Accounting for Spatial Heterogeneity and Autocorrelation in Spatial Discrete Choice Models: Implications for Behavioral Predictions
研究了在随机效用模型中忽略空间异质性和空间自相关对模型表现和福利估计的影响，发现同时考虑两者能提升模型性能，对土地利用和渔业经济学研究者有参考价值。
The random utility model (RUM) is commonly used in the land-use and fishery economics literature. This research investigates the affect that spatial heterogeneity and spatial autocorrelation have within the RUM framework using alternative specifications of the multinomial logit, multinomial probit, and spatial multinomial probit models. Using data on the spatial decisions of fishermen, the results illustrate that ignoring spatial heterogeneity in the unobservable portion of the RUM dramatically affects model performance and welfare estimates. Furthermore, accounting for spatial autocorrelation in addition to spatial heterogeneity increases the performance of the RUM. <i></i>