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AR(1) 模型分析中的平坦先验与无知先验AI 解读

Abstract The paper compares, by a Monte‐Carlo study based on an AR(1) model, the performance of the flat prior and the ignorance prior suggested by Phillips. It argues that the ignorance prior gives heavy weight to values of the autoregressive parameter p higher than 1, and hence distorts the sample evidence as summarized in the likelihood function. It yields bimodal posterior distributions, with the second mode at p higher than 1, even when the true value of p is…

含滞后因变量和序列相关误差的回归模型中的序列相关检验AI 解读

The paper compares the power of two tests for serial correlation in regression models with lagged dependent variables, recently suggested by Durbin, with that of the likelihood ratio test by means of two sets of Monte-Carlo experiments-one in which the exogenous series is taken to be the quarterly GNP series for the USA and the other in which the exogenous series is generated by a known autoregression.