<p> This dissertation carries out a series of Monte Carlo simulations seeking the implications for welfare estimates from three research practices commonly implemented in empirical applications of mixed logit and latent class logit. </p><p> Chapter 3 compares welfare measures across conditional logit, mixed logit, and latent class logit. The practice of comparing welfare estimates is widely used in the field. However, this chapter shows comparisons of welfare estimates seem unable to provide reliable information about the differences in welfare estimates that result from controlling for unobserved heterogeneity. The reason is that estimates from mixed logit and latent class logit are inherently inecient and inaccurate. </p><p> Researchers tend to use their own judgement to select the number of classes of a latent class logit. Chapter 4 studies the reliability of welfare estimates obtained under two scenarios for which an empirical researcher using his/her judgement would arguably choose less classes than the true number of classes. Results show that models with a number of classes smaller than the true number tend to yield down- ward biased and inaccurate estimates. The latent class logit with the true number of classes always yield unbiased estimates but their accuracy may be worse than models with the smaller number of classes. </p><p> Studies implementing discrete choice experiments commonly obtain estimates of preference parameters from latent class logit models. This practice, however, implies a mismatch: discrete choice experiments are designed under the assumption of homogeneity in preferences, and latent class logit search for heterogeneous preferences. Chapter 5 studies whether welfare estimates are robust to this mismatch. This chapter checks whether the number of choice tasks impact the reliability of welfare estimates. The findings show welfare estimates are unbiased regardless the number of choice tasks, and their accuracy increases with the number of choice tasks. However, some of the welfare estimates are inefficient to the point that cannot be statistically distinguished from zero, regardless the number of choice tasks. </p><p> Implications from these findings for the empirical literature are discussed. </p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3587273 |
| Date | 29 August 2013 |
| Creators | Martinez-Cruz, Adan L. |
| Publisher | University of Maryland, College Park |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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