Karun Adusumilli, Dita Eckardt
arXiv 19 Dec 2019 · Econometrics
arXiv:1912.09509 · PDF · Extracted main text
We study the use of Temporal-Difference learning for estimating the structural parameters in dynamic discrete choice models. Our algorithms are based on the conditional choice probability approach but use functional approximations to estimate various terms in the pseudo-likelihood function. We suggest two approaches: The first - linear semi-gradient - provides approximations to the recursive terms using basis functions. The second - Approximate Value Iteration - builds a sequence of approximations to the recursive terms by solving non-parametric estimation problems. Our approaches are fast and naturally allow for continuous and/or high-dimensional state spaces. Furthermore, they do not require specification of transition densities. In dynamic games, they avoid integrating over other players' actions, further heightening the computational advantage. Our proposals can be paired with popular existing methods such as pseudo-maximum-likelihood, and we propose locally robust corrections for the latter to achieve parametric rates of convergence. Monte Carlo simulations confirm the properties of our algorithms in practice.
appendix boundary found by appendix_command · 61% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | V. Aguirregabiria and P. Mira, “Swapping the nested fixed point algo… (2007) Sequential estimation of dynamic discrete games | 1.000 | 9 | 3 | 100% |
| 2 | J. N. Tsitsiklis and B. Van Roy, “An analysis of temporal-difference… (1997) An analysis of temporal-difference learning with function approximation | 0.928 | 5 | 4 | 80% |
| 3 | R. Munos and C. Szepesvári, “Finite-time bounds for fitted value ite… (2008) Finite-time bounds for fitted value iteration | 0.928 | 4 | 3 | 100% |
| 4 | V. J. Hotz and R. A. Miller, “Conditional choice probabilities and t… (1993) Conditional choice probabilities and the estimation of dynamic models | 0.874 | 7 | 2 | 100% |
| 5 | V. Chernozhukov, J. C. Escanciano, H. Ichimura, W. K. Newey, and J.… (2022) Locally robust semiparametric estimation | 0.860 | 11 | 3 | 64% |
| 6 | J. Rust, “Optimal replacement of gmc bus engines: An empirical model… (1987) Optimal replacement of gmc bus engines: An empirical model of harold zurcher | 0.843 | 4 | 4 | 75% |
| 7 | V. Aguirregabiria and P. Mira, “Swapping the nested fixed point algo… (2002) Swapping the nested fixed point algorithm: A class of estimators for discrete markov decision models | 0.811 | 5 | 2 | 80% |
| 8 | V. Aguirregabiria and P. Mira, “Swapping the nested fixed point algo… (2010) Dynamic discrete choice structural models: A survey | 0.737 | 3 | 3 | 67% |
| 9 | V. Aguirregabiria and A. Magesan, “Solution and estimation of dynami… (2018) Solution and estimation of dynamic discrete choice structural models using euler equations | 0.737 | 3 | 2 | 100% |
| 10 | M. Pesendorfer and P. Schmidt-Dengler, “Asymptotic least squares est… (2008) Asymptotic least squares estimators for dynamic games | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.