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Consistency of the instrumental weighted variables

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Abstract

A robust version of method of Instrumental Variables accommodating the idea of an implicit weighting the residuals is proposed and its properties studied. Firstly, it is shown that all solutions of the corresponding normal equations are bounded in probability. Then the weak consistency of them is proved. The algorithm, evaluating the estimate, is described and results of small MC study discussed.

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References

  • Arellano M., Bond S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58:277–297

    Article  MATH  Google Scholar 

  • Arellano M., Bover O. (1995). Another look at the instrumental variables estimation of error components models. Journal of Econometrics 68(1):29–52

    Article  MATH  Google Scholar 

  • Bickel P.J. (1975). One-step Huber estimates in the linear model. Journal of American Statistical Association 70:428–433

    Article  MATH  MathSciNet  Google Scholar 

  • Boček P., Lachout P. (1995). Linear programming approach to LMS-estimation. Memorial Volume of Computational Statistics and Data Analysis 19:129–134

    MATH  Google Scholar 

  • Bowden R.J., Turkington D.A. (1984). Instrumental Variables. Cambridge, Cambridge University Press

    MATH  Google Scholar 

  • Breiman L. (1968). Probability. London, Addison-Wesley

    MATH  Google Scholar 

  • Chatterjee S., Hadi A.S. (1988). Sensitivity Analysis in Linear Regression. New York, Wiley

    Book  MATH  Google Scholar 

  • Čížek, P. (2002). Robust estimation with discrete explanatory variables. COMPSTAT 2002, Berlin, pp. 509–514.

  • Der G., Everitt B.S. (2002). A handbook of statistical analyses using SAS. Boca Raton, Chapman and Hall/CRC Press

    Google Scholar 

  • Erickson T. (2001). Constructing instruments for regression with measurement error when no additional data are available. Econometrica, Notes and Comments, 69:221–222

    Google Scholar 

  • Fisher R.A. (1920). A mathematical examination of the methods of determining the accuracy of an observation by the mean error and by the mean squares error. Monthly Notes of Royal Astrophysical Society. 80:758–770

    Google Scholar 

  • Fisher R.A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of Royal Society London Series 222:309–368

    Article  Google Scholar 

  • Fox J. (2002). An R and S-PLUS companion to applied regression. Thousand Oaks, SAGE Publications

    Google Scholar 

  • Glivenko V.I. (1933). Sulla determinazione empirica delle leggi di probabilita. Giornale Istituto Italiano Attuari 4:92

    Google Scholar 

  • Hahn J., Hausman J. (2002). A new specification test for the validity of instrumental variables. Econometrica 70:163–189

    Article  MATH  MathSciNet  Google Scholar 

  • Hájek J., Šidák Z. (1967). Theory of rank test. New York, Academic Press

    Google Scholar 

  • Hampel F.R., Ronchetti E.M., Rousseeuw P.J., Stahel W.A. (1986). Robust statistics—The approach based on influence functions. New York, Wiley

    MATH  Google Scholar 

  • Heckman J.J. (1996). Randomization as instrumental variables. The Review of Economics and Statistics 78:336–341

    Article  Google Scholar 

  • Hettmansperger T.P., Sheather S.J. (1992). A cautionary note on the method of least median squares. The American Statistician 46:79–83

    Article  MathSciNet  Google Scholar 

  • Judge G.G., Griffiths W.E., Hill R.C., Lutkepohl H., Lee T.C. (1985). The theory and practice of econometrics (2nd edn). New York, Wiley

    Google Scholar 

  • Jurečková, J., Sen, P. K. (1993). Regression rank scores scale statistics and studentization in linear models. In Proceedings of the fifth Prague symposium on asymptotic statistics (pp. 111–121). Heidelberg: Physica-Verlag/Springer.

  • Kalina, J. (2004). Durbin–Watson test for least weighted squares. In Proceedings of COMPSTAT 2004 (pp. 1287–1294). Heidelberg: Physica-Verlag/Springer.

  • Manski F.C., Pepper J.V. (2000). Monotone instrumental variables: With application to the return to scholling. Econometrica 68:997–1010

    Article  MATH  MathSciNet  Google Scholar 

  • Mašíček, L. (2003). Diagnostika a sensitivita robustních odhadů.(Diagnostics and sensitivity of robust estimators, in Czech.) Disertační práce (PhD-disertation).

  • Mašíček, L. (2004a). Consistency of the least weighted squares estimator. In Statistics for industry and technology (pp. 183–194). Basel: Birkha̋ser Verlag.

  • Mašíček L. (2004b). Optimality of the least weighted squares estimator. Kybernetika 40:715–734

    MathSciNet  Google Scholar 

  • Plát, P. (2004a). Modifikace Whiteova testu pro nejmenší vážené čtverce. (Modification of White’s test for the least weighted squares, in Czech.) In J. Antoch, G. Dohnal (Eds.), ROBUST 2004 (pp. 291–298).

  • Plát, P. (2004b). The least weighted squares estimator. Proceedings of COMPSTAT 2004 (pp. 1653–1660). Heidelberg: Physica-Verlag/Springer.

  • Rousseeuw P.J. (1984). Least median of square regression. Journal of American Statistical Association, 79:871–880

    Article  MATH  MathSciNet  Google Scholar 

  • Rousseeuw P.J., Leroy A.M. (1987). Robust regression and outlier detection. New York, Wiley

    Book  MATH  Google Scholar 

  • Sargan J.D. (1988). Testing for misspecification after estimating using instrumental variables. In: Massouumi E. (ed). Contribution to econometrics: John Denis Sargan (Vol. 1). Cambridge, Cambridge University Press

    Google Scholar 

  • Stock J.H., Trebbi F. (2003). Who invented instrumental variable regression? Journal of Economic Perspectives 17:177–194

    Article  Google Scholar 

  • Van Huffel S. (2004). Total least squares and error-in-variables modelling: Bridging the gap between statistics, computational mathematics and enginnering. In: Antoch J. (ed). Proceedings in Computational Statistics, COMPSTAT 2004. Heidelberg, Physica-Verlag/Springer, pp. 539–555

    Google Scholar 

  • Víšek J.Á. (1992). Stability of regression model estimates with respect to subsamples. Computational Statistics 7:183–203

    MATH  MathSciNet  Google Scholar 

  • Víšek, J. Á. (1994). A cautionary note on the method of the Least Median of Squares reconsidered. In Transactions of the twelth prague conference on information theory, statistical decision functions and random processes (pp. 254–259).

  • Víšek J.Á. (1996a). Sensitivity analysis of M-estimates. Annals of the Institute of Statistical Mathematics 48:469–495

    Article  MATH  MathSciNet  Google Scholar 

  • Víšek J.Á. (1996b). On high breakdown point estimation. Computational Statistics 11:137–146

    MATH  MathSciNet  Google Scholar 

  • Víšek J.Á. (1998a). Robust instruments. In: Antoch J., Dohnal G., (eds). (published by Union of Czech Mathematicians and Physicists), Robust’98. Prague, MatFyz Press, pp. 195–224

    Google Scholar 

  • Víšek, J. Á. (1998b). Robust specification test. In M. Hušková, P. Lachout, J. Á. Víšek, Union of Czech Mathematicians and Physicists (Eds.), Proceedings of Prague Stochastics’98 (pp. 581–586). Prague: MatFyz Press.

  • Víšek J.Á. (2000a). On the diversity of estimates. Computational Statistics and Data analysis 34:67–89

    Article  MATH  Google Scholar 

  • Víšek, J. Á. (2000b). A new paradigm of point estimation. Data Analysis 2000/II, Modern statistical methods—modelling, regression, classification and data mining (pp. 195–230). Pardubice: Trilobyte.

  • Víšek J.Á. (2000c): Regression with high breakdown point. In: Antoch J., Dohnal G. (eds). Union of Czech Mathematicians and Physicists, Robust 2000. Prague, MatFyz Press, pp. 324–356

    Google Scholar 

  • Víšek J.Á. (2000d). Over- and underfitting the M-estimates. Bulletin of the Czech Econometric Society 7:53–83

    Google Scholar 

  • Víšek, J. Á. (2000e). Character of the Czech economy in transition. In Proceedings of the conference “The Czech society on the break of the third millennium” (pp. 181–205). Karolinum: Publishing House of the Charles University. ISBN 80-7184-825-5.

  • Víšek J.Á. (2002a). The least weighted squares I. The asymptotic linearity of normal equations. Bulletin of the Czech Econometric Society 9:31–58

    Google Scholar 

  • Víšek J.Á. (2002b). The least weighted squares II. Consistency and asymptotic normality. Bulletin of the Czech Econometric Society 9:1–28

    Google Scholar 

  • Víšek J.Á. (2002c). Sensitivity analysis of M-estimates of nonlinear regression model: Influence of data subsets. Annals of the Institute of Statistical Mathematics 54:261–290

    Article  MATH  MathSciNet  Google Scholar 

  • Víšek J.Á. (2002d). White test for the least weigthed squares. In: Klinke S., Ahrend P., Richter L. (eds). COMPSTAT 2002, Proceedings of the conference CompStat 2002—Short communications and poster (CD). Berlin, Springer

    Google Scholar 

  • Víšek J.Á. (2003a). Durbin-Watson statistic in robust regression. Probability and Mathematical Statistics 23:435–483

    MATH  MathSciNet  Google Scholar 

  • Víšek, J. Á. (2003b). Development of the Czech export in nineties. In Konsolidace vládnutí a podnikání v České republice a v Evropské unii I. Umění vládnout, ekonomika, politika, 2003 (pp. 193–220). Prague: MatFyz Press. ISBN 80-86732-00-2.

  • Víšek J.Á. (2006a). Kolmogorov–Smirnov statistics in multiple regression. In: Antoch J., Dohnal G. (eds). Proceedings of the ROBUST 2006, JČMF and KPMS MFF UK. Prague, MatFyz Press, pp. 367–374

    Google Scholar 

  • Víšek, J. Á. (2006b). The least trimmed squares. Part I—Consistency. Part II—\(\sqrt{n}\) -consistency. Part III—Asymptotic normality and Bahadur representation. Kybernetika, 42, 1–36, 181–202, 203–224.

  • Víšek J.Á. (2006c). Instrumental weighted variables—algorithm. In: Rizzi A., Vichi M. (eds). Proceedings of the COMPSTAT 2006. Heidelberg, Physica-Verlag/Springer, pp. 777–786

    Google Scholar 

  • Víšek J.Á. (2006d). The least trimmed squares. Sensitivity study. In: Hušková M., Janžura M. (eds). Proceedings of the Prague stochastics 2006. Prague, MatFyz Press, pp. 728–738

    Google Scholar 

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Correspondence to Jan Ámos Víšek.

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Research was supported by grant of GA ČR number 402/06/0408.

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Víšek, J.Á. Consistency of the instrumental weighted variables. Ann Inst Stat Math 61, 543–578 (2009). https://doi.org/10.1007/s10463-007-0159-8

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  • DOI: https://doi.org/10.1007/s10463-007-0159-8

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