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Abstract

We propose simulation based estimation for discrete sequential move games of perfect information which relies on the simulated moments and importance sampling. We use importance sampling techniques not only to reduce computational burden and simulation error, but also to overcome non-smoothness problems. The model is identified with only weak scale and location normalizations, monte Carlo evidence demonstrates that the estimator can perform well in moderately-sized samples.



Item Type: MPRA Paper -

Original Title: Simulation Based Estimation of Discrete Sequential Move Games of Perfect Information-

Language: English-

Keywords: Game-Theoretic Econometric Models; Sequential-Move Game; Method of Simulated Moments; Importance Sampling; Conditional Moment Restrictions.-

Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: GeneralC - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C35 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; ProportionsC - Mathematical and Quantitative Methods > C0 - General > C01 - EconometricsC - Mathematical and Quantitative Methods > C7 - Game Theory and Bargaining Theory > C72 - Noncooperative Games-





Autor: Wang, Yafeng

Fuente: https://mpra.ub.uni-muenchen.de/23153/







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