Full-Information Selection Bias Correction for Discrete Choice Models with Observation-Conditional Regressors
研究了多项选择模型中基于观测选择构建属性值导致的自选择偏差，提出全信息最大似然估计和无限识别加权法，并应用于白令海鳕鱼渔场的选址决策。
We examine self-selection in polychotomous choice models that construct attribute values for each alternative conditioned on observed choices. Using observations made only when the alternative was chosen ignores private information which was a basis for the decision, biasing resulting estimates. We suggest a full-information maximum likelihood procedure that performs well at the extremes of the choice set in our sample, and use an “identification at infinity” weighting to identify levels. We apply the model to understanding fishing location choice in the economically significant Bering Sea pollock fishery, where expected catches at each location are constructed from harvests observed when that location is chosen.