Singularity and Autoregressive Disturbances in Linear Logit Models
探讨在线性Logit需求系统中加入自回归扰动的影响，论证正态误差假设在此模型中比在加性扰动份额方程中更合适，并给出理论和实证支持。
The implications of including autoregressive disturbances in linear logit models of demand systems are explored. It is argued that the normality assumption of the error terms is more appropriate in the linear logit model than in a share equation model with additive disturbances (commonly found in the literature). Autoregressive disturbances and their implications for model estimation are discussed in that context. Both theoretical arguments and empirical evidence are presented in favor of the logit specification given the presence of serial correlation.