LIKELIHOOD INFERENCE IN AN AUTOREGRESSION WITH FIXED EFFECTS
研究了多分层固定效应自回归模型中回归系数的剖面得分偏差，通过中心化剖面得分得到无偏估计方程和调整后的剖面似然，但调整似然的全局最大值在无穷远处，需在椭球内寻找局部极大值以获得一致估计。
We calculate the bias of the profile score for the regression coefficients in a multistratum autoregressive model with stratum-specific intercepts. The bias is free of incidental parameters. Centering the profile score delivers an unbiased estimating equation and, upon integration, an adjusted profile likelihood. A variety of other approaches to constructing modified profile likelihoods are shown to yield equivalent results. However, the global maximizer of the adjusted likelihood lies at infinity for any sample size, and the adjusted profile score has multiple zeros. Consistent parameter estimates are obtained as local maximizers inside or on an ellipsoid centered at the maximum likelihood estimator.