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Threshold effects in panel data stochastic frontier models of dairy production in Canada

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  • Yélou, Clément
  • Larue, Bruno
  • Tran, Kien C.

Abstract

One of the most enduring problems in econometrics is how to properly account for heterogeneity among firms. Threshold regression models are intuitively appealing methods to deal with this issue. We consider a fixed-effect panel data stochastic frontier model (Schmidt and Sickles, 1984; Martin-Marcos and Suarez-Galvez, 2000) and, relying on Hansen (1999, 2000a), we propose an estimator that accommodates multiple thresholds. Our model assumes absence of any unmeasured time invariant heterogeneity across firms as in Greene (2005, p. 277). Slope and threshold parameters can be estimated using a within estimator combined with a grid search over the threshold parameters. Testing for threshold effects is problematic because threshold parameters are not identified under the null hypothesis, a case of the so-called Davies' problem. We apply the bootstrap procedure proposed by Hansen (1999, 2000a) to test for the presence of thresholds. An asymptotic confidence set for the threshold parameter can be obtained by inverting an LR test, using the distribution result presented in Hansen (1999, 2000a). Our empirical application features a panel of Quebec dairy farms. We use farm size as the threshold variable. The presence of a trend in the specification matters for the determination of the number of thresholds. Technical efficiency scores and rankings of farms estimated from competing model specifications are highly correlated and do not vary significantly across groups of farm sizes defined by the threshold parameter values.

Suggested Citation

  • Yélou, Clément & Larue, Bruno & Tran, Kien C., 2010. "Threshold effects in panel data stochastic frontier models of dairy production in Canada," Economic Modelling, Elsevier, vol. 27(3), pages 641-647, May.
  • Handle: RePEc:eee:ecmode:v:27:y:2010:i:3:p:641-647
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. M.K. Ndegue Fongue & Lota D. Tamini & B. Larue & G.E. West, 2014. "Efficiences technique et environnementale en agriculture: le cas du bassin de la rivière Chaudière au Québec," Cahiers de recherche CREATE 2014-10, CREATE.
    2. Hung-pin Lai, 2013. "Estimation of the threshold stochastic frontier model in the presence of an endogenous sample split variable," Journal of Productivity Analysis, Springer, vol. 40(2), pages 227-237, October.
    3. Lambert, Remy, 2012. "A Primer on the Economics of Supply Management and Food Supply Chains," Working Papers 125246, Structure and Performance of Agriculture and Agri-products Industry (SPAA).
    4. Lota D. Tamini & Bruno Larue & Gale E. West & Moise K.Ndegue Fongue, 2016. "Agricultural production and pollutant runoffs in QuŽbecÕs Chaudi re river watershed: what are the potential environmental gains?," Cahiers de recherche CREATE 2016-2, CREATE.
    5. Menegaki, Angeliki N., 2013. "Accounting for unobserved management in renewable energy & growth," Energy, Elsevier, vol. 63(C), pages 345-355.
    6. Alphonse G. Singbo & Bruno Larue, 2014. "Scale Economies and Technical Efficiency of Quebec Dairy Farms," Cahiers de recherche CREATE 2014-7, CREATE.
    7. Brea, Humberto & Grifell-Tatje, Emili & Orea, Luis, 2012. "Expectations with Unrealistic Optimism: An Empirical Application," Efficiency Series Papers 2012/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    8. Rosemarie Bégin & Lota D. Tamini & Maurice Doyon, 2014. "L'effet du travail hors-ferme sur l'efficacité technique des fermes laitières québécoises: un modèle intégrant les biais de sélection sur les observables et inobservables," Cahiers de recherche CREATE 2014-9, CREATE.
    9. Camilla Mastromarco & Laura Serlenga & Yongcheol Shin, 2012. "Is Globalization Driving Efficiency? A Threshold Stochastic Frontier Panel Data Modeling Approach," Review of International Economics, Wiley Blackwell, vol. 20(3), pages 563-579, August.

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