IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2301.04439.html
   My bibliography  Save this paper

Uniform Inference in Linear Error-in-Variables Models: Divide-and-Conquer

Author

Listed:
  • Tom Boot
  • Art=uras Juodis

Abstract

It is customary to estimate error-in-variables models using higher-order moments of observables. This moments-based estimator is consistent only when the coefficient of the latent regressor is assumed to be non-zero. We develop a new estimator based on the divide-and-conquer principle that is consistent for any value of the coefficient of the latent regressor. In an application on the relation between investment, (mismeasured) Tobin's $q$ and cash flow, we find time periods in which the effect of Tobin's $q$ is not statistically different from zero. The implausibly large higher-order moment estimates in these periods disappear when using the proposed estimator.

Suggested Citation

  • Tom Boot & Art=uras Juodis, 2023. "Uniform Inference in Linear Error-in-Variables Models: Divide-and-Conquer," Papers 2301.04439, arXiv.org.
    • Handle: RePEc:arx:papers:2301.04439
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2301.04439
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Timothy Erickson & Toni M. Whited, 2000. "Measurement Error and the Relationship between Investment and q," Journal of Political Economy, University of Chicago Press, vol. 108(5), pages 1027-1057, October.
    2. Leigh A. Riddick & Toni M. Whited, 2009. "The Corporate Propensity to Save," Journal of Finance, American Finance Association, vol. 64(4), pages 1729-1766, August.
    3. Joshua D. Angrist & Alan B. Krueger, 1993. "Split Sample Instrumental Variables," Working Papers 699, Princeton University, Department of Economics, Industrial Relations Section..
    4. Pal, Manoranjan, 1980. "Consistent moment estimators of regression coefficients in the presence of errors in variables," Journal of Econometrics, Elsevier, vol. 14(3), pages 349-364, December.
    5. Steven M. Fazzari & R. Glenn Hubbard & Bruce C. Petersen, 1988. "Financing Constraints and Corporate Investment," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 19(1), pages 141-206.
    6. Dagenais, Marcel G. & Dagenais, Denyse L., 1997. "Higher moment estimators for linear regression models with errors in the variables," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 193-221.
    7. Andrei, Daniel & Mann, William & Moyen, Nathalie, 2019. "Why did the q theory of investment start working?," Journal of Financial Economics, Elsevier, vol. 133(2), pages 251-272.
    8. S. M. Schennach & Yingyao Hu, 2013. "Nonparametric Identification and Semiparametric Estimation of Classical Measurement Error Models Without Side Information," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 177-186, March.
    9. Susanne M. Schennach, 2016. "Recent Advances in the Measurement Error Literature," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 341-377, October.
    10. Arthur Lewbel, 1997. "Constructing Instruments for Regressions with Measurement Error when no Additional Data are Available, with an Application to Patents and R&D," Econometrica, Econometric Society, vol. 65(5), pages 1201-1214, September.
    11. Angrist, Joshua D & Krueger, Alan B, 1995. "Split-Sample Instrumental Variables Estimates of the Return to Schooling," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 225-235, April.
    12. Erickson, Timothy & Whited, Toni M., 2002. "Two-Step Gmm Estimation Of The Errors-In-Variables Model Using High-Order Moments," Econometric Theory, Cambridge University Press, vol. 18(3), pages 776-799, June.
    13. Erickson, Timothy & Jiang, Colin Huan & Whited, Toni M., 2014. "Minimum distance estimation of the errors-in-variables model using linear cumulant equations," Journal of Econometrics, Elsevier, vol. 183(2), pages 211-221.
    14. Shi, Chengchun & Lu, Wenbin & Song, Rui, 2018. "A massive data framework for M-estimators with cubic-rate," LSE Research Online Documents on Economics 102111, London School of Economics and Political Science, LSE Library.
    15. Chengchun Shi & Wenbin Lu & Rui Song, 2018. "A Massive Data Framework for M-Estimators with Cubic-Rate," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(524), pages 1698-1709, October.
    16. Kapteyn, Arie & Wansbeek, Tom, 1983. "Identification in the Linear Errors in Variables Model," Econometrica, Econometric Society, vol. 51(6), pages 1847-1849, November.
    17. Bekker, Paul A, 1986. "Identification in the Linear Errors in Variables Model: Comment," Econometrica, Econometric Society, vol. 54(1), pages 215-217, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erickson, Timothy & Jiang, Colin Huan & Whited, Toni M., 2014. "Minimum distance estimation of the errors-in-variables model using linear cumulant equations," Journal of Econometrics, Elsevier, vol. 183(2), pages 211-221.
    2. Hu, Yingyao & Schennach, Susanne & Shiu, Ji-Liang, 2022. "Identification of nonparametric monotonic regression models with continuous nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 226(2), pages 269-294.
    3. Gospodinov, Nikolay & Komunjer, Ivana & Ng, Serena, 2017. "Simulated minimum distance estimation of dynamic models with errors-in-variables," Journal of Econometrics, Elsevier, vol. 200(2), pages 181-193.
    4. Nikolay Gospodinov & Ivana Komunjer & Serena Ng, 2014. "Minimum Distance Estimation of Dynamic Models with Errors-In-Variables," FRB Atlanta Working Paper 2014-11, Federal Reserve Bank of Atlanta.
    5. Susanne M. Schennach, 2012. "Measurement error in nonlinear models - a review," CeMMAP working papers 41/12, Institute for Fiscal Studies.
    6. Christian Calmès & Denis Cormier & Francois Éric Racicot & Raymond Théoret, 2012. "Firms' Accruals and Tobin’s q," RePAd Working Paper Series UQO-DSA-wp032012, Département des sciences administratives, UQO.
    7. Chalak, Karim & Kim, Daniel, 2020. "Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt," Journal of Econometrics, Elsevier, vol. 214(2), pages 413-432.
    8. Moshirian, Fariborz & Nanda, Vikram & Vadilyev, Alexander & Zhang, Bohui, 2017. "What drives investment–cash flow sensitivity around the World? An asset tangibility Perspective," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 1-17.
    9. Mauricio Jara‐Bertín & Cristian Pinto‐Gutiérrez & Carlos Pombo, 2021. "The effect of intra‐group loans on the cash flow sensitivity of cash: Evidence from Chile," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 374-403, June.
    10. Christian Gourieroux & Joann Jasiak, 2023. "Dynamic deconvolution and identification of independent autoregressive sources," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 151-180, March.
    11. Bonhomme, Stphane & Robin, Jean-Marc, 2009. "Consistent noisy independent component analysis," Journal of Econometrics, Elsevier, vol. 149(1), pages 12-25, April.
    12. Aydin Ozkan & Roberto J. Santillán‐Salgado & Yilmaz Yildiz & María del Rocío Vega Zavala, 2020. "What Happened To The Willingness Of Companies To Invest After The Financial Crisis? Evidence From Latin American Countries," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 43(2), pages 231-262, May.
    13. Machokoto, Michael & Areneke, Geofry, 2020. "Does innovation and financial constraints affect the propensity to save in emerging markets?," Research in International Business and Finance, Elsevier, vol. 52(C).
    14. Susanne M. Schennach & Yingyao Hu & Arthur Lewbel, 2007. "Nonparametric identification of the classical errors-in-variables model without side information," Boston College Working Papers in Economics 674, Boston College Department of Economics.
    15. Milan Hladík & Michal Černý & Jaromír Antoch, 2020. "EIV regression with bounded errors in data: total ‘least squares’ with Chebyshev norm," Statistical Papers, Springer, vol. 61(1), pages 279-301, February.
    16. Liao, Shushu, 2021. "The effect of credit shocks in the context of labor market frictions," Journal of Banking & Finance, Elsevier, vol. 125(C).
    17. Meijer, Erik & Spierdijk, Laura & Wansbeek, Tom, 2017. "Consistent estimation of linear panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 169-180.
    18. Peters, Ryan H. & Taylor, Lucian A., 2017. "Intangible capital and the investment-q relation," Journal of Financial Economics, Elsevier, vol. 123(2), pages 251-272.
    19. Timothy Erickson & Toni M. Whited, 2012. "Treating Measurement Error in Tobin's q," The Review of Financial Studies, Society for Financial Studies, vol. 25(4), pages 1286-1329.
    20. Chen, Hong-Yi & Lee, Alice C. & Lee, Cheng-Few, 2015. "Alternative errors-in-variables models and their applications in finance research," The Quarterly Review of Economics and Finance, Elsevier, vol. 58(C), pages 213-227.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2301.04439. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.