References

This page lists the papers that the public estimator pages draw from. The estimator pages link here so the methodological source is visible before the usage examples.

Dynamic Discrete Choice

  • Rust, J. (1987). “Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher.” Econometrica, 55(5), 999-1033.

  • Hotz, V. J., and Miller, R. A. (1993). “Conditional Choice Probabilities and the Estimation of Dynamic Models.” Review of Economic Studies, 60(3), 497-529.

  • Aguirregabiria, V., and Mira, P. (2002). “Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models.” Econometrica, 70(4), 1519-1543.

  • Su, C.-L., and Judd, K. L. (2012). “Constrained Optimization Approaches to Estimation of Structural Models.” Econometrica, 80(5), 2213-2230.

  • Iskhakov, F., Lee, J., Rust, J., Schjerning, B., and Seo, K. (2016). “Comment on ‘Constrained Optimization Approaches to Estimation of Structural Models’.” Econometrica, 84(1), 365-370.

  • Shapiro, A., and Xu, H. (2005). “Stochastic Mathematical Programs with Equilibrium Constraints, Modeling and Sample Average Approximation.” Published in Optimization, 57(3), 395-418 (2008).

  • Koiso, S., and Otani, S. (2024). “An MPEC Estimator for the Sequential Search Model.” arXiv:2409.04378.

Approximate Structural Estimation

  • Luo, Y., and Sang, P. (2024). “Efficient Estimation of Structural Models via Sieves.” Working paper, University of Toronto.

  • Nguyen, H. (2025). “Neural Networks for Efficient Estimation of High-Dimensional Dynamic Discrete Choice Models.” Working paper, Georgetown University.

  • Adusumilli, K., and Eckardt, D. (2025). “Temporal-Difference Estimation of Dynamic Discrete Choice Models.” Working paper.

Maximum-Entropy and Adversarial IRL

  • Ziebart, B. D., Maas, A., Bagnell, J. A., and Dey, A. K. (2008). “Maximum Entropy Inverse Reinforcement Learning.” Proceedings of the 23rd AAAI Conference on Artificial Intelligence, 1433-1438.

  • Ziebart, B. D. (2010). Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy. PhD thesis, Carnegie Mellon University.

  • Wulfmeier, M., Ondruska, P., and Posner, I. (2015). “Maximum Entropy Deep Inverse Reinforcement Learning.” NIPS Deep Reinforcement Learning Workshop.

  • Fu, J., Luo, K., and Levine, S. (2018). “Learning Robust Rewards with Adversarial Inverse Reinforcement Learning.” International Conference on Learning Representations.

  • Lee, P. S., Sudhir, K., and Wang, T. (2026). “Consumer Engagement with Sequential Content: A Content-Aware Dynamic Choice Model.” SSRN working paper, abstract 6331041.

Q-Function and Divergence IRL

  • Ni, T., Sikchi, H., Wang, Y., Gupta, T., Lee, L., and Eysenbach, B. (2020). “f-IRL: Inverse Reinforcement Learning via State Marginal Matching.” Proceedings of the 4th Conference on Robot Learning.

  • Garg, D., Chakraborty, S., Cundy, C., Song, J., and Ermon, S. (2021). “IQ-Learn: Inverse Soft-Q Learning for Imitation.” Advances in Neural Information Processing Systems.

  • Kang, E. H., Yoganarasimhan, H., and Jain, L. (2025). “An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model.” arXiv preprint arXiv:2502.14131.