Defining inter-alternative error structures for joint revealed preference-stated preference modelling: New evidence

Abstract

Joint model estimation with revealed (RP) and stated preference (SP) data has become very popular in the last few years and it is now considered common practice. Although this might be true in terms of its widespread use in practical applications, many theoretical issues related to estimation and prediction with joint RP-SP data are far from being fully explored. Given the ample diffusion of RP-SP modelling in practice, its misuse can have severe consequences in terms of policy analysis and evaluation of transport investments; thus, it is crucial to continue research on this problem in order to try and give a theoretical justification to the many relevant issues that remain uncovered. One particularly interesting issue, which has not been well explored, is the effect of partial data enrichment on the correlation structure among alternatives (i.e. when different correlation structures are revealed in the RP and SP data sets). This problem, which is often found in practice, has no trivial solution and raises new interesting theoretical questions in terms of both estimation and prediction. In this paper we discuss the theoretical and practical implications of this problem and then provide empirical evidence, from a real case, of the errors that may creep in when these models are not applied correctly. Some guidelines to help filling this important gap in the proper use RP-SP data are finally provided.