Finite Lag Estimation of Non-Markovian Processes
研究了用固定滞后数的转移密度替代非马尔可夫平稳过程正确似然函数中的转移密度得到的拟极大似然估计量，证明其渐近方差随滞后数增加趋近于极大似然估计量的渐近方差。
Abstract We consider the quasi-maximum likelihood estimator (qmle) obtained by replacing each transition density in the correct likelihood for a non-Markovian, stationary process by a transition density with a fixed number of lags. This estimator is of interest because it is asymptotically equivalent to the efficient method of moments estimator as typically implemented in dynamic macro and finance applications. We show that the standard regularity conditions of quasi-maximum likelihood imply that a score vector defined over the infinite past exists. We verify that the existence of a score on the infinite past implies that the asymptotic variance of the finite lag qmle tends to the asymptotic variance of the maximum likelihood estimator as the number of lags tends to infinity.