IDEAS home Printed from https://ideas.repec.org/p/aah/create/2008-09.html
   My bibliography  Save this paper

An analysis of the indicator saturation estimator as a robust regression estimator

Author

Listed:
  • Søren Johansen
  • Bent Nielsen

    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract

An algorithm suggested by Hendry (1999) for estimation in a regression with more regressors than observations, is analyzed with the purpose of finding an estimator that is robust to outliers and structural breaks. This estimator is an example of a one-step M-estimator based on Huber's skip function. The asymptotic theory is derived in the situation where there are no outliers or structural breaks using empirical process techniques. Stationary processes, trend stationary autoregressions and unit root processes are considered.

Suggested Citation

  • Søren Johansen & Bent Nielsen, 2008. "An analysis of the indicator saturation estimator as a robust regression estimator," CREATES Research Papers 2008-09, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2008-09
    as

    Download full text from publisher

    File URL: https://repec.econ.au.dk/repec/creates/rp/08/rp08_09.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, September.
    2. Nielsen, Bent, 2005. "Strong Consistency Results For Least Squares Estimators In General Vector Autoregressions With Deterministic Terms," Econometric Theory, Cambridge University Press, vol. 21(3), pages 534-561, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. J. James Reade & Ulrich Volz, 2011. "From the General to the Specific," Discussion Papers 11-18, Department of Economics, University of Birmingham.
    2. Neil R. Ericsson, 2008. "The Fragility of Sensitivity Analysis: An Encompassing Perspective," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 895-914, December.
    3. David Hendry & Carlos Santos, 2010. "An Automatic Test of Super Exogeneity," Economics Series Working Papers 476, University of Oxford, Department of Economics.
    4. Neil R. Ericsson & Steven B. Kamin, 2008. "Constructive data mining: modeling Argentine broad money demand," International Finance Discussion Papers 943, Board of Governors of the Federal Reserve System (U.S.).

    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. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
    2. Javier Alejo & Nicolás Badaracco, 2015. "Counterfactual Distributions in Bivariate Models—A Conditional Quantile Approach," Econometrics, MDPI, vol. 3(4), pages 1-14, November.
    3. Zhao, Zhibiao & Wu, Wei Biao, 2009. "Nonparametric inference of discretely sampled stable Lévy processes," Journal of Econometrics, Elsevier, vol. 153(1), pages 83-92, November.
    4. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    5. Ho, Hwai-Chung, 2015. "Sample quantile analysis for long-memory stochastic volatility models," Journal of Econometrics, Elsevier, vol. 189(2), pages 360-370.
    6. Asongu, Simplice A. & Odhiambo, Nicholas M., 2021. "Inequality, finance and renewable energy consumption in Sub-Saharan Africa," Renewable Energy, Elsevier, vol. 165(P1), pages 678-688.
    7. Yebin Cheng & Jan G. De Gooijer & Dawit Zerom, 2011. "Efficient Estimation of an Additive Quantile Regression Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(1), pages 46-62, March.
    8. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    9. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    10. Ikechukwu Darlington Nwaka & Fatma Guven-Lisaniler & Gulcay Tuna, 2016. "Gender wage differences in Nigerian self and paid employment: Do marriage and children matter?," The Economic and Labour Relations Review, , vol. 27(4), pages 490-510, December.
    11. Chao, Shih-Kang & Härdle, Wolfgang K. & Yuan, Ming, 2021. "Factorisable Multitask Quantile Regression," Econometric Theory, Cambridge University Press, vol. 37(4), pages 794-816, August.
    12. Benjamin Hofner & Andreas Mayr & Nikolay Robinzonov & Matthias Schmid, 2014. "Model-based boosting in R: a hands-on tutorial using the R package mboost," Computational Statistics, Springer, vol. 29(1), pages 3-35, February.
    13. repec:hal:wpspec:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    14. Xiaohong Chen & Roger Koenker & Zhijie Xiao, 2009. "Copula-based nonlinear quantile autoregression," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 50-67, January.
    15. Giovanni Bonaccolto & Massimiliano Caporin & Sandra Paterlini, 2018. "Asset allocation strategies based on penalized quantile regression," Computational Management Science, Springer, vol. 15(1), pages 1-32, January.
    16. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
    17. Abeliansky, Ana & Krenz, Astrid, 2015. "Democracy and international trade: Differential effects from a panel quantile regression framework," University of Göttingen Working Papers in Economics 243, University of Goettingen, Department of Economics.
    18. Das, Priyam & Ghosal, Subhashis, 2018. "Bayesian non-parametric simultaneous quantile regression for complete and grid data," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 172-186.
    19. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    20. repec:hum:wpaper:sfb649dp2015-031 is not listed on IDEAS
    21. Normann Lorenz, 2014. "Using quantile regression for optimal risk adjustment," Research Papers in Economics 2014-11, University of Trier, Department of Economics.

    More about this item

    Keywords

    Empirical processes; Huber's skip; indicator saturation; M-estimator; outlier robustness; vector autoregressive process;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    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:aah:create:2008-09. 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: the person in charge (email available below). General contact details of provider: http://www.econ.au.dk/afn/ .

    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.