Parameter bias in misspecified HCM: An empirical study.

Model misspecification is likely to occur when working with real datasets. However, previous studies showing the advantages of the hybrid choice models have mostly used models where structural and measurement equations match the functions employed in the data generating process, especially when parameter biases were discussed. The aim of this paper is to investigate the extent of parameter bias in misspecified hybrid choice models. For this task, a mode choice model is estimated on synthetic data with efforts focus on mimicking the conditions present in real datasets, where the postulated structural and measurement equations are less flexible than the functions used for the data generating process. Results show that hybrid choice models, even if misspecified, manage to recover better parameter estimates than a multinomial logit. However, hybrid choice models are not unbeatable, as results indicate that misspecified hybrid choice models might still yield biased parameter estimates. Moreover, results suggest that hybrid choice models successfully isolate the source of model bias, preventing its propagation to other parameter estimates. Results also show that parameter estimates from hybrid choice models are sensible to modelling assumptions, and that parameter estimates of the utility function are robust, given that errors are modelled. ; QC 20180828