Estimation and Inference for Latent Markov Models by Fourier Recursions

2026-09-07

AI Summary

本文提出了一种新的基于傅里叶递归框架的方法，用于解决非高斯和非线性潜在马尔可夫模型的估计与推理问题。

Abstract

This paper proposes a new theoretically exact Fourier recursion framework for a broad class of latent Markov models (LMMs), encompassing models widely used across a broad range of fields in economics. It can be viewed as a counterpart of the celebrated Kalman filter for non-Gaussian and nonlinear LMMs. Closed-form recursive updates of Fourier coefficients jointly deliver filtering, likelihood evaluation, and simultaneously accumulate the score and Hessian online. We introduce a unified truncated implementation that ensures uniform error control and numerical stability, preventing approximation errors from accumulating through the recursion. We establish asymptotic properties of the feasible maximum likelihood estimator for LMMs, provide a recursion-based consistent estimator for the Fisher information and discuss the possible model misspecification. These results provide the first general asymptotic theory for feasible approximate maximum likelihood estimation in LMMs. Simulations demonstrate its accuracy and stability, while an application to U.S. bankruptcy data recovers a persistent latent bankruptcy-pressure process.

Problem

• Latent Markov Models
• Fourier Recursions
• Non-Gaussian
• Nonlinear
• Model Misspecification

Innovation

• Fourier Recursions
• Latent Markov Models
• Kalman Filter Counterpart
• Asymptotic Theory
• Maximum Likelihood Estimation