Hybrid unadjusted Langevin methods for high-dimensional latent variable models

Rubén Loaiza-Maya, Didier Nibbering and Dan Zhu

Journal of Econometrics, 2024, vol. 241, issue 2

Abstract: The exact estimation of latent variable models with big data is known to be challenging. The latents have to be integrated out numerically, and the dimension of the latent variables increases with the sample size. This paper develops a novel approximate Bayesian method based on the Langevin diffusion process. The method employs the Fisher identity to integrate out the latent variables, which makes it accurate and computationally feasible when applied to big data. In contrast to other approximate estimation methods, it does not require the choice of a parametric distribution for the unknowns, which often leads to inaccuracies. In an empirical discrete choice example with a million observations, the proposed method accurately estimates the posterior choice probabilities using only 2% of the computation time of exact MCMC.

Keywords: Unadjusted Langevin algorithm; Latent variable models; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C25 C55 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:241:y:2024:i:2:s0304407624000873

DOI: 10.1016/j.jeconom.2024.105741

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