Effects of distributional assumptions on conditional estimates from Mixed Logit models
Rose, John M.
- Working Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
With the gains in popularity of random coefficients models such as Mixed Logit, an increasing body of work is looking at the issue of the distributional assumptions made in such models. The results from these studies have shown that more flexibly distributions can have significant advantages over their more assumption-bound counterparts, such as the commonly used Normal distribution. However, their use also leads to significant increases in model complexity and estimation cost. Recently, there has also been an increase in the number of applications making use of individual-specific draws for random coefficients, obtained through conditioning on the observed choices. However, much as in the case of work based on unconditional distributions, the majority of such applications is based entirely on the use of the Normal distribution, and there is little knowledge as to the impact of the (unconditional) distributional assumptions on conditional model results. This gap is filled by the present paper, which, with the help of various applications making use of real and simulated data, shows that different distributions can lead to very different results, even when conditioning on observed choices. Furthermore, the advantages of certain distributions in estimation do not necessarily translate into advantages when working with conditional draws. Finally, the results show that model fit on its own is not an appropriate indicator of how good an approximation to the true distribution is provided by a model, especially when basing the analysis on conditional estimates. Show more
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Journal / seriesArbeitsberichte Verkehrs- und Raumplanung
PublisherETH, Eidgenössische Technische Hochschule Zürich, Institut für Verkehrsplanung und Transportsysteme
SubjectECONOMETRICS AND ECONOMETRIC MODELS (OPERATIONS RESEARCH); VERKEHRSMODELLE + VERKEHRSSIMULATION (VERKEHR UND TRANSPORT); STOCHASTIC MODELS + STOCHASTIC SIMULATION (PROBABILITY THEORY); ÖKONOMETRIE UND ÖKONOMETRISCHE MODELLE (OPERATIONS RESEARCH); STOCHASTISCHE MODELLE + STOCHASTISCHE SIMULATION (WAHRSCHEINLICHKEITSRECHNUNG); TRANSPORT MODELS + TRAFFIC SIMULATION (TRANSPORTATION AND TRAFFIC)
Organisational unit02610 - Inst. f. Verkehrspl. u. Transportsyst. / Inst. Transport Planning and Systems
03521 - Axhausen, Kay W. / Axhausen, Kay W.
02226 - NSL - Netzwerk Stadt und Landschaft / NSL - Network City and Landscape
02655 - Netzwerk Stadt und Landschaft D-ARCH
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