arXiv 7 Aug 2018 · Econometrics · 2 citations (OpenAlex)
arXiv:1808.02569 · PDF · DOI · OpenAlex · Extracted main text
Dynamic discrete choice models often discretize the state vector and restrict its dimension in order to achieve valid inference. I propose a novel two-stage estimator for the set-identified structural parameter that incorporates a high-dimensional state space into the dynamic model of imperfect competition. In the first stage, I estimate the state variable's law of motion and the equilibrium policy function using machine learning tools. In the second stage, I plug the first-stage estimates into a moment inequality and solve for the structural parameter. The moment function is presented as the sum of two components, where the first one expresses the equilibrium assumption and the second one is a bias correction term that makes the sum insensitive (i.e., orthogonal) to first-stage bias. The proposed estimator uniformly converges at the root-N rate and I use it to construct confidence regions. The results developed here can be used to incorporate high-dimensional state space into classic dynamic discrete choice models, for example, those considered in Rust (1987), Bajari et al. (2007), and Scott (2013).
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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 | Bajari, P., Benkard, C. L., and Levin, J (2007) Estimating dynamic models of imperfect competition | 1.000 | 9 | 3 | 100% |
| 2 | Rust, J (1987) Optimal replacement of gmc bus engines: An empirical model of harold zurcher | 1.000 | 6 | 3 | 100% |
| 3 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2017) Double/debiased machine learning for treatment and causal parameters | 0.894 | 7 | 4 | 71% |
| 4 | Scott, P (2013) Dynamic discrete choice estimation of agricultural land use | 0.843 | 3 | 3 | 100% |
| 5 | Chernozhukov, V., Hong, H., and Tamer, E (2007) Estimation and confidence regions for parameter sets in econometric models | 0.811 | 4 | 2 | 100% |
| 6 | Newey, W (1994) The asymptotic variance of semiparametric estimators | 0.811 | 4 | 2 | 100% |
| 7 | Hotz, V. J. and Miller, R. A (1993) Conditional choice probabilities and the estimation of dynamic models | 0.737 | 3 | 2 | 100% |
| 8 | Chernozhukov, V., Escanciano, J. C., Ichimura, H., and Newey, W (2017) Locally robust semiparametric estimation | 0.644 | 4 | 1 | 100% |
| 9 | Neyman, J (1959) Optimal asymptotic tests of composite statistical hypotheses | 0.511 | 2 | 1 | 100% |
| 10 | Andrews, D (1994) Asymptotics for semiparametric econometric models via stochastic equicontinuity | 0.405 | 1 | 1 | 100% |
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