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The Performance of Nested Logit Models When Welfare Estimation Is the Goal
Author(s) -
Herriges Joseph A.,
Kling Catherine L.
Publication year - 1997
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.2307/1244421
Subject(s) - nested logit , logit , nesting (process) , econometrics , specification , mixed logit , nested set model , logistic regression , welfare , estimation , economics , computer science , monte carlo method , discrete choice , statistics , mathematics , engineering , data mining , mechanical engineering , market economy , management , relational database
In this paper we examine the performance of nested logit models in the face of two specification errors. The first specification error arises when a nested logit model is appropriate, but the wrong nesting structure is chosen. The second specification error occurs when the underlying stochastic process is not consistent with nested logit. Particular attention is placed upon the impact that these errors can have on welfare predictions. Monte Carlo experiments are used, together with analytical results, to examine the resulting bias to welfare estimates. In addition, we explore the value of alternative model selection criteria in choosing nesting structures.