# Advanced Econometrics I

## Course Information

**Meeting Time:** Tuesdays and Thursdays, 11:10 AM–12:30 PM.

**Location:** Baker Systems 128.

**Instructor:** Jason Blevins.

**Office hours:** By appointment on Tuesdays 1:00–2:30 PM.

**Website:** https://jblevins.org/courses/econ8830s24/

### 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 9: Introduction

January 11: 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 16: Numerical methods for nonlinear optimization

January 18: Parametric discrete choice models

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

January 23: Quantile regression

January 25 & 30: Numerical optimization in Matlab

February 1: Simulation Methods and Simulation-Based Estimation

**Nonparametric Methods & Identification**

February 6: Nonparametric estimation

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

February 8: 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*) (*)

- Koopmans (1949) (

**Static Discrete Choice Models**

February 13:

*Woohun Son*- Barsegyan, Molinari, and Thirkettle (2021) (*)
**Problem Set 1 Due**- Problem Set 2 Assigned

February 15:

*Wongeun Son*February 20:

*Wei Fan*- Davis (2006) (*)

**Econometrics of Static Games**

February 22: Entry games

February 27: Simultaneous bivariate response

- Tamer (2003) (*)

**Dynamic Discrete Choice Models**

February 29: Rust model

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

- Rust (1987) (
March 5: CCP estimation of Single-Agent Models

March 7:

*Shubham Roy*- Scott (2013) (*)
**Problem Set 2 Due**- Problem Set 3 Assigned

March 12 & 14:

*No Class, Spring Break*March 19:

*Huseyin Emre Sayici*March 21:

*Harrison Youn*- Norets (2012) (*)

March 26:

*Wonjun Lyou*March 28:

*Piyush Gupta*

**Dynamic Discrete Choice Games**

April 2: Nested Pseudo-Likelihood Estimation of Games

April 4: Efficient pseudo likelihood (EPL) estimation of dynamic games

April 9 & 11: Continuous time dynamic discrete choice

**Field Paper Presentations**

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

April 16:

*Field Paper Presentations Day 1*- Woohun Son
- Wonjun Lyou
- Harrison Youn

April 18:

*Field Paper Presentations Day 2*(TBD)- Wei Fan
- Wongeun Son
**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).
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*Econometrica*74, 611–629 - Imbens, G., and C. Manski (2004).
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*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.