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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.

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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 27 (2010)
Issue (Month): 3 (May)
Pages: 641-647

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Handle: RePEc:eee:ecmode:v:27:y:2010:i:3:p:641-647

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Web page: http://www.elsevier.com/locate/inca/30411

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Keywords: Stochastic frontier models Threshold regression Technical efficiency Bootstrap Dairy production;

References

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  1. Bruce E. Hansen, 1996. "Sample Splitting and Threshold Estimation," Boston College Working Papers in Economics 319., Boston College Department of Economics, revised 12 May 1998.
  2. Bai, Jushan & Lumsdaine, Robin L & Stock, James H, 1998. "Testing for and Dating Common Breaks in Multivariate Time Series," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 395-432, July.
  3. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
  4. Ana Martin-Marcos & Cristina Suarez-Galvez, 2000. "Technical efficiency of Spanish manufacturing firms: a panel data approach," Applied Economics, Taylor & Francis Journals, vol. 32(10), pages 1249-1258.
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  7. Myungsup Kim & Yangseon Kim & Peter Schmidt, 2007. "On the accuracy of bootstrap confidence intervals for efficiency levels in stochastic frontier models with panel data," Journal of Productivity Analysis, Springer, vol. 28(3), pages 165-181, December.
  8. Bruce E. Hansen, 1997. "Threshold effects in non-dynamic panels: Estimation, testing and inference," Boston College Working Papers in Economics 365, Boston College Department of Economics.
  9. Larue, Bruno & Gervais, Jean-Philippe & Pouliot, Sebastien, 2007. "Should tariff-rate quotas mimic quotas?: Implications for trade liberalization under a supply management policy," The North American Journal of Economics and Finance, Elsevier, vol. 18(3), pages 247-261, December.
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  16. Hansen, Bruce E., 2000. "Testing for structural change in conditional models," Journal of Econometrics, Elsevier, vol. 97(1), pages 93-115, July.
  17. Enders, Walter & Granger, C. W. J., 1998. "Unit Root Tests and Asymmetric Adjustment with an Example Using the Term Structure of Interest Rates," Staff General Research Papers 1388, Iowa State University, Department of Economics.
  18. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
  19. Cornwell, C. & Schmidt, P., 1993. "Production Frontiers and Efficiency Measurement," Papers 427e, Georgia - College of Business Administration, Department of Economics.
  20. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
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  22. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
  23. Efthymios G. Tsionas & Subal C. Kumbhakar, 2004. "Markov switching stochastic frontier model," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 398-425, December.
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Cited by:
  1. 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).
  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.

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