A Practical Comparison of Modeling Approaches for Panel Data
In contrast to cross-sectional data, panel data provide us with the ability to directly observe and model changes in behavior resulting from changes in causal variables. Explicitly modeling change should allow more accurate predictions, at least for the short term. Model estimation using panel data, however, requires us to sort out various types of within-person and cross-sectional effects, both observed and unobserved. This chapter begins with a discussion of the types of variables one might expect and their treatment in model estimation. Although quite complex statistical methods are required to isolate all types of cross-sectional and dynamic effects in panel data, a number of relatively simple model forms can be used to incorporate at least some aspects of dynamic transitions in choice behavior. These simple dynamic models are compared to their static counterparts in the context of a commuter “before and after” panel study in the Netherlands. The models are compared in terms of the estimation results and, more importantly, in terms of the predictions they provide in a dynamic application framework. The chapter thus provides an illustration and discussion of the use of dynamic panel models in forecasting, an issue which is rarely treated in the practical literature.
KeywordsTravel Time Panel Data Mode Choice Rail Line Nest Logit Model
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