This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs). For non-Gaussian HMMs, the computation of the maximum likelihood estimator (MLE) involves a high-dimensional integral that has no analytical solution and can be difficult to approach accurately. We develop a new alternative method based on the theory of estimating functions and a deconvolution strategy. Our procedure requires the same assumptions as the MLE and deconvolution estimators. We provide theoretical guarantees about the performance of the resulting estimator; its consistency and asymptotic normality are established. This leads to the construction of confidence intervals. Monte Carlo experiments are investigated and compared with the MLE. Finally, we illustrate our approach using real data for ex-ante interest rate forecasts.

本文开发了一种简单且计算效率高的参数化方法来估计一般隐马尔可夫模型（HMM）。对于非高斯 HMM，最大似然估计 (MLE) 的计算涉及高维积分，该积分没有解析解，并且难以准确逼近。我们基于估计函数的理论和反卷积策略开发了一种新的替代方法。我们的过程需要与 MLE 和反卷积估计器相同的假设。我们为结果估计器的性能提供理论保证；其一致性和渐近正态性成立。这导致了置信区间的构建。研究了蒙特卡罗实验并与 MLE 进行了比较。最后，