Hiroaki Kaido, Jiaxuan Li, Marc Rysman
arXiv 10 Apr 2018 · Econometrics
arXiv:1804.03674 · PDF · DOI · OpenAlex · Extracted main text
This paper explores the effects of simulated moments on the performance of inference methods based on moment inequalities. Commonly used confidence sets for parameters are level sets of criterion functions whose boundary points may depend on sample moments in an irregular manner. Due to this feature, simulation errors can affect the performance of inference in non-standard ways. In particular, a (first-order) bias due to the simulation errors may remain in the estimated boundary of the confidence set. We demonstrate, through Monte Carlo experiments, that simulation errors can significantly reduce the coverage probabilities of confidence sets in small samples. The size distortion is particularly severe when the number of inequality restrictions is large. These results highlight the danger of ignoring the sampling variations due to the simulation errors in moment inequality models. Similar issues arise when using predicted variables in moment inequalities models. We propose a method for properly correcting for these variations based on regularizing the intersection of moments in parameter space, and we show that our proposed method performs well theoretically and in practice.
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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 | Andrews, D. W. & Soares, G (2010) Inference for parameters defined by moment inequalities using generalized moment selection | 1.000 | 11 | 3 | 100% |
| 2 | Chernozhukov, V., Lee, S., & Rosen, A (2013) Intersection bounds: Estimation and inference | 0.928 | 4 | 3 | 100% |
| 3 | Chernozhukov, V., Hong, H., & Tamer, E (2007) Estimation and confidence regions for parameter sets in econometric models | 0.874 | 6 | 3 | 67% |
| 4 | Ciliberto, F. & Tamer, E (2009) Market structure and multiple equilibria in airline markets | 0.874 | 5 | 2 | 100% |
| 5 | Eizenberg, A (2013) Upstream innovation and product variety in the U.S. home PC market | 0.874 | 5 | 2 | 100% |
| 6 | Gourieroux, C. & Montfort, A (1996) Simulation-Based Econometric Methods | 0.843 | 3 | 3 | 100% |
| 7 | Beck, A. & Teboulle, M (2012) Smoothing and first order methods: A unified framework | 0.822 | 9 | 3 | 56% |
| 8 | Andrews, D. W. & Guggenberger, P (2009) Validity of subsampling and plug-in asymptotic inference for parameters defined by moment inequalities | 0.794 | 6 | 3 | 50% |
| 9 | Pakes, A. & Pollard, D (1989) Simulation and the asymptotics of optimization estimators | 0.737 | 3 | 3 | 67% |
| 10 | Andrews, D. W. K. & Barwick, P. J (2012) Inference for parameters defined by moment inequalities: A recommended moment selection procedure | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 98 scored citations.