Modeling Parametric Evolution in a Random Utility Framework
随机效用框架中的参数演化建模
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…