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Parametric Nonlinear Regression with Endogenous Switching

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  • Joseph Terza

Abstract

Based on the insightful work of Olsen (1980) for the linear context, a generic and unifying framework is developed that affords a simple extension of the classical method of Heckman (1974, 1976, 1978, 1979) to a broad class of nonlinear regression models involving endogenous switching and its two most common incarnations, endogenous sample selection and endogenous treatment effects. The approach should be appealing to applied researchers for three reasons. First, econometric applications involving endogenous switching abound. Secondly, the approach requires neither linearity of the regression function nor full parametric specification of the model. It can, in fact, be applied under the minimal parametric assumptions—i.e., specification of only the conditional means of the outcome and switching variables. Finally, it is amenable to relatively straightforward estimation methods. Examples of applications of the method are discussed.

Suggested Citation

  • Joseph Terza, 2009. "Parametric Nonlinear Regression with Endogenous Switching," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 555-580.
  • Handle: RePEc:taf:emetrv:v:28:y:2009:i:6:p:555-580
    DOI: 10.1080/07474930802473751
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    1. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, January.
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    Cited by:

    1. Massimiliano Bratti & Alfonso Miranda, 2011. "Endogenous treatment effects for count data models with endogenous participation or sample selection," Health Economics, John Wiley & Sons, Ltd., vol. 20(9), pages 1090-1109, September.
    2. Nigel Key & William D. McBride, 2014. "Sub-therapeutic Antibiotics and the Efficiency of U.S. Hog Farms," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(3), pages 831-850.
    3. Andrew J. Epstein & Jonathan D. Ketcham, 2014. "Information technology and agency in physicians' prescribing decisions," RAND Journal of Economics, RAND Corporation, vol. 45(2), pages 422-448, June.
    4. Park, Timothy A., 2014. "Assessing Performance Impacts in Food Retail Distribution Systems: A Stochastic Frontier Model Correcting for Sample Selection," Agricultural and Resource Economics Review, Cambridge University Press, vol. 43(3), pages 373-389, December.
    5. Wooldridge, Jeffrey M., 2014. "Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 182(1), pages 226-234.
    6. Rabbitt, Matthew P., 2013. "Measuring the Effect of Supplemental Nutrition Assistance Program Participation on Food Insecurity Using a Behavioral Rasch Selection Model," UNCG Economics Working Papers 13-20, University of North Carolina at Greensboro, Department of Economics.
    7. Terza, Joseph V. & Tsai, Wei-Der, 2006. "Censored Probit Estimation with Correlation near the Boundary: A Useful Reparameteriztion," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 2(1), pages 1-12.
    8. Matthew P. Rabbitt, 2017. "Estimating Treatment Effects in the Presence of Correlated Binary Outcomes and Contemporaneous Selection," 2017 Stata Conference 23, Stata Users Group.
    9. Abdul-Rahaman, Awal & Abdulai, Awudu, 2018. "Do farmer groups impact on farm yield and efficiency of smallholder farmers? Evidence from rice farmers in northern Ghana," Food Policy, Elsevier, vol. 81(C), pages 95-105.
    10. Boris E. Bravo‐Ureta & Mario González‐Flores & William Greene & Daniel Solís, 2021. "Technology and Technical Efficiency Change: Evidence from a Difference in Differences Selectivity Corrected Stochastic Production Frontier Model," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 362-385, January.
    11. Hung-pin Lai, 2015. "Maximum likelihood estimation of the stochastic frontier model with endogenous switching or sample selection," Journal of Productivity Analysis, Springer, vol. 43(1), pages 105-117, February.
    12. Keay, Myoung-Jin, 2016. "Partial copula methods for models with multiple discrete endogenous explanatory variables and sample selection," Economics Letters, Elsevier, vol. 144(C), pages 85-87.
    13. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    14. Sung, Jae-hoon & Miranowski, John A., 2016. "Information technologies and field-level chemical use for corn production," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235858, Agricultural and Applied Economics Association.
    15. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    16. Fitzenberger, Bernd & Furdas, Marina & Sajons, Christoph, 2016. "End-of-year spending and the long-run employment effects of training programs for the unemployed," ZEW Discussion Papers 16-084, ZEW - Leibniz Centre for European Economic Research.
    17. Takuya Hasebe, 2018. "Treatment effect estimators for count data models," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1868-1873, November.
    18. Boris E. Bravo-Ureta, 2014. "Stochastic frontiers, productivity effects and development projects," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 51-58.

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