Maximum likelihood estimation of latent Markov models using closed-form approximations
利用闭式近似估计隐状态 Markov 模型的极大似然
This paper proposes and implements an efficient and flexible method to compute maximum likelihood estimators of continuous-time models when part of the state vector is latent. Stochastic volatility and term structure models are typical examples. Existing methods integrate out the latent variables using either simulations as in MCMC, or replace the latent variables by observable proxies. By contrast, our approach relies on closed-form approximations to estimate parameters and…