A Logit Model with Missing Information Illustrated by Testing for Hidden Unemployment in Transition Economies
将EM算法扩展到多项Logit模型，处理类别归属信息缺失的情况，提出潜在多项Logit（LMNL）模型，并用其检验中东欧转型经济中的隐性失业，发现自称自雇者中额外存在0.5%的隐性失业。
Abstract In an important paper, Dempster, Laird and Rubin (1977) showed how the expectation maximization (EM) algorithm could be used to obtain maximum likelihood estimates of parameters in a multinomial probability model with missing information. This article extends Dempster, Laird and Rubin's work on the EM algorithm to the estimation of a multinomial logit model with missing information on category membership. We call this new model the latent multinomial logit (LMNL) model. A constrained version of the LMNL model is used to examine the issue of hidden unemployment in transition economies following the approach of Earle and Sakova (2000) . We found an additional 0.5% hidden unemployment among workers describing themselves as self‐employed in the transition economies of Central and Eastern Europe.