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Comparing Nonparametric Regression Quantiles

  • Cristian Huse
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    This paper investigates how conditional quantiles of a given distribution relate to each other. Given two conditional quantiles estimated nonparametrically, we investigate their relation by linking them through a parametric transformation. Asymptotic normality of the associated parameter vector is established, and the method is illustrated with data from the Family Expenditure Survey (FES) of UK households. The FES records expenditures of households on six broad categories of goods (alcohol, clothing, food, fuel, transport, and "other goods"), and the methodology is applied by estimating and comparing the conditional quantiles of the Engel relation. The only category for which expenditure can explain the shift in the quantile curves is for "other goods" relationship, indicating an increase in heterogeneity for better off households, suggesting a "taste for variety" effect as the expenditure level increases. For the remaining categories one cannot reject the null of a parallel shift of the quantile curves

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    File URL: http://repec.org/esLATM04/up.6957.1081972869.pdf
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    Paper provided by Econometric Society in its series Econometric Society 2004 Latin American Meetings with number 165.

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    Date of creation: 11 Aug 2004
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    Handle: RePEc:ecm:latm04:165
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    1. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
    2. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of semiparametric models when the criterion function is not smooth," LSE Research Online Documents on Economics 2167, London School of Economics and Political Science, LSE Library.
    3. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762.
    4. repec:cep:stiecm:/2003/450 is not listed on IDEAS
    5. White, Halbert & Kim, Tae-Hwan, 2002. "Estimation, Inference, and Specification Testing for Possibly Misspecified Quantile Regression," University of California at San Diego, Economics Working Paper Series qt1s38s0dn, Department of Economics, UC San Diego.
    6. Newey, W.K., 1989. "The Asymptotic Variance Of Semiparametric Estimotors," Papers 346, Princeton, Department of Economics - Econometric Research Program.
    7. Richard Blundell & Martin Browning & Ian Crawford, 2002. "Nonparametric Engel Curves and Revealed Preference," CAM Working Papers 2002-04, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    8. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    9. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-59, October.
    10. Andrews, Donald W K, 1994. "Asymptotics for Semiparametric Econometric Models via Stochastic Equicontinuity," Econometrica, Econometric Society, vol. 62(1), pages 43-72, January.
    11. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    12. Richard Blundell & Alan Duncan & Krishna Pendakur, 1998. "Semiparametric estimation and consumer demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 435-461.
    13. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
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