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Instrumental variables estimation with flexible distribution

Author

Listed:
  • Christian Hansen

    (Institute for Fiscal Studies and Chicago GSB)

  • James B. McDonald

    (Institute for Fiscal Studies)

  • Whitney K. Newey

    (Institute for Fiscal Studies and MIT)

Abstract

Instrumental variables are often associated with low estimator precision. This paper explores efficiency gains which might be achievable using moment conditions which are nonlinear in the disturbances and are based on flexible parametric families for error distributions. We show that these estimators can achieve the semiparametric efficiency bound when the true error distribution is a member of the parametric family. Monte Carlo simulations demonstrate low efficiency loss in the case of normal error distributions and potentially significant efficiency improvements in the case of thick-tailed and/or skewed error distributions.

Suggested Citation

  • Christian Hansen & James B. McDonald & Whitney K. Newey, 2007. "Instrumental variables estimation with flexible distribution," CeMMAP working papers CWP21/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:21/07
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    File URL: http://cemmap.ifs.org.uk/wps/cwp2107.pdf
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    Cited by:

    1. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2016. "Endogeneity in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 190(2), pages 280-288.
    2. Lee, Adam & Mesters, Geert, 2024. "Locally robust inference for non-Gaussian linear simultaneous equations models," Journal of Econometrics, Elsevier, vol. 240(1).
    3. Poirier, Alexandre, 2017. "Efficient estimation in models with independence restrictions," Journal of Econometrics, Elsevier, vol. 196(1), pages 1-22.
    4. Kerman, Sean C. & McDonald, James B., 2013. "Skewness–kurtosis bounds for the skewed generalized T and related distributions," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2129-2134.
    5. Guizzardi, Andrea & Ballestra, Luca Vincenzo & D'Innocenzo, Enzo, 2022. "Hotel dynamic pricing, stochastic demand and covid-19," Annals of Tourism Research, Elsevier, vol. 97(C).
    6. Scott Alan Carson & Wael M. Al-Sawai & Scott A. Carson, 2023. "Partially Adaptive Econometric Methods and Vertically Integrated Majors in the Oil and Gas Industry," CESifo Working Paper Series 10733, CESifo.
    7. Zhi, Bangdong & Wang, Xiaojun & Xu, Fangming, 2020. "Impawn rate optimisation in inventory financing: A canonical vine copula-based approach," International Journal of Production Economics, Elsevier, vol. 227(C).
    8. McDonald, James & Stoddard, Olga & Walton, Daniel, 2018. "On using interval response data in experimental economics," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 72(C), pages 9-16.
    9. Sølvsten, Mikkel, 2020. "Robust estimation with many instruments," Journal of Econometrics, Elsevier, vol. 214(2), pages 495-512.
    10. Jason Cook & James McDonald, 2013. "Partially Adaptive Estimation of Interval Censored Regression Models," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 119-131, June.
    11. James B. McDonald & Daniel B. Walton & Bryan Chia, 2020. "Distributional Assumptions and the Estimation of Contingent Valuation Models," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 431-460, August.
    12. Tsionas, Efthymios G., 2013. "Bayesian inference in regression with Pearson disturbances," Economics Letters, Elsevier, vol. 118(1), pages 177-181.
    13. Scott A. Carson & James B. McDonald, 2018. "Partially Adaptive Econometric Methods and the Modern Obesity Epidemic," CESifo Working Paper Series 7058, CESifo.
    14. Siemsen, Thomas & Vilsmeier, Johannes, 2018. "On a quest for robustness: About model risk, randomness and discretion in credit risk stress tests," Discussion Papers 31/2018, Deutsche Bundesbank.
    15. Juraj Pekár & Mário Pčolár, 2022. "Empirical distribution of daily stock returns of selected developing and emerging markets with application to financial risk management," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(2), pages 699-731, June.
    16. Mamonov Mikhail E. & Parmeter Christopher F. & Prokhorov Artem B., 2022. "Dependence modeling in stochastic frontier analysis," Dependence Modeling, De Gruyter, vol. 10(1), pages 123-144, January.
    17. Ng Serena & Bai Jushan, 2009. "Selecting Instrumental Variables in a Data Rich Environment," Journal of Time Series Econometrics, De Gruyter, vol. 1(1), pages 1-34, April.
    18. Sikora, Grzegorz & Michalak, Anna & Bielak, Łukasz & Miśta, Paweł & Wyłomańska, Agnieszka, 2019. "Stochastic modeling of currency exchange rates with novel validation techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1202-1215.
    19. Oh Kang Kwon & Stephen Satchell, 2020. "The Distribution of Cross Sectional Momentum Returns When Underlying Asset Returns Are Student’s t Distributed," JRFM, MDPI, vol. 13(2), pages 1-19, February.
    20. Szarek, Dawid & Bielak, Łukasz & Wyłomańska, Agnieszka, 2020. "Long-term prediction of the metals’ prices using non-Gaussian time-inhomogeneous stochastic process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    21. John Deke & Mariel Finucane & Daniel Thal, "undated". "The BASIE (BAyeSian Interpretation of Estimates) Framework for Interpreting Findings from Impact Evaluations: A Practical Guide for Education Researchers," Mathematica Policy Research Reports 5a0d5dff375d42048799878be, Mathematica Policy Research.
    22. Shum, Wai Yan, 2020. "Modelling conditional skewness: Heterogeneous beliefs, short sale restrictions and market declines," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    23. Jetro Anttonen & Markku Lanne & Jani Luoto, 2024. "Statistically identified structural VAR model with potentially skewed and fat‐tailed errors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 422-437, April.

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