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Evaluation of likelihood based tests for non-nested dichotomus choice contingent valuation models

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  • M. Genius
  • E. Strazzera

Abstract

Distributional assumptions are crucial in the estimation of the value of public projects assessed by means of contingent valuations analyses, and it would seem obvious that tests for model specification should play an important part in the statistical analysis. It can be observed, though, that when the competing hypotheses are non nested, the choice of the model is often based on heuristic grounds, or, at most, on deterministic selection model criteria such as Akaike s (1973). In this paper we study two alternative, probabilistic, approaches to checking model specification, that, like Akaike s, are based on the Kullback-Leibler Information Criterion (KLIC) - the model selection testing proposed by Vuong (1989) and the non nested model test proposed by Cox, in the simulated approach of Pesaran and Pesaran (1993). The three approaches are confronted by comparing their performance in selecting among different contingent valuation models applied to simulated data. Our preliminary results seem to warrant the use of Vuong s test, complemented in same cases by the application of the Cox test.

Suggested Citation

  • M. Genius & E. Strazzera, 2000. "Evaluation of likelihood based tests for non-nested dichotomus choice contingent valuation models," Working Paper CRENoS 200012, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  • Handle: RePEc:cns:cnscwp:200012
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    References listed on IDEAS

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    1. Cameron, Trudy Ann & James, Michelle D, 1987. "Efficient Estimation Methods for "Closed-ended' Contingent Valuation Surveys," The Review of Economics and Statistics, MIT Press, vol. 69(2), pages 269-276, May.
    2. Davidson, Russell & MacKinnon, James G, 1981. "Several Tests for Model Specification in the Presence of Alternative Hypotheses," Econometrica, Econometric Society, vol. 49(3), pages 781-793, May.
    3. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521471626, August.
    4. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    5. Hashem Pesaran, M. & Pesaran, Bahram, 1993. "A simulation approach to the problem of computing Cox's statistic for testing nonnested models," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 377-392.
    6. Cameron, Trudy Ann, 1988. "A new paradigm for valuing non-market goods using referendum data: Maximum likelihood estimation by censored logistic regression," Journal of Environmental Economics and Management, Elsevier, vol. 15(3), pages 355-379, September.
    7. Granger, Clive W. J. & King, Maxwell L. & White, Halbert, 1995. "Comments on testing economic theories and the use of model selection criteria," Journal of Econometrics, Elsevier, vol. 67(1), pages 173-187, May.
    8. Pesaran, M. H. & Weeks, M., 1999. "Non-nested Hypothesis Testing: An Overview," Cambridge Working Papers in Economics 9918, Faculty of Economics, University of Cambridge.
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    Cited by:

    1. R. Naylor, 2001. "Industry profits and market size under bilateral oligopoly," Working Paper CRENoS 200108, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    2. R. Naylor, 2001. "Firm profits and the number of firms under unionised oligopoly," Working Paper CRENoS 200109, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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