Bryan S. Graham, Andrin Pelican
arXiv 21 Jul 2023 · Econometrics
arXiv:2307.11857 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a simulation algorithm for evaluating the log-likelihood function of a large supermodular binary-action game. Covered examples include (certain types of) peer effect, technology adoption, strategic network formation, and multi-market entry games. More generally, the algorithm facilitates simulated maximum likelihood (SML) estimation of games with large numbers of players, $T$, and/or many binary actions per player, $M$ (e.g., games with tens of thousands of strategic actions, $TM=O(10^4)$). In such cases the likelihood of the observed pure strategy combination is typically (i) very small and (ii) a $TM$-fold integral who region of integration has a complicated geometry. Direct numerical integration, as well as accept-reject Monte Carlo integration, are computationally impractical in such settings. In contrast, we introduce a novel importance sampling algorithm which allows for accurate likelihood simulation with modest numbers of simulation draws.
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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 | De Weerdt, J (2004) Insurance Against Poverty, chapter Risk-sharing and endogenous network formation, pages 197 – 216 | 1.000 | 10 | 4 | 100% |
| 2 | Hajivassiliou, V. and Ruud, P. A (1994) Handbook of Econometrics, volume 4, chapter Classical estimation methods for LDV models using simulation, pages 2383 – 2441 | 1.000 | 5 | 4 | 100% |
| 3 | Manski, C. F (1993) Identification of endogenous social effects: the reflection problem | 1.000 | 5 | 3 | 100% |
| 4 | Krauth, B. V (2006) Simulation-based estimation of peer effects | 0.928 | 4 | 3 | 100% |
| 5 | Miyauchi, Y (2016) Structural estimation of a pairwise stable network with nonnegative externality | 0.928 | 4 | 3 | 100% |
| 6 | Soetevent, A. and Kooreman, P (2007) A discrete choice model with social interactions: with an application to high school teen behavior | 0.928 | 4 | 3 | 100% |
| 7 | Jia, P (2008) What happens when wal-mart comes to town: an empirical analysis of the discount retailing industry | 0.874 | 5 | 2 | 100% |
| 8 | Graham, B. S (2020) Handbook of Econometrics, volume 7, chapter Network data, pages 111 – 218 self | 0.843 | 3 | 3 | 100% |
| 9 | Jackson, M. O., Rodriguez-Barraquer, T., and Tan, X (2012) Social capital and social quilts: network patterns of favor exchange | 0.811 | 4 | 2 | 100% |
| 10 | Ackerberg, D. A (2009) A new use of importance sampling to reduce computational burden in simulation estimation | 0.737 | 4 | 3 | 50% |
Showing the top 10 of 56 scored citations.