Advanced Econometrics I

Course Information

Meeting Time: Tuesdays and Thursdays, 11:10 AM–12:30 PM.
Location: Online (Zoom)
Instructor: Jason Blevins.
Office hours: By appointment on Tuesdays 12:45–2:00 PM.
Website: https://jblevins.org/courses/econ8830s21/

Description

The main goals of this course are (1) to introduce students to a set of core methods in microeconometrics, along with the necessary computational skills to implement them, which they can apply to their own dissertation research and (2) to survey recent research which develops or applies these methods. Topics covered may include identification, discrete choice models, quantile regression, duration models, semiparametric methods, nonparametric methods, and set estimation, simulation methods, optimization methods, and estimation of structural models such as static games, single-agent dynamic discrete choice models, and dynamic discrete choice games.

Prerequisites

Economics 8731 and 8732, or equivalent with instructor consent.

Texts

There are no required texts for this course, however, the following texts may be useful for reference and futher reading:

• Amemiya, T. (1985). Advanced Econometrics. Harvard University Press.
• Cameron, A. C. and P. K. Trivedi (2005). Microeconometrics: Methods and Applications. Cambridge University Press.
• Davidson, J. (1994). Stochastic Limit Theory: An Introduction for Econometricians.
• Engle, R. and D. McFadden (Eds.) (1994). Handbook of Econometrics, Volume 4. North Holland.
• Horowitz, J. L. (2010). Semiparametric and Nonparametric Methods in Econometrics. Springer.
• Train, K. (2009). Discrete Choice Methods with Simulation (2nd edition). Cambridge University Press.
• Van der Vaart, A. W. (1998). Asymptotic Statistics. Cambridge University Press.

Requirements

This course will focus on a collection of topics in microeconometrics via lectures, notes, research papers, and problem sets. Students will complete three problem sets throughout the semester to implement methods discussed in class and give a presentation on a paper from the reading list. We will periodically discuss the implementation of these methods in class, using the problem set questions as motivation. At the end of the semester, second-year students will give a short presentations of their econometrics field paper proposals.

Schedule & Topics

Below is a tentative schedule and list of topics, subject to change. Specific readings, sub-topics, and problem sets will be added as the semester progresses.

Estimation of Nonlinear, Parametric Models

• January 12: Introduction

• January 14: Consistency and asymptotic normality of extremum estimators

  • Newey and McFadden (1994)
  • Amemiya (1985, Chapter 4) (*)
  • Review of Basic Asymptotic Theory and Asymptotic Distribution of M-estimator - Lecture notes from Han Hong of Stanford University.
  • Consistency of Extremum Estimators and Asymptotic Normality of Extremum Estimators - Lecture notes by Xiaoxia Shi of the University of Wisconsin.
• January 19: Numerical methods for nonlinear optimization
  • Amemiya (1985, Chapter 4) (*)
  • Train (2009, Chapter 8)
  • Goffe, Ferrier, and Rogers (1994)
  • Storn and Price (1997)
• January 21: Parametric discrete choice models
  • Amemiya (1985, Chapter 9) (*)
  • Train (2009, Chapters 1–3) (*)
  • Problem Set 1 Assigned.
• January 26: Quantile regression
  • Koenker and Bassett (1978)
  • Koenker and Hallock (2001) (*)
• January 28 & February 2: Numerical optimization in Matlab
  • Matlab: Unconstrained Nonlinear Optimization Algorithms
  • Matlab: fminunc
  • Matlab: fminsearch Algorithm
  • Matlab: linprog
  • Matlab: Simulated Annealing
  • Discussion of Problem Set 1.
• February 4: Simulation Methods and Simulation-Based Estimation
  • Train (2009, Chapters 9 and 10) (*)
  • Hajivassiliou and Ruud (1994)
  • McFadden (1989)

Nonparametric Methods & Identification

• February 9: Nonparametric estimation
  • Kernel density/distribution estimation
  • Kernel regression
  • Cross validation
  • Problem Set 1 Due
  • Problem Set 2 Assigned
• February 11: Nonparametric identification
  • Matzkin (2013) (Annual Reviews) (*)
  • Hurwicz (1950)
  • Koopmans (1949)
  • Matzkin (2007) (Handbook of Econometrics)
  • Matzkin (1992) (binary response and binary choice)
  • Matzkin (2003) (nonadditive random functions)
• February 16: Partial identification
  • Manski and Tamer (2002) (regressions with interval data) (*)
  • Chernozhukov, Hong, and Tamer (2007) (critrion-function-based estimation) (*)
  • Tamer (2010) (Annual Reviews)
  • Honoré and Tamer (2006) (random effects dynamic discrete choice)

Econometrics of Static Games

• February 18: Entry games
  • Bresnahan and Reiss (1991) (*)

• February 23: No Class: Spring Break (1)

• February 25: Minhee Lee
  • Andrews and Soares (2010)
• March 2: Simultaneous bivariate response
  • Tamer (2003) (*)
  • Problem Set 2 Due
• March 4: Shi Ryoung Chang
  • Zhu and Singh (2009)
• March 9: Ilhwan Song
  • Kim and Singleton (2012)
• March 11: Pingchuan Ma
  • Sweeting (2009)

Dynamic Discrete Choice Models

• March 16: Rust model
  • Rust (1987) (*)
  • Rust (1988)
  • Rust (1994)
  • Problem Set 3 Assigned
• March 18: CCP estimation of Single-Agent Models
  • Hotz and Miller (1993)
  • Aguirregabiria and Mira (2002)
• March 23: Zheyu Ni
  • Arcidiacono and Miller (2011)
• March 25: Robert Daniel
  • Aguirregabiria (2010)
• March 30: Nested Pseudo-Likelihood Estimation of Games
  • Aguirregabiria, V. and P. Mira (2007)

• April 1: No Class, Spring Break (2)

• April 6: Yanli Lin
  • Aguirregabiria, Gu, Luo, and Mira (2020)
• April 8: Efficient Pseudo Likelihood
  • Dearing and Blevins (2019)
• April 13 & 15: Continuous time dynamic discrete choice
  • Arcidiacono, Bayer, Blevins, and Ellickson (2016) (*)
  • Blevins (2018)
  • Problem Set 3 Due

Field Paper Presentations

Approximately 30 minutes each followed by 10 minutes for comments and discussion.

• April 20: Field Paper Presentations Day 1

• April 22: Field Paper Presentations Day 2

Reading List

Below are lists of additional papers for each topic we will cover for further reading.

Simulation, Computation, and Estimation of Nonlinear Models

• Ackerberg, D. A. (2009). A New Use of Importance Sampling to Reduce Computational Burden in Simulation Estimation. Quantitative Marketing and Economics 7, 343–376.
• 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.
• Chernozhukov, V. and H. Hong (2003). An MCMC Approach to Classical Estimation. Journal of Econometrics 115, 293–346.
• Doucet, A., N. de Freitas, and N. Gordon (2001). An Introduction to Sequential Monte Carlo Methods.
• Eisenhauer, P., J. J. Heckman, and S. Mosso (2015). Estimation of Dynamic Discrete Choice Models by Maximum Likelihood and the Simulated Method of Moments International Economic Review 56, 331–357.
• Goffe, W. L., G. D. Ferrier, and J. Rogers (1994). Global optimization of statistical functions with simulated annealing. Journal of Econometrics 60, 65–99.
• 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.
• Jun, S. J., J. Pinkse, Y. Wan (2015). Classical Laplace estimation for \(\sqrt[3]{n}\)-consistent estimators: Improved convergence rates and rate-adaptive inference. Journal of Econometrics 187, 201–216.
• Kormilitsina, A. and D. Nekipelov (2012). Approximation Properties of Laplace-Type Estimators. in Advances in Econometrics 28, 291–318.
• Lee, L.-F. (1995). Asymptotic Bias in Simulated Maximum Likelihood Estimation of Discrete Choice Models. Econometric Theory 11, 437–483.
• 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.
• McFadden, D. (1989). A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration. Econometrica 57, 995–1026.
• Newey, W. and D. McFadden (1994). Large sample estimation and hypothesis testing. In Handbook of Econometrics, Volume 4, eds. R. Engle and D. McFadden. New York: Elsevier.
• Storn, R. and K. Price (1997). Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Jornal of Global Optimization 11, 341–359.
• Price, K., R. Storn, and J. Lampinen (2005). Differential Evolution: A Practical Approach to Global Optimization. Springer.
• Train, Kenneth (2009). Discrete Choice Methods with Simulation, second edition. Cambridge University Press.

Nonparametric and Semiparametric Methods

• Koenker, R. and G. Bassett, Jr. (1978). Regression Quantiles. Econometrica 46, 33–50.
• Koenker, R. and K. F. Hallock (2001). Quantile Regression. Journal of Economic Perspectives 15, 143–156.

Discrete Choice Models

• Chen, S., S. Khan, X. Tang (2016). Informational content of special regressors in heteroskedastic binary response models. Journal of Econometrics 193, 162–182.
• Manski, C. (1988) Identification of Binary Response Models. Journal of the American Statistical Association 83, 729–738.

Identification

• 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.
• Berry, S.T., A. Gandhi, and P. Haile (2011). Connected Substitutes and Invertibility of Demand. NBER Working Paper 17193.
• Berry, S.T. and P. Haile (2011). Identification in a Class of Nonparametric Simultaneous Equations Models. Cowles Foundation Discussion Paper No. 1787.
• Blevins, J. R. (2016). Identifying Restrictions for Finite Parameter Continuous Time Models with Discrete Time Data. Forthcoming in Econometric Theory.
• Brown, B. W. (1983). The Identification Problem in Systems Nonlinear in the Variables. Econometrica 51, 175–196.
• Chamberlain, G. (2010). Binary Response Models for Panel Data: Identification and Information. Econometrica 78, 159–168.
• 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.
• Fisher, F.M. (1966) The Identification Problem in Econometrics, McGraw-Hill.
• Hsiao, Cheng (1983). Identification. In Handbook of Econometrics, Vol. 1. Amsterdam: North-Holland.
• 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. (1949). Identification Problems in Economic Model Construction. Econometrica 17, 125–144.
• Koopmans, T.C. (1953). Identification Problems in Economic Model Construction. In Studies in Econometric Methods, Cowles Commission Monograph 14. New Haven: Yale University Press.
• Koopmans, T.C. and O. Reiersøl (1950). The Identification of Structural Characteristics. Annals of Mathematical Statistics 21, 165–181.
• 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. (2007). Nonparametric Identification In Handbook of Econometrics, Volume 6B, eds. J. J. Heckman and E. E. Leamer.
• Matzkin, R. L. (2008). Identification in Nonparametric Simultaneous Equations. Econometrica 76, 945–978.
• Roehrig, C. S. (1988). Conditions for Identification in Nonparametric and Parametric Models. Econometrica 56, 433–447.
• Rothenberg, T.J. (1971). Identification in Parametric Models. Econometrica 39, 577–591.
• Matzkin, R. L. (2013). Nonparametric Identification in Structural Economic Models Annual Review of Economics 5, 457–486.
• 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.

Static Games of Complete Information

• Bajari, P., Hong, H. and Ryan, S. P. (2010). Identification and Estimation of a Discrete Game of Compete Information. Econometrica 78, 1529–1568.
• Berry, S. (1992). Estimation of a Model of Entry in the Airline Industry. Econometrica 60, 889–917.
• Bresnahan, T. and P. C. Reiss (1990). Entry in Monopoly Markets. Review of Economic Studies 57, 531–553.
• Bresnahan, T. F. and P. C. Reiss (1991). Empirical Models of Discrete Games Journal of Econometrics 48, 57–81.
• Bresnahan, T. F. and P. C. Reiss (1991). Entry and Competition in Concentrated Markets. Journal of Political Economy 99, 977–1009.
• Jia, P. (2008). What Happens When Wal-Mart Comes to Town: An Empirical Analysis of the Discount Industry. Econometrica 76, 1263–1316.
• Mazzeo, M. (2002). Product Choice and Oligopoly Market Structure. RAND Journal of Economics 33, 221–242.
• Tamer, E. (2003). Incomplete Simultaneous Discrete Response Model with Multiple Equilibria. Review of Economic Studies 70, 147–165.

Static Games of Incomplete Information

• Ackerberg, D. and G. Gowrisankaran (2006). Quantifying Equilibrium Network Externalities in the ACH Banking Industry. RAND Journal of Economics 37, 738–761.
• 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.
• Brock, W. A. and S. N. Durlauf (2001). Discrete Choice with Social Interactions. Review of Economic Studies 68, 235–260.
• 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.
• Seim, K. (2006). An Empirical Model of Firm Entry with Endogenous Product-Type Choices. RAND Journal of Economics 37, 619–640.
• Zhu, T. and V. Singh (2009). Spatial competition with endogenous location choices: An application to discount retailing. Quantitative Marketing and Economics 7, 1–35.

Static Games with Multiple Equilibria

• 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.
• 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.
• Galichon, A. and M. Henry (2011). Set Identification in Models with Multiple Equilibria. Review of Economic Studies, forthcoming.
• Pakes, A., J. Porter, K. Ho, and J. Ishii (2014). Moment Inequalities and Their Application. Forthcoming in Econometrica.
• de Paula, Á. (2013). Econometric Analysis of Games with Multiple Equilibria Annual Review of Economics 5, 107–31.
• 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

• Blevins, J. R. (2015). Structural Estimation of Sequential Games of Complete Information. Economic Inquiry 53, 791–811.
• Ciliberto, F. and Z. Zhang (2014). Multiple Equilibria and Deterrence in Airline Markets. Unpublished manuscript, University of Virginia.
• Einav, L. (2010). Not All Rivals Look Alike: Estimating an Equilibrium Model of The Release Date Timing Game. Economic Inquiry 48, 369–390.

Single-Agent Dynamic Models

• 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.
• 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.
• Arcidiacono, P. and R.A. Miller (2011). Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity. Econometrica 79, 1823–1867.
• Bajari, P., S. Chu, D. Nekipelov, and M. Park (2016). Identification and Semiparametric Estimation of a Finite Horizon Dynamic Discrete Choice Model with a Terminating Action. Quantitative Marketing and Economics 14, 271–323.
• Blevins, J. R. (2014). Nonparametric Identification of Dynamic Decision Processes with Discrete and Continuous Choices. Quantitative Economics 5, 531–554.
• Blevins, J. R. (2015). Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models. Forthcoming at Journal of Applied Econometrics.
• Connault, B. (2014). Hidden Rust Models. Unpublished manuscript, Princeton University.
• 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.
• Gotz, G. A. and J. J. McCall (1980). Estimation in sequential decision-making models: A methodological note. Economics Letters 6, 131–136.
• Hotz, V. J. and R. A. Miller (1993). Conditional Choice Probabilities and the Estimation of Dynamic Models. Review of Economic Studies 60, 397–429.
• 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.
• Imai, S., N. Jain, and A. Ching (2009). Bayesian Estimation of Dynamic Discrete Choice Models. Econometrica 77, 1865–1899.
• Magnac, T. and D. Thesmar (2002). Identifying Dynamic Discrete Decision Processes. Econometrica 70, 801–816.
• Kasahara, H. and K. Shimotsu (2009). Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices. Econometrica 77, 135–175.
• Kasahara, H. and K. Shimotsu (2012). Sequential Estimation of Structural Models with a Fixed Point Constraint. Econometrica 80, 2303–2319.
• Keane, M. P. and K. I. Wolpin (1997). The Career Decisions of Young Men. Journal of Political Economy 105, 473–522.
• Miller, R. A. (1984). Job matching and occupational choice. Journal of Political Economy 92, 1086–1120.
• Norets, A. (2009). Inference in Dynamic Discrete Choice Models with Serially Correlated Unobserved State Variables. Econometrica 77, 1665–1682.
• Norets, A. and X. Tang (2010). Semiparametric Inference in Dynamic Binary Choice Models Review of Economic Studies 81, 1229–1262.
• Pakes, A. (1986). Patents as Options: Some Estimates of the Value of Holding European Patent Stocks. Econometrica 54, 755–784.
• Robin, J.-M. and F. Postel-Vinay (2002). Equilibrium Wage Dispersion with Worker and Employer Heterogeneity. Econometrica 70, 2295–2350.
• Rust, J. (1987). Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher. Econometrica 55, 999–1033.
• Rust, J. (1988). Maximum Likelihood Estimation of Discrete Control Processes. SIAM Journal on Control and Optimimization 26, 1006–1024.
• 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.
• 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.
• Wolpin, K. I. (1984). An estimable dynamic stochastic model of fertility and child mortality. Journal of Political Economy 92, 852–874.

Dynamic Games

• 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.
• Aguirregabiria, V. and P. Mira (2007). Sequential estimation of dynamic discrete games. Econometrica 75, 1–53.
• Aguirregabiria, V. and P. Mira (2010). Dynamic discrete choice structural models: A survey. Journal of Econometrics 156, 38–67.
• 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.
• Bajari, P., C. L. Benkard, and J. Levin (2007). Estimating Dynamic Models of Imperfect Competition. Econometrica 75, 1331–1370.
• Bajari, P., V. Chernozhukov, H. Hong, and D. Nekipelov (2009). Nonparametric and Semiparametric Analysis of a Dynamic Discrete Game. Unpublished manuscript, Stanford University.
• Benkard, L. (2004). A Dynamic Analysis of the Market for Wide-Bodied Commercial Aircraft. Review of Economic Studies 71, 581–611.
• Beresteanu, A., P. B. Ellickson, and S. Misra (2011). The Dynamics of Retail Oligopoly. Unpublished manuscript, University of Rochester.
• Blevins, J.R. (2018). Identification and Estimation of Continuous Time Dynamic Discrete Choice Games. Working paper.
• Collard-Wexler, A. (2013). Demand Fluctuations in the Ready-Mix Concrete Industry. Econometrica 81, 1003–1037.
• Doraszelski, U. and K. L. Judd (2012). Avoiding the curse of dimensionality in dynamic stochastic games. Quantitative Economics 3, 53–93.
• 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.
• Ericson, R. and A. Pakes (1995). Markov-Perfect Industry Dynamics: A Framework for Empirical Work. Review of Economics and Statistics 62, 53–82.
• Grieco, P. L. E. (2011). Discrete Games with Flexible Information Structures: An Application to Local Grocery Markets. RAND Journal of Economics 45, 303–340.
• Hopenhayn, H. A. (1992) Entry, Exit, and firm Dynamics in Long Run Equilibrium. Econometrica 60, 1127–1150.
• Jofre-Bonet, M. and M. Pesendorfer (2003). Estimation of a Dynamic Auction Game. Econometrica 71, 1443–1489.
• Pakes, A. and P. McGuire (2001). Stochastic Algorithms, Symmetric Markov Perfect Equilibrium, and the ‘Curse’ of Dimensionality. Econometrica 69, 1261–1281.
• 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.
• 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.
• Pesendorfer, M. and P. Schmidt-Dengler (2008). Asymptotic Least Squares Estimators for Dynamic Games. Review of Economic Studies 75, 901–928.
• 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.
• Ryan, S. P. (2012). The Costs of Environmental Regulation in a Concentrated Industry. Econometrica 80, 1019–1061.
• Su, C.-L. and K. L. Judd (2012). Constrained Optimization Approaches to Estimation of Structural Models. Econometrica 80, 2213–2230.
• Weintraub, G. Y., C. L. Benkard, and B. Van Roy (2008). Markov perfect industry dynamics with many firms. Econometrica 76, 1375–1411.

Partial Identification

• 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.
• Beresteanu, A. and F. Molinari (2008). Asymptotic Properties for a Class of Partially Identified Models. Econometrica 76, 763–814.
• Blevins, J. R. (2011). Partial Identification and Inference in Binary Choice and Duration Panel Data Models. Unpublished manuscript, Ohio State University.
• Blevins, J. R. (2015). Non-Standard Rates of Convergence of Criterion-Function-Based Set Estimators. Econometrics Journal 18, 172–199.
• Chernozhukov, V., H. Hong, E. Tamer (2007). Estimation and Confidence Regions for Parameter Sets in Econometric Models. Econometrica 75, 1243–1284.
• Honoré, B. E. and E. Tamer (2006). Bounds on Parameters in Dynamic Discrete Choice Models. Econometrica 74, 611–629
• Imbens, G., and C. Manski (2004). Confidence Intervals for Partially Identified Parameters. Econometrica 72, 1845–1857.
• 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.
• Manski, C. F. and E. Tamer (2002). Inference on Regressions with Interval Data on a Regressor or Outcome. Econometrica 70, 519–546.
• Stoye, J. (2009). More on Confidence Intervals for Partially Identified Parameters. Econometrica 77, 1299–1315.
• Tamer, E. (2010). Partial Identification in Econometrics. Annual Review of Economics 2, 167–195.