# 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

- Identification
- Computation and Estimation of Nonlinear Models
- Static Games
- Single Agent Dynamic Models
- Dynamic Games
- 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, 611–629Beresteanu, 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.