Modeling Parametric Evolution in a Random Utility Framework
提出在随机效用模型中用向量自回归过程直接建模参数随时间的变化，并用贝叶斯方法估计，能改进预测、预测长期参数水平并纠正聚合偏误，以超市商品选择数据验证了参数动态的存在。
Random utility models have become standard econometric tools, allowing parameter inference for individual-level categorical choice data. Such models typically presume that changes in observed choices over time can be attributed to changes in either covariates or unobservables. We study how choice dynamics can be captured more faithfully by also directly modeling temporal changes in parameters, using a vector autoregressive process and Bayesian estimation. This approach offers a number of advantages for theorists and practitioners, including improved forecasts, prediction of long-run parameter levels, and correction for potential aggregation biases. We illustrate the method using choices for a common supermarket good, where we find strong support for parameter dynamics.