Federico A. Bugni, Jackson Bunting, Takuya Ura
arXiv 5 Oct 2020 · Econometrics · publishedQuantitative Economics (2025) · 1 citations (OpenAlex)
arXiv:2010.02297 · PDF · DOI · OpenAlex · Extracted main text
The literature on dynamic discrete games often assumes that the conditional choice probabilities and the state transition probabilities are homogeneous across markets and over time. We refer to this as the "homogeneity assumption" in dynamic discrete games. This assumption enables empirical studies to estimate the game's structural parameters by pooling data from multiple markets and from many time periods. In this paper, we propose a hypothesis test to evaluate whether the homogeneity assumption holds in the data. Our hypothesis test is the result of an approximate randomization test, implemented via a Markov chain Monte Carlo (MCMC) algorithm. We show that our hypothesis test becomes valid as the (user-defined) number of MCMC draws diverges, for any fixed number of markets, time periods, and players. We apply our test to the empirical study of the U.S.\ Portland cement industry in Ryan (2012).
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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 | Otsu, T., M. Pesendorfer, and Y. Takahashi (2016) Pooling data across markets in dynamic Markov Games | 1.000 | 32 | 4 | 100% |
| 2 | Ryan, S (2012) The costs of environmental regulation in a concentrated industry | 1.000 | 11 | 3 | 100% |
| 3 | Besag, J. and D. Mondal (2013) Exact Goodness-of-Fit Tests for Markov Chains | 0.928 | 5 | 3 | 80% |
| 4 | Pesendorfer, M. and P. Schmidt-Dengler (2008) Asymptotic Least Squares Estimators for Dynamic Games | 0.928 | 4 | 3 | 100% |
| 5 | Bajari, P., D. Benkard, and J. Levin (2007) Estimating Dynamic Models of Imperfect Competition | 0.843 | 3 | 3 | 100% |
| 6 | Lehmann, E. L. and J. P. Romano (2005) Testing Statistical Hypothesis: Third edition | 0.794 | 14 | 3 | 50% |
| 7 | Kandel, D., M. Yossi, R. Unger, and P. Winkler (1996) Shuffling biological sequences | 0.737 | 4 | 3 | 50% |
| 8 | Keane, M. P. and K. I. Wolpin (1997) The Career Decisions of Young Men | 0.693 | 5 | 1 | 100% |
| 9 | Aguirregabiria, V. and A. Magesan (2020) Identification and estimation of dynamic games when players’ beliefs are not in equilibrium | 0.644 | 2 | 2 | 100% |
| 10 | Aguirregabiria, V. and P. Mira (2007) Sequential Estimation of Dynamic Discrete Games | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Marginal homogeneity tests with panel data | 0.405 | 1 | 1 |