IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

An Information Theoretic Approach to Flexible Stochastic Frontier Models

Parametric stochastic frontier models have a long history in applied production eco- nomics, but the class of tractible parametric models is relatively small. Consequently, researchers have recently considered nonparametric alternatives such as kernel den- sity estimators, functional approximations, and data envelopment analysis (DEA). The purpose of this paper is to present an information theoretic approach to constructing more flexible classes of parametric stochastic frontier models. Further, the proposed class of models nests all of the commonly used parametric methods as special cases, and the proposed modeling framework provides a comprehensive means to conduct model specification tests. The modeling framework is also extended to develop information theoretic measures of mean technical efficiency and to construct a profile likelihood estimator of the stochastic frontier model.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://economics.missouri.edu/working-papers/2007/wp0717_millerd.pdf
Our checks indicate that this address may not be valid because: 404 Not Found (http://economics.missouri.edu/working-papers/2007/wp0717_millerd.pdf [301 Moved Permanently]--> https://economics.missouri.edu/working-papers/2007/wp0717_millerd.pdf). If this is indeed the case, please notify (Valerie Kulp)


Download Restriction: no

Paper provided by Department of Economics, University of Missouri in its series Working Papers with number 0717.

as
in new window

Length: 26 pgs.
Date of creation: 16 Jul 2007
Date of revision:
Handle: RePEc:umc:wpaper:0717
Contact details of provider: Postal: 118 Professional Building, Columbia, MO 65211
Phone: (573) 882-0063
Fax: (573) 882-2697
Web page: http://economics.missouri.edu/

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Schmidt, Peter & Lin, Tsai-Fen, 1984. "Simple tests of alternative specifications in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 24(3), pages 349-361, March.
  2. Ryu, Hang K., 1993. "Maximum entropy estimation of density and regression functions," Journal of Econometrics, Elsevier, vol. 56(3), pages 397-440, April.
  3. Yuichi Kitamura & Michael Stutzer, 1997. "An Information-Theoretic Alternative to Generalized Method of Moments Estimation," Econometrica, Econometric Society, vol. 65(4), pages 861-874, July.
  4. Imbens, Guido W, 1997. "One-Step Estimators for Over-Identified Generalized Method of Moments Models," Review of Economic Studies, Wiley Blackwell, vol. 64(3), pages 359-83, July.
  5. Zellner, A & Revankar, N S, 1969. "Generalized Production Functions," Review of Economic Studies, Wiley Blackwell, vol. 36(106), pages 241-50, April.
  6. Robin Sickles & David Good & Lullit Getachew, 2002. "Specification of Distance Functions Using Semi- and Nonparametric Methods with an Application to the Dynamic Performance of Eastern and Western European Air Carriers," Journal of Productivity Analysis, Springer, vol. 17(1), pages 133-155, January.
  7. Sengupta, Jati K., 1992. "The maximum entropy approach in production frontier estimation," Mathematical Social Sciences, Elsevier, vol. 25(1), pages 41-57, December.
  8. D. Ormoneit & H. White, 1999. "An efficient algorithm to compute maximum entropy densities," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 127-140.
  9. Lee, Lung-Fei & Tyler, William G., 1978. "The stochastic frontier production function and average efficiency : An empirical analysis," Journal of Econometrics, Elsevier, vol. 7(3), pages 385-389, April.
  10. Douglas J. Miller, 2002. "Entropy-Based Methods of Modeling Stochastic Production Efficiency," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(5), pages 1264-1270.
  11. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June.
  12. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:umc:wpaper:0717. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Valerie Kulp)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.