Solving dynamic discrete choice models using smoothing and sieve methods
نویسندگان
چکیده
We propose to combine smoothing, simulations and sieve approximations solve for either the integrated or expected value function in a general class of dynamic discrete choice (DDC) models. use importance sampling approximate Bellman operators defining two functions. The random operators, therefore also corresponding solutions, are generally non-smooth which is undesirable. To circumvent this issue, we introduce smoothed versions functions using methods. show that one can avoid sieves by generalizing adapting “self-approximating” method Rust (1997b) our setting. provide an asymptotic theory both solution methods they converge with N -rate, where number Monte Carlo draws, towards Gaussian processes. examine their performance practice through set numerical experiments find perform well being particularly attractive terms computational speed accuracy.
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ژورنال
عنوان ژورنال: Journal of Econometrics
سال: 2021
ISSN: ['1872-6895', '0304-4076']
DOI: https://doi.org/10.1016/j.jeconom.2020.02.007