Advanced Econometrics I

Course Information

Meeting Time: Tuesdays and Thursdays, 11:10 AM–12:30 PM. Location: Baker Systems 272. Instructor: Jason Blevins. Office hours: By appointment on Tuesdays 1:00–2:30 PM. Website: https://jblevins.org/courses/econ8830s25/

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. (2009). 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 7: Introduction

• January 9: 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 14: Numerical methods for nonlinear optimization

  • Amemiya (1985, Chapter 4) (*)
  • Train (2009, Chapter 8)
  • Goffe, Ferrier, and Rogers (1994)
  • Storn and Price (1997)

• January 16: Parametric discrete choice models

  • Amemiya (1985, Chapter 9) (*)
  • Train (2009, Chapters 1–3) (*)
  • Problem Set 1 Assigned.

• January 21: Quantile regression

  • Koenker and Bassett (1978)
  • Koenker and Hallock (2001) (*)

• January 23 & 28: Numerical optimization in Matlab

  • Matlab: Unconstrained Nonlinear Optimization Algorithms
  • Matlab: fminunc
  • Matlab: fminsearch Algorithm
  • Matlab: linprog
  • Matlab: Simulated Annealing
  • Discussion of Problem Set 1.

• January 30: Simulation Methods and Simulation-Based Estimation

  • McFadden (1989)
  • Hajivassiliou and Ruud (1994)
  • Train (2009, Chapters 9 and 10) (*)

Nonparametric Methods & Identification

• February 4: Nonparametric estimation

  • Hastie, T., R. Tibshirani, J. Friedman (2008). Chapter 6: Kernel Smoothing Methods. (*)
  • Horowitz (2009). Appendix: Nonparametric Density Estimation and Nonparametric Regression.

• February 6: Nonparametric identification of structural models

  • Koopmans (1949) (Econometrica)
  • Hurwicz (1950) (Cowles Commission Monograph)
  • Matzkin (1992) (Econometrica - binary response and binary choice)
  • Matzkin (2003) (Econometrica - nonadditive random functions)
  • Matzkin (2007) (Handbook of Econometrics)
  • Matzkin (2013) (Annual Reviews) (*)

Static Discrete Choice Models

• February 11: TBD: Student Presentation

  • Problem Set 1 Due
  • Problem Set 2 Assigned

• February 13: TBD: Student Presentation

• February 18: TBD: Student Presentation

Econometrics of Static Games

• February 20: Entry games

  • Bresnahan and Reiss (1991) (*)

• February 25: Simultaneous bivariate response

  • Tamer (2003) (*)

Dynamic Discrete Choice Models

• February 27: Rust model

  • Rust (1987) (Econometrica) (*)
  • Rust (1988) (SIAM J. Control & Optimization)
  • Rust (1994) (Handbook of Econometrics)
  • Rust (1996) (Handbook of Computational Economics)

• March 4: CCP estimation of Single-Agent Models

  • Hotz and Miller (1993)
  • Aguirregabiria and Mira (2002) (*)

• March 6: TBD: Student Presentation

  • Problem Set 2 Due
  • Problem Set 3 Assigned

• March 11 & 13: No Class, Spring Break

• March 18: TBD: Student Presentation

• March 20: TBD: Student Presentation

• March 25: TBD: Student Presentation

• March 27: TBD: Student Presentation

Dynamic Discrete Choice Games

• April 1: Nested Pseudo-Likelihood Estimation of Games

  • Aguirregabiria, V. and P. Mira (2007)

• April 3: Efficient pseudo likelihood (EPL) estimation of dynamic games

  • Dearing and Blevins (2023)

• April 8 & 10: Continuous time dynamic discrete choice

  • Arcidiacono, Bayer, Blevins, and Ellickson (2016) (*)
  • Blevins (2022)
  • Blevins and Kim (2024)

Field Paper Presentations

Approximately 20 minutes each followed by 5 minutes for comments and discussion.

• April 15: Field Paper Presentations Day 1 (TBD)

• April 17: Field Paper Presentations Day 2 (TBD)

  • Problem Set 3 Due

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.
• 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.
• 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

• Hastie, T., R. Tibshirani, J. Friedman (2008). The Elements of Statistical Learning, 2nd ed. Springer.

• Horowitz, J.L. (2009). Semiparametric and Nonparametric Methods in Econometrics. Springer.

Discrete Choice Models

• Barseghyan, L., F. Molinari, and M. Thirkettle (2021). Discrete Choice under Risk with Limited Consideration. American Economic Review 111, 1972–2006.
• Chen, S., S. Khan, X. Tang (2016). Informational content of special regressors in heteroskedastic binary response models. Journal of Econometrics 193, 162–182.
• Davis, P. (2006). Spatial Competition in Retail Markets: Movie Theaters. RAND Journal of Economics 37, 964–982.
• Manski, C. (1988) Identification of Binary Response Models. Journal of the American Statistical Association 83, 729–738.
• Wei, Y. and N. Malik (2023). Unstructured Data, Econometric Models, and Estimation Bias. Working Paper.

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

• Ackerberg, D. and G. Gowrisankaran (2006). Quantifying Equilibrium Network Externalities in the ACH Banking Industry. RAND Journal of Economics 37, 738–761.
• 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.
• 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.
• Bajari, P., H. Hong and S.P. Ryan (2010). Identification and Estimation of a Discrete Game of Compete Information. Econometrica 78, 1529–1568.
• Bayer, P. and Timmins, C. (2005). On the Equilibrium Properties of Locational Sorting Models. Journal of Urban Economics 57, 462–477.
• Beresteanu, A., F. Molinari, and I. Molchanov (2011). Sharp Identification Regions in Models with Convex Moment Predictions. CEMMAP Working Paper CWP25/10.
• Berry, S. (1992). Estimation of a Model of Entry in the Airline Industry. Econometrica 60, 889–917.
• 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.
• Blevins, J. R. (2015). Structural Estimation of Sequential Games of Complete Information. Economic Inquiry 53, 791–811.
• 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.
• Brock, W. A. and S. N. Durlauf (2001). Discrete Choice with Social Interactions. Review of Economic Studies 68, 235–260.
• Ciliberto, F. and E. Tamer (2009). Market Structure and Multiple Equilibrium in Airline Markets. Econometrica 77, 1791–1828.
• Ciliberto, F. and Z. Zhang (2017). Multiple Equilibria and Deterrence in Airline Markets. Economic Inquiry 55, 319–338.
• Einav, L. (2010). Not All Rivals Look Alike: Estimating an Equilibrium Model of The Release Date Timing Game. Economic Inquiry 48, 369–390.
• Ellickson, P. and S. Misra (2008). Supermarket Pricing Strategies. Marketing Science 27, 811–828.
• Fan, Y. (2013). Ownership Consolidation and Product Characteristics: A Study of the US Daily Newspaper Market. American Economic Review 103, 1598–1628.
• Galichon, A. and M. Henry (2011). Set Identification in Models with Multiple Equilibria. Review of Economic Studies, forthcoming.
• Haile, P. A., A. Hortaçsu and G. Kosenok (2008). On the Empirical Content of Quantal Response Equilibrium. American Economic Review 98, 180–200.
• Ho, K. and R.S. Lee (2017). Insurer Competition in Health Care Markets. Econometrica 85, 379–417.
• Jia, P. (2008). What Happens When Wal-Mart Comes to Town: An Empirical Analysis of the Discount Industry. Econometrica 76, 1263–1316.
• Khan and Nekipelov (2019). [Information structure and statistical information in discrete response models]]khan-nekipelov–2019. Quantitative Economics 9.
• Mazzeo, M. (2002). Product Choice and Oligopoly Market Structure. RAND Journal of Economics 33, 221–242.
• Pakes, A., J. Porter, K. Ho, and J. Ishii (2015). Moment Inequalities and Their Application. Econometrica 83, 315–334.
• de Paula, Á. (2013). Econometric Analysis of Games with Multiple Equilibria Annual Review of Economics 5, 107–31.
• Seim, K. (2006). An Empirical Model of Firm Entry with Endogenous Product-Type Choices. RAND Journal of Economics 37, 619–640.
• Sweeting, A. (2009). The Strategic Timing Incentives of Commercial Radio Stations: An Empirical Analysis Using Multiple Equilibria. RAND Journal of Economics 40, 710–742.
• Tamer, E. (2003). Incomplete Simultaneous Discrete Response Model with Multiple Equilibria. Review of Economic Studies 70, 147–165.
• Zhu, T. and V. Singh (2009). Spatial competition with endogenous location choices: An application to discount retailing. Quantitative Marketing and Economics 7, 1–35.

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.
• An, Y., Y. Hu, and R. Xiao (2021). Dynamic Decisions under Subjective Expectations: A Structural Analysis Journal of Econometrics 222, 645–675.
• 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.
• Britton, J. and B. Waltmann (2021). Revisiting the solution of dynamic discrete choice models: Time to bring back Keane and Wolpin (1994). IFS Working Paper, No. W21/13.
• 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.
• Igami, Mitsuru (2020). Artificial Intelligence as Structural Estimation: Deep Blue, Bonanza, and AlphaGo. Econometrics Journal, Volume 23, pp. S1–S24.
• 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.
• Kalouptsidi, M. (2018). Detection and Impact of Industrial Subsidies The Case of Chinese Shipbuilding. Review of Economic Studies 85, 1111–1158.
• 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. (2012). Estimation of Dynamic Discrete Choice Models Using Artificial Neural Network Approximations. Econometric Reviews 31, 84–106.
• 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.
• Rust, J. (1996). Numerical Dynamic Programming in Economics. In H. M. Amman, D. A. Kendrick, and J. Rust (Eds.), Handbook of Computational Economics, Volume 1, Elsevier.
• Scott, P.T. (2013). Dynamic Discrete Choice Estimation of Agricultural Land Use. Working paper.
• Semenova, Vira (2018). Machine Learning for Dynamic Discrete Choice. Working paper, UC Berkeley.
• 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, Gu, Luo, and Mira (2021). Diffusion of COVID–19 in Social and Production Networks: Simulation Evidence from A Dynamic Model Annals of Economics and Statistics 142, 179–210.
• Aguirregabiria, V. and C.-Y. Ho (2012). A dynamic oligopoly game of the US airline industry: Estimation and policy experiments. Journal of Econometrics 168, 156–173.
• Aguirregabiria, V. and A. Magesan (2012). Identification and Estimation of Dynamic Discrete Games when Players’ Beliefs are not in Equilibrium. Review of Economic Studies 87, 582–625.
• 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 (2016). Estimation of Dynamic Discrete Choice Models in Continuous Time. Review of Economic Studies 83, 889–931.
• 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. (2022). Identification and Estimation of Continuous Time Dynamic Discrete Choice Games. Working paper.
• Blevins, J.R. and M. Kim (2024). Nested Pseudo Likelihood Estimation of Continuous-Time Dynamic Discrete Games. Journal of Econometrics 238, 105576.
• Collard-Wexler, A. (2013). Demand Fluctuations in the Ready-Mix Concrete Industry. Econometrica 81, 1003–1037.
• Dearing, A. and J. R. Blevins (2024). Efficient and Convergent Sequential Pseudo-Likelihood Estimation of Dynamic Discrete Games. Forthcoming at Review of Economic Studies.
• 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.
• Lin, Z., X. Tang, and M. Xiao (2022). Endogeneity in Games with Incomplete Information: U.S. Cellphone Service Deployment Working Paper.
• 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.
• Qin, M. S., M. A. Vitorino, and G. John (2022). Planes, Trains and Co-Opetition: Evidence from China. Working Paper.
• 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.
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