Efficient drift parameter estimation for ergodic solutions of backward SDEs
研究了离散观测下遍历随机过程漂移参数的拟极大似然估计方法，证明了估计量的一致性和渐近正态性，适用于半参数扩散和倒向随机微分方程。
Abstract We derive consistency and asymptotic normality results for quasi‐maximum likelihood methods for drift parameters of ergodic stochastic processes observed in discrete time in an underlying continuous‐time setting. The special feature of our analysis is that the stochastic integral part is unobserved and nonparametric. Additionally, the drift may depend on the (unknown and unobserved) stochastic integrand. Our results hold for ergodic semi‐parametric diffusions and backward SDEs. Simulation studies confirm that the methods proposed yield good convergence results.