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

Author

Listed:
  • 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.

Suggested Citation

  • Tomás del Barrio Casto & William Nilsson & Andrés J. Picazo-Tadeo, 2013. "How wrong can you be, without noticing? Further evidence on speci cation errors in the Conditional Logit," Working Papers 1318, Department of Applied Economics II, Universidad de Valencia.
  • Handle: RePEc:eec:wpaper:1318
    as

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    File URL: ftp://147.156.210.157/RePEc/pdf/eec_1318.pdf
    File Function: First version, 2013
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    References listed on IDEAS

    as
    1. Torres, Cati & Hanley, Nick & Riera, Antoni, 2011. "How wrong can you be? Implications of incorrect utility function specification for welfare measurement in choice experiments," Journal of Environmental Economics and Management, Elsevier, vol. 62(1), pages 111-121, July.
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    More about this item

    Keywords

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

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D69 - Microeconomics - - Welfare Economics - - - Other
    • C99 - Mathematical and Quantitative Methods - - Design of Experiments - - - Other
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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