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How wrong can you be, without noticing? Further evidence on speci cation errors in the Conditional Logit

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  • Tomás del Barrio Casto

    (University of the Balearic Islands, Palma de Mallorca)

  • William Nilsson

    (University of the Balearic Islands, Palma de Mallorca)

  • Andrés J. Picazo-Tadeo

    (University of Valencia)

Abstract

Discrete choice models such as the conditional logit model are widely used tools in applied econometrics and, particularly, in the eld of environmental valuation and welfare measurement in order to provide policymakers with sound information for making strategic choices. Monte Carlo simulations are used in this study to analyze biases due to omitted relevant variables and functional form misspeci cation in the conditional logit model. Using an easy-to-estimate speci cation test is effective to reduce the risks for large biases. One somewhat discouraging result is, however, that a moderate bias can be found even when the omitted variable is orthogonal to the explanatory variables included. This result is particularly interesting considering the increasing interest in using randomized experiments to obtain causal interpretations of key parameters. Randomization, with independence between included and omitted variables, does not guarantee unbiased estimates in the conditional logit model.

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Bibliographic Info

Paper provided by Department of Applied Economics II, Universidad de Valencia in its series Working Papers with number 1318.

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Length: 48 pages
Date of creation: Jul 2013
Date of revision:
Handle: RePEc:eec:wpaper:1318

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Keywords: Environmental valuation; Welfare measurements; Choice experiments; Monte Carlo analysis; Speci cation tests;

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