang).
• November 27: No class: Thanksgiving

Partial Identification

• December 2: Manski and Tamer (2002) (Teng-Jen Chang).
• December 4: Chernozhukov, Hong, and Tamer (2007).
• December 9: Blevins (2013).

Final Project

• December 16: Final project due

Reading List

Below are lists of selected papers for each topic we will cover. For each topic, the primary papers which we will cover in class appear first, followed by other related papers in no particular order.

Identification

• Koopmans, T.C. (1949). Identification Problems in Economic Model Construction. Econometrica 17, 125–144.

• Koopmans, T.C. and O. Reiersøl (1950). The Identification of Structural Characteristics. Annals of Mathematical Statistics 21, 165–181.

• Hurwicz, L. (1950). Generalization of the Concept of Identification. In Statistical Inference in Dynamic Economic Models, Cowles Commission Monograph 10. New York: John Wiley and Sons.

• Koopmans, T.C. (1953). Identification Problems in Economic Model Construction. In Studies in Econometric Methods, Cowles Commission Monograph 14. New Haven: Yale University Press.

• Fisher, F.M. (1966) The Identification Problem in Econometrics, McGraw-Hill.

• Fisher, F.M. (1961) Identifiability Criteria in Nonlinear Systems. Econometrica 29, 574–590.

• Fisher, F.M. (1965). Identifiability Criteria in Nonlinear Systems: A Further Note. Econometrica 33, 197–205.

• Rothenberg, T.J. (1971). Identification in Parametric Models. Econometrica 39, 577–591.

• Matzkin, R. L. (1992). Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and The Binary Choice Models. Econometrica 60, 239–270.

• Matzkin, R. L. (2003). Nonparametric Estimation of Nonadditive Random Functions. Econometrica 71, 1339–1375.

• Matzkin, R. L. (2008). Identification in Nonparametric Simultaneous Equations. Econometrica 76, 945–978.

• Brown, B. W. (1983). The Identification Problem in Systems Nonlinear in the Variables. Econometrica 51, 175–196.

• Roehrig, C. S. (1988). Conditions for Identification in Nonparametric and Parametric Models. Econometrica 56, 433–447.

• Benkard, C. L., and S. Berry (2006). On the Nonparametric Identification of Non-Linear Simultaneous Equations Models: Comment on Brown (1983) and Rhoerig (1988). Econometrica 74, 1429–1440.

• Wald, A. (1950). Note on the Identification of Economic Relations. In Statistical Inference in Dynamic Economic Models, Cowles Commission Monograph 10. New York: John Wiley and Sons.

• Fisher, Franklin M. (1966). The Identification Problem in Econometrics. McGraw-Hill.

• Hsiao, Cheng (1983). Identification. In Handbook of Econometrics, Vol. 1. Amsterdam: North-Holland.

• Berry, S.T. and P. Haile (2011). Identification in a Class of Nonparametric Simultaneous Equations Models. Cowles Foundation Discussion Paper No. 1787.

• Berry, S.T., A. Gandhi, and P. Haile (2011). Connected Substitutes and Invertibility of Demand. NBER Working Paper 17193.

• Matzkin, R. L. (2013). Nonparametric Identification in Structural Economic Models Annual Review of Economics 5, 457–486.

Computation and Estimation of Nonlinear Models

• McFadden, D. (1989). A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration. Econometrica 57, 995–1026.

• Lerman, Steven R. and C. F. Manski (1981). On the Use of Simulated Frequencies to Approximate Choice Probabilities. In Structural Analysis of Discrete Data and Econometric Applications, eds. C. F. Manski and D. L. McFadden. Cambridge, MA: MIT Press.

• Lee, L.-F. (1995). Asymptotic Bias in Simulated Maximum Likelihood Estimation of Discrete Choice Models. Econometric Theory 11, 437–483.

• Hajivassiliou, V. and P. Ruud (1994). Classical Estimation Methods for LDV Models using Simulation. In Handbook of Econometrics, Volume 4, eds. R. Engle and D. McFadden. New York: Elsevier.

• Chernozhukov, V. and H. Hong (2003). An MCMC Approach to Classical Estimation. Journal of Econometrics 115, 293–346.

• Ackerberg, D.A. (2009). A New Use of Importance Sampling to Reduce Computational Burden in Simulation Estimation. Quantitative Marketing and Economics 7, 343–376.

• Train, Kenneth (2009). Discrete Choice Methods with Simulation, second edition. Cambridge University Press.

• Ackerberg, D.A., X. Chen and J. Hahn (2012). A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators. Review of Economics and Statistics 94, 481–498.

Static Games of Complete Information

• Berry, S. (1992). Estimation of a Model of Entry in the Airline Industry. Econometrica 60, 889–917.

• Bresnahan, T. F. and P. C. Reiss (1991). Empirical Models of Discrete Games. Journal of Econometrics 48, 57–81.

• Tamer, E. (2003). Incomplete Simultaneous Discrete Response Model with Multiple Equilibria. Review of Economic Studies 70, 147–165.

• Bajari, P., Hong, H. and Ryan, S. P. (2010). Identification and Estimation of a Discrete Game of Compete Information. Econometrica 78, 1529–1568.

• Bresnahan, T. F. and P. C. Reiss (1991). Entry and Competition in Concentrated Markets. Journal of Political Economy 99, 977–1009.

• Bresnahan, T. and P. C. Reiss (1990). Entry in Monopoly Markets. Review of Economic Studies 57, 531–553.

• Mazzeo, M. (2002). Product Choice and Oligopoly Market Structure. RAND Journal of Economics 33, 221–242.

• Jia, P. (2008). What Happens When Wal-Mart Comes to Town: An Empirical Analysis of the Discount Industry. Econometrica 76, 1263–1316.

Static Games of Incomplete Information

• Brock, W. A. and S. N. Durlauf (2001). Discrete Choice with Social Interactions. Review of Economic Studies 68, 235–260.

• Bajari, P., H. Hong, J. Krainer, and D. Nekipelov (2010). Estimating Static Models of Strategic Interactions. Journal of Business and Economic Statistics 28, 469–482.

• Bajari, P., H. Hong, and D. Nekipelov (2010). Game Theory and Econometrics: A Survey of Some Recent Research. Unpublished manuscript.

• Bayer, P. and Timmins, C. (2005). On the Equilibrium Properties of Locational Sorting Models. Journal of Urban Economics 57, 462–477.

• Seim, K. (2006). An Empirical Model of Firm Entry with Endogenous Product-Type Choices. RAND Journal of Economics 37, 619–640.

• Ackerberg, D. and G. Gowrisankaran (2006). Quantifying Equilibrium Network Externalities in the ACH Banking Industry. RAND Journal of Economics 37, 738–761.

• Ellickson, P. and S. Misra (2008). Supermarket Pricing Strategies. Marketing Science 27, 811–828.

• Haile, P. A., A. Hortaçsu and G. Kosenok (2008). On the Empirical Content of Quantal Response Equilibrium. American Economic Review 98, 180–200.

Static Games with Multiple Equilibria

• Berry, S. and E. Tamer (2006). Identification in Models of Oligopoly Entry. Advances in Economics and Econometrics, Blundell, Newey, and Persson (Eds.), Vol. 2, Ninth World Congress. Cambridge University Press.

• Ciliberto, F. and E. Tamer (2009). Market Structure and Multiple Equilibrium in Airline Markets. Econometrica 77, 1791–1828.

• Andrews, D.W.K. and P. Jia Barwick (2012). Inference for Parameters Defined by Moment Inequalities: A Recommended Moment Selection Procedure. Econometrica 80, 2805–2826.

• Beresteanu, A., F. Molinari, and I. Molchanov (2011). Sharp Identification Regions in Models with Convex Moment Predictions. CEMMAP Working Paper CWP25/10.

• Pakes, A., J. Porter, K. Ho, and J. Ishii (2014). Moment Inequalities and Their Application. Forthcoming in Econometrica.

• Galichon, A. and M. Henry (2011). Set Identification in Models with Multiple Equilibria. Review of Economic Studies, forthcoming.

• Sweeting, A. (2009). The Strategic Timing Incentives of Commercial Radio Stations: An Empirical Analysis Using Multiple Equilibria. RAND Journal of Economics 40, 710–742.

Sequential-Move Static Games

• Einav, L. (2010). Not All Rivals Look Alike: Estimating an Equilibrium Model of The Release Date Timing Game. Economic Inquiry 48, 369–390.

• Blevins, J. R. (2014). Structural Estimation of Sequential Games of Complete Information. OSU Working Paper 14–01.

Single-Agent Dynamic Models

• Pakes, A. (1986). Patents as Options: Some Estimates of the Value of Holding European Patent Stocks. Econometrica 54, 755–784.

• Rust, J. (1987). Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher. Econometrica 55, 999–1033.

• Hotz, V. J. and R. A. Miller (1993). Conditional Choice Probabilities and the Estimation of Dynamic Models. Review of Economic Studies 60, 397–429.

• Rust, J. (1994). Structural Estimation of Markov Decision Processes. In R. F. Engle and D. L. McFadden (Eds.), Handbook of Econometrics, Volume 4, Amsterdam. North Holland.

• Aguirregabiria, V. and Mira, P. (2002). Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models. Econometrica 70, 1519–1543.

• T. Magnac and D. Thesmar (2002). Identifying Dynamic Discrete Decision Processes. Econometrica 70, 801–816.

• Arcidiacono, P. and R.A. Miller (2011). Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity. Econometrica 79, 1823–1867.

• Aguirregabiria, V. (2010). Another Look at the Identification of Dynamic Discrete Decision Processes: An Application to Retirement Behavior. Journal of Business and Economic Statistics 28, 201–218.

• Gotz, G. A. and J. J. McCall (1980). Estimation in sequential decision-making models: A methodological note. Economics Letters 6, 131–136.

• Miller, R. A. (1984). Job matching and occupational choice. Journal of Political Economy 92, 1086–1120.

• Wolpin, K. I. (1984). An estimable dynamic stochastic model of fertility and child mortality. Journal of Political Economy 92, 852–874.

• Rust, J. (1988). Maximum Likelihood Estimation of Discrete Control Processes. SIAM Journal on Control and Optimimization 26, 1006–1024.

• Eckstein, Z. and K. I. Wolpin (1989). The Specification and Estimation of Dynamic Stochastic Discrete Choice Models: A Survey. Journal of Human Resources 24, 562–598.

• Hotz, V. J., R. A. Miller, S. Sanders, and J. Smith (1994). A Simulation Estimator for Dynamic Models of Discrete Choice. Review of Economic Studies 61, 265–289.

• Keane, M. P. and K. I. Wolpin (1997). The Career Decisions of Young Men. Journal of Political Economy 105, 473–522.

• Kasahara, H. and K. Shimotsu (2009). Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices. Econometrica 77, 135–175.

• Imai, S., N. Jain, and A. Ching (2009). Bayesian Estimation of Dynamic Discrete Choice Models. Econometrica 77, 1865–1899.

• Norets, A. (2009). Inference in Dynamic Discrete Choice Models with Serially Correlated Unobserved State Variables. Econometrica 77, 1665–1682.

• Kasahara, H. and K. Shimotsu (2012). Sequential Estimation of Structural Models with a Fixed Point Constraint. Econometrica 80, 2303–2319.

• Shum, M. and Y. Hu (2012). Nonparametric Identification of Dynamic Models with Unobserved State Variables. Journal of Econometrics 171, 32–44.

• Shum, M. and Y. Hu (2013). Identifying Dynamic Games with Serially-Correlated Unobservables. Advances in Econometrics 31. Emerald Publishing.

• Norets, A. and X. Tang (2010). Semiparametric Inference in Dynamic Binary Choice Models Review of Economic Studies 81, 1229–1262.

• Robin, J.-M. and F. Postel-Vinay (2002). Equilibrium Wage Dispersion with Worker and Employer Heterogeneity. Econometrica 70, 2295–2350.

• Bajari, P., S. Chu, D. Nekipelov, and M. Park (2013). Semiparametric Estimation of a Finite Horizon Dynamic Discrete Choice Model with Application to Subprime Mortgage Default. Unpublished manuscript, University of Washington.

• Blevins, J. R. (2011). Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models. OSU Working Paper 11–01.

• Blevins, J. R. (2014). Nonparametric Identification of Dynamic Decision Processes with Discrete and Continuous Choices. Quantitative Economics, Forthcoming.

Dynamic Games

• Pesendorfer, M. and P. Schmidt-Dengler (2008). Asymptotic Least Squares Estimators for Dynamic Games. Review of Economic Studies 75, 901–928.

• Bajari, P., V. Chernozhukov, H. Hong, and D. Nekipelov (2009). Nonparametric and Semiparametric Analysis of a Dynamic Discrete Game. Unpublished manuscript, Stanford University.

• Bajari, P., C. L. Benkard, and J. Levin (2007). Estimating Dynamic Models of Imperfect Competition. Econometrica 75, 1331–1370.

• Aguirregabiria, V. and P. Mira (2010). Dynamic discrete choice structural models: A survey. Journal of Econometrics 156, 38–67.

• Aguirregabiria, V. and P. Mira (2007). Sequential estimation of dynamic discrete games. Econometrica 75, 1–53.

• Arcidiacono, P., P. Bayer, J.R. Blevins, and P.B. Ellickson (2012). Estimation of Dynamic Discrete Choice Models in Continuous Time. NBER Working Paper 18449.

• Blevins, J.R. (2014). Identification and Estimation of Continuous Time Dynamic Games. Working paper.

• Ericson, R. and A. Pakes (1995). Markov-Perfect Industry Dynamics: A Framework for Empirical Work. Review of Economics and Statistics 62, 53–82.

• Pakes, A. and P. McGuire (1994). Computing Markov-perfect Nash equilibria: Numerical Implications of a Dynamic Differentiated Product Model. RAND Journal of Economics 25, 555–589.

• Rust, J. (1994). Estimation of Dynamic Structural Models, Problems and Prospects: Discrete Decision Processes. In C. Sims (Ed.), Advances in Econometrics: Sixth World Congress, Volume 2. Cambridge University Press.

• Benkard, L. (2004). A Dynamic Analysis of the Market for Wide-Bodied Commercial Aircraft. Review of Economic Studies 71, 581–611.

• Pakes, A., M. Ostrovsky, and S. Berry (2007) Simple Estimators for the Parameters of Discrete Dynamic Games, with Entry/Exit Examples. RAND Journal of Economics 38, 373–399.

• Ryan, S. P. (2012). The Costs of Environmental Regulation in a Concentrated Industry. Econometrica 80, 1019–1061.

• Beresteanu, A., P. B. Ellickson, and S. Misra (2011). The Dynamics of Retail Oligopoly. Unpublished manuscript, University of Rochester.

• Collard-Wexler, A. (2013). Demand Fluctuations in the Ready-Mix Concrete Industry. Econometrica 81, 1003–1037.

• Macieira, J. (2009). A Dynamic Model of Innovation and Technological Competition in the Supercomputer Industry. Unpublished manuscript, Virginia Tech.

• Doraszelski, U. and K. L. Judd (2012). Avoiding the curse of dimensionality in dynamic stochastic games. Quantitative Economics 3, 53–93.

• Grieco, P. L. E. (2011). Discrete Games with Flexible Information Structures: An Application to Local Grocery Markets. RAND Journal of Economics 45, 303–340.

• Doraszelski, U. and A. Pakes (2007). A framework for applied dynamic analysis in IO. In M. Armstrong and R. H. Porter (Eds.), Handbook of Industrial Organization, Volume 3. North Holland.

• Pakes, A. and P. McGuire (2001). Stochastic Algorithms, Symmetric Markov Perfect Equilibrium, and the ‘Curse’ of Dimensionality. Econometrica 69, 1261–1281.

• Weintraub, G. Y., C. L. Benkard, and B. Van Roy (2008). Markov perfect industry dynamics with many firms. Econometrica 76, 1375–1411.

• Su, C.-L. and K. L. Judd (2012). Constrained Optimization Approaches to Estimation of Structural Models. Econometrica 80, 2213–2230.

• Jofre-Bonet, M. and M. Pesendorfer (2003). Estimation of a Dynamic Auction Game. Econometrica 71, 1443–1489.

• Srisuma, S. (2010). Estimation of Structural Optimization Models: A Note on Identification. Unpublished manuscript, London School of Economics.

• Hopenhayn, H. A. (1992) Entry, Exit, and firm Dynamics in Long Run Equilibrium. Econometrica 60, 1127–1150.

• Aguirregabiria, V. and A. Magesan (2012). Identification and Estimation of Dynamic Discrete Games when Players’ Beliefs are not in Equilibrium. Unpublished manuscript, University of Toronto.

Partial Identification

• Manski, C. F. and E. Tamer (2002). Inference on Regressions with Interval Data on a Regressor or Outcome. Econometrica 70, 519–546.

• Chernozhukov, V., H. Hong, E. Tamer (2007). Estimation and Confidence Regions for Parameter Sets in Econometric Models. Econometrica 75, 1243–1284.

• Tamer, E. (2010). Partial Identification in Econometrics. Annual Review of Economics 2, 167–195.

• Manski, C. (1990). Nonparametric Bounds on Treatment Effects. American Economic Review 80, Papers and Proceedings, 319–323.

• Magnac, T. and E. Maurin (2008). Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data. Review of Economic Studies 75, 835–864.

• Imbens, G., and C. Manski (2004). Confidence Intervals for Partially Identified Parameters. Econometrica 72, 1845–1857.

• Honoré, B. E. and E. Tamer (2006). Bounds on Parameters in Dynamic Discrete Choice Models. Econometrica 74, 611–629

• Beresteanu, A. and F. Molinari (2008). Asymptotic Properties for a Class of Partially Identified Models. Econometrica 76, 763–814.

• Stoye, J. (2009). More on Confidence Intervals for Partially Identified Parameters. Econometrica 77, 1299–1315.

• Andrews, D. W. K. and P. Jia (2008). Inference for Parameters Defined by Moment Inequalities: A Recommended Moment Selection Procedure. Cowles Foundation Discussion Paper No. 1676.

• Andrews, D. W. K. and G. Soares (2010). Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection. Econometrica 78, 119–157.

• Blevins, J. R. (2013). Non-Standard Rates of Convergence of Criterion-Function-Based Set Estimators. OSU Working Paper 13–02.

• Blevins, J. R. (2011). Partial Identification and Inference in Binary Choice and Duration Panel Data Models. Unpublished manuscript, Ohio State University.