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Comparing Nonparametric Regression Quantiles

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  • Cristian Huse
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    Abstract

    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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    Bibliographic Info

    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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    Related research

    Keywords: Quantile Regression; Semiparametric Estimation; Specification Testing; Engel Curve; Household Expenditure; Budget Shares.;

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    1. Roger Koenker & Zhijie Xiao, 2002. "Inference on the Quantile Regression Process," Econometrica, Econometric Society, vol. 70(4), pages 1583-1612, July.
    2. Newey, Whitney K, 1994. "The Asymptotic Variance of Semiparametric Estimators," Econometrica, Econometric Society, vol. 62(6), pages 1349-82, November.
    3. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    4. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762, October.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. 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.
    7. 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.
    8. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function is not Smooth," STICERD - Econometrics Paper Series /2003/450, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    9. 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.
    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. 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.
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