arXiv 4 Nov 2025 · Econometrics · publishedQuantitative Economics (2026)
arXiv:2511.02701 · PDF · DOI · OpenAlex · Extracted main text
This paper considers the theoretical, computational, and econometric properties of continuous time dynamic discrete choice games with stochastically sequential moves, introduced by Arcidiacono, Bayer, Blevins, and Ellickson (2016). We consider identification of the rate of move arrivals, which was assumed to be known in previous work, as well as a generalized version with heterogeneous move arrival rates. We re-establish conditions for existence of a Markov perfect equilibrium in the generalized model and consider identification of the model primitives with only discrete time data sampled at fixed intervals. Three foundational example models are considered: a single agent renewal model, a dynamic entry and exit model, and a quality ladder model. Through these examples we examine the computational and statistical properties of estimators via Monte Carlo experiments and an empirical example using data from Rust (1987). The experiments show how parameter estimates behave when moving from continuous time data to discrete time data of decreasing frequency and the computational feasibility as the number of firms grows. The empirical example highlights the impact of allowing decision rates to vary.
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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 | Rust, J (1987) Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher | 0.693 | 11 | 1 | 100% |
| 2 | Aguirregabiria, V. and P. Mira (2007) Sequential estimation of dynamic discrete games | 0.693 | 8 | 1 | 100% |
| 3 | Singer, B. and S. Spilerman (1976) The representation of social processes by Markov models | 0.693 | 6 | 1 | 100% |
| 4 | Pesendorfer, M. and P. Schmidt-Dengler (2008) Asymptotic least squares estimators for dynamic games | 0.644 | 4 | 1 | 100% |
| 5 | Blevins, J. R (2017) Identifying restrictions for finite parameter continuous time models with discrete time data self | 0.606 | 9 | 1 | 67% |
| 6 | Arcidiacono, P., P. Bayer, J. R. Blevins, and P. B. Ellickson (2016) Estimation of dynamic discrete choice models in continuous time with an application to retail competition | 0.585 | 3 | 1 | 100% |
| 7 | Hotz, V. J. and R. A. Miller (1993) Conditional choice probabilities and the estimation of dynamic models | 0.585 | 3 | 1 | 100% |
| 8 | Phillips, P. C. B (1973) The problem of identification in finite parameter continuous time models | 0.585 | 3 | 1 | 100% |
| 9 | Doraszelski, U. and K. L. Judd (2012) Avoiding the curse of dimensionality in dynamic stochastic games | 0.511 | 4 | 1 | 50% |
| 10 | Blevins, J. R. and M. Kim (2024) Nested pseudo likelihood estimation of continuous-time dynamic discrete games self | 0.511 | 3 | 1 | 67% |
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