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Weighted and Two Stage Least Squares Estimation of Semiparametric Truncated Regression Models

Author

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  • Shakeeb Khan

    (University of Rochester)

  • Arthur Lewbel

    (Boston College)

Abstract

This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of the coefficients in a truncated regression model. The distribution of the errors is unknown and permits general forms of unknown heteroskedasticity. Also provided is an instrumental variables based two stage least squares estimator for this model, which can be used when some regressors are endogenous, mismeasured, or otherwise correlated with the errors. A simulation study indicates the new estimators perform well in finite samples. Our limiting distribution theory includes a new asymptotic trimming result addressing the boundary bias in first stage density estimation without knowledge of the support boundary.

Suggested Citation

  • Shakeeb Khan & Arthur Lewbel, 2002. "Weighted and Two Stage Least Squares Estimation of Semiparametric Truncated Regression Models," Boston College Working Papers in Economics 525, Boston College Department of Economics, revised 04 Sep 2006.
  • Handle: RePEc:boc:bocoec:525
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    References listed on IDEAS

    as
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    3. Arthur Lewbel, 1998. "Semiparametric Latent Variable Model Estimation with Endogenous or Mismeasured Regressors," Econometrica, Econometric Society, vol. 66(1), pages 105-122, January.
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    7. Pakes, Ariel & Pollard, David, 1989. "Simulation and the Asymptotics of Optimization Estimators," Econometrica, Econometric Society, vol. 57(5), pages 1027-1057, September.
    8. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
    9. Honore, Bo E. & Powell, James L., 1994. "Pairwise difference estimators of censored and truncated regression models," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 241-278.
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    Citations

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    Cited by:

    1. Yingying Dong & Arthur Lewbel, 2015. "A Simple Estimator for Binary Choice Models with Endogenous Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 82-105, February.
    2. Manthos D. Delis & Philip Molyneux & Fotios Pasiouras, 2011. "Regulations and Productivity Growth in Banking: Evidence from Transition Economies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(4), pages 735-764, June.
    3. Lewbel, Arthur, 2007. "Endogenous selection or treatment model estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 777-806, December.
    4. Brissimis, Sophocles N. & Delis, Manthos D. & Papanikolaou, Nikolaos I., 2008. "Exploring the nexus between banking sector reform and performance: Evidence from newly acceded EU countries," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2674-2683, December.
    5. Chu, Ba & Jacho-Chávez, David T., 2012. "k-NEAREST NEIGHBOR ESTIMATION OF INVERSE-DENSITY-WEIGHTED EXPECTATIONS WITH DEPENDENT DATA," Econometric Theory, Cambridge University Press, vol. 28(4), pages 769-803, August.
    6. Gao, Yichen & Li, Cong & Liang, Zhongwen, 2015. "Binary response correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 188(2), pages 421-434.
    7. Antonis Adam & Manthos Delis & Pantelis Kammas, 2014. "Fiscal decentralization and public sector efficiency: evidence from OECD countries," Economics of Governance, Springer, vol. 15(1), pages 17-49, February.
    8. Lewbel, Arthur, 2000. "Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 97(1), pages 145-177, July.
    9. repec:ebl:ecbull:v:3:y:2008:i:48:p:1-6 is not listed on IDEAS
    10. David Jacho-Chávez, 2008. "k nearest-neighbor estimation of inverse density weighted expectations," Economics Bulletin, AccessEcon, vol. 3(48), pages 1-6.
    11. Louis Grange & Felipe González & Ignacio Vargas & Rodrigo Troncoso, 2015. "A Logit Model With Endogenous Explanatory Variables and Network Externalities," Networks and Spatial Economics, Springer, vol. 15(1), pages 89-116, March.
    12. Li, Zhengtao & Hu, Bin, 2018. "Perceived health risk, environmental knowledge, and contingent valuation for improving air quality: New evidence from the Jinchuan mining area in China," Economics & Human Biology, Elsevier, vol. 31(C), pages 54-68.
    13. Sufian , Fadzlan & Kamarudin, Fakarudin, 2012. "Bank-Specific and Macroeconomic Determinants of Profitability of Bangladesh's Commercial Banks," Bangladesh Development Studies, Bangladesh Institute of Development Studies (BIDS), vol. 35(4), pages 1-29, December.
    14. Delis, Manthos D & Molyneux, Philip & Pasiouras, Fotios, 2009. "Regulations and productivity growth in banking," MPRA Paper 13891, University Library of Munich, Germany.
    15. Arthur Lewbel, 2012. "An Overview of the Special Regressor Method," Boston College Working Papers in Economics 810, Boston College Department of Economics.
    16. Ghazalian, Pascal & Tamini, Lota & Larue, Bruno & Gervais, Jean-Philippe, 2007. "A Gravity approach to evaluate the significance of trade liberalization in vertically-related goods in the presence of non-tariff barriers," MPRA Paper 2744, University Library of Munich, Germany.
    17. Matzkin, Rosa L., 2012. "Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 166(1), pages 106-115.
    18. Nir Billfeld & Moshe Kim, 2019. "Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision," Papers 1902.06286, arXiv.org.

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    More about this item

    Keywords

    Semiparametric; Truncated Regression; Heteroscedasticity; Latent Variable Models; Endogenous Regressors.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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