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On the instrument functional form with a binary endogenous explanatory variable

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  • Xu, Ruonan

Abstract

I demonstrate that, in a linear regression model with a binary endogenous explanatory variable, a two-step IV estimation procedure could avoid the weak identification issue caused by a first-stage linear projection onto the linear instruments.

Suggested Citation

  • Xu, Ruonan, 2021. "On the instrument functional form with a binary endogenous explanatory variable," Economics Letters, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:ecolet:v:206:y:2021:i:c:s0165176521002706
    DOI: 10.1016/j.econlet.2021.109993
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    References listed on IDEAS

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    1. Taryn Dinkelman, 2011. "The Effects of Rural Electrification on Employment: New Evidence from South Africa," American Economic Review, American Economic Association, vol. 101(7), pages 3078-3108, December.
    2. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    3. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    4. Dieterle, Steven G. & Snell, Andy, 2016. "A simple diagnostic to investigate instrument validity and heterogeneous effects when using a single instrument," Labour Economics, Elsevier, vol. 42(C), pages 76-86.
    5. Jun, Sung Jae & Pinkse, Joris, 2012. "Testing Under Weak Identification With Conditional Moment Restrictions," Econometric Theory, Cambridge University Press, vol. 28(6), pages 1229-1282, December.
    6. José Luis Montiel Olea & Carolin Pflueger, 2013. "A Robust Test for Weak Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 358-369, July.
    7. Ruud, Paul A, 1983. "Sufficient Conditions for the Consistency of Maximum Likelihood Estimation Despite Misspecifications of Distribution in Multinomial Discrete Choice Models," Econometrica, Econometric Society, vol. 51(1), pages 225-228, January.
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    Cited by:

    1. Johannes Boehm & Jan Sonntag, 2023. "Vertical Integration and Foreclosure: Evidence from Production Network Data," Management Science, INFORMS, vol. 69(1), pages 141-161, January.
    2. repec:hal:spmain:info:hdl:2441/44gofgf80399mp5fq5q50vv5t6 is not listed on IDEAS
    3. Francisco Pedraja-Chaparro & Daniel Santín & Rosa Simancas, 2022. "Show me the money! The impact of a conditional cash transfer on educational achievement," Empirical Economics, Springer, vol. 63(5), pages 2721-2750, November.
    4. Sapna Goel, 2022. "Effect of Deagrarianization at the Household Level on the Scale and Nature of Women’s Work in Rural India," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 65(4), pages 1053-1082, December.
    5. Angel, Marco Del & Richardson, Gary, 2024. "Independent regulators and financial stability evidence from gubernatorial election campaigns in the Progressive Era," Journal of Financial Economics, Elsevier, vol. 152(C).
    6. repec:hal:wpspec:info:hdl:2441/44gofgf80399mp5fq5q50vv5t6 is not listed on IDEAS

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    More about this item

    Keywords

    Weak instruments; Fitted probit probability; F statistic; Heteroskedasticity; Two-stage least squares;
    All these keywords.

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure

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