# 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-1987)=
- Rust, J. (1987). "Optimal Replacement of GMC Bus Engines: An Empirical Model
  of Harold Zurcher." *Econometrica*, 55(5), 999-1033.

(hotz-miller-1993)=
- 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-mira-2002)=
- 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-judd-2012)=
- Su, C.-L., and Judd, K. L. (2012). "Constrained Optimization Approaches to
  Estimation of Structural Models." *Econometrica*, 80(5), 2213-2230.

(iskhakov-2016)=
- 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-xu-2005)=
- 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-otani-2024)=
- Koiso, S., and Otani, S. (2024). "An MPEC Estimator for the Sequential Search
  Model." arXiv:2409.04378.

## Approximate Structural Estimation

(luo-sang-2024)=
- Luo, Y., and Sang, P. (2024). "Efficient Estimation of Structural Models via
  Sieves." Working paper, University of Toronto.

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

(adusumilli-eckardt-2025)=
- Adusumilli, K., and Eckardt, D. (2025). "Temporal-Difference Estimation of
  Dynamic Discrete Choice Models." Working paper.

## Maximum-Entropy and Adversarial IRL

(ziebart-2008)=
- 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-2010)=
- Ziebart, B. D. (2010). *Modeling Purposeful Adaptive Behavior with the
  Principle of Maximum Causal Entropy*. PhD thesis, Carnegie Mellon University.

(wulfmeier-2015)=
- Wulfmeier, M., Ondruska, P., and Posner, I. (2015). "Maximum Entropy Deep
  Inverse Reinforcement Learning." NIPS Deep Reinforcement Learning Workshop.

(fu-2018)=
- Fu, J., Luo, K., and Levine, S. (2018). "Learning Robust Rewards with
  Adversarial Inverse Reinforcement Learning." *International Conference on
  Learning Representations*.

(lee-sudhir-wang-2026)=
- 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-2020)=
- 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-2021)=
- 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-2025)=
- 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*.

## Identification and Related Foundations

(kim-2021)=
- Kim, K., Garg, S., Shiragur, K., and Ermon, S. (2021). "Reward
  Identification in Inverse Reinforcement Learning." *International Conference
  on Machine Learning*.

(cao-2021)=
- Cao, H., Cohen, S. B., and Szpruch, L. (2021). "Identifiability in Inverse
  Reinforcement Learning." *Advances in Neural Information Processing Systems*.

(bray-2019)=
- Bray, R. L. "Unnesting the Fixed Point in the Estimation of Dynamic
  Programs." Working paper, SSRN 3307175.

(oguz-bray-2026)=
- Oguz, E., and Bray, R. L. (2026). "Training Neural Networks Embedded in
  Dynamic Discrete Choice Models." Working paper, arXiv 2604.09736.