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Density Estimation Using Inverse and Reciprocal Inverse Guassian Kernels

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  • Olivier SCAILLET

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES))

Abstract

This paper introduces two new nonparametric estimators for probability density functions which have support on the non-negative half-line. These kernel estimators are based on some inverse Gaussian and reciprocal inverse Gaussian probability density functions used as kernels. We show that they share the same properties as those of gamma kernel estimators : they are free of boundary bias, always non-negative, and achieve the optimal rate of convergence for the mean integrated squarred error. Extensions to regression curve estimation and hazard rate estimation under random censoring are briefly discussed. Monte Carlo results concerning finite sample properties are reported for different distributions.

Suggested Citation

  • Olivier SCAILLET, 2001. "Density Estimation Using Inverse and Reciprocal Inverse Guassian Kernels," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2001017, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
  • Handle: RePEc:ctl:louvir:2001017
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    References listed on IDEAS

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    1. Jones, Larry E & Manuelli, Rodolfo E, 1992. "The Coordination Problem and Equilibrium Theories of Recessions," American Economic Review, American Economic Association, vol. 82(3), pages 451-471, June.
    2. DREZE, Jacques, 1999. "On the macroeconomics of uncertainty and incomplete markets," CORE Discussion Papers 1999064, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Oliver Hart, 1982. "A Model of Imperfect Competition with Keynesian Features," The Quarterly Journal of Economics, Oxford University Press, vol. 97(1), pages 109-138.
    4. Russell Cooper & Andrew John, 1988. "Coordinating Coordination Failures in Keynesian Models," The Quarterly Journal of Economics, Oxford University Press, vol. 103(3), pages 441-463.
    5. Diamond, Peter A, 1982. "Aggregate Demand Management in Search Equilibrium," Journal of Political Economy, University of Chicago Press, vol. 90(5), pages 881-894, October.
    6. Howitt, Peter & McAfee, R Preston, 1992. "Animal Spirits," American Economic Review, American Economic Association, vol. 82(3), pages 493-507, June.
    7. Roberts, John, 1987. "An Equilibrium Model with Involuntary Unemployment at Flexible, Competitive Prices and Wages," American Economic Review, American Economic Association, vol. 77(5), pages 856-874, December.
    8. Dreze, Jacques H., 1997. "Walras--Keynes equilibria coordination and macroeconomics," European Economic Review, Elsevier, vol. 41(9), pages 1735-1762, December.
    9. Jordi GalÎ, 1996. "Multiple equilibria in a growth model with monopolistic competition (*)," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 8(2), pages 251-266.
    10. John Bryant, 1983. "A Simple Rational Expectations Keynes-type Model," The Quarterly Journal of Economics, Oxford University Press, vol. 98(3), pages 525-528.
    11. Nobuhiro Kiyotaki, 1988. "Multiple Expectational Equilibria Under Monopolistic Competition," The Quarterly Journal of Economics, Oxford University Press, vol. 103(4), pages 695-713.
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    Citations

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    Cited by:

    1. Eduardo Fé, 2010. "An application of local linear regression with asymmetric kernels to regression discontinuity designs," The School of Economics Discussion Paper Series 1016, Economics, The University of Manchester.
    2. Bouezmarni, Taoufik & Rombouts, Jeroen V.K., 2010. "Nonparametric density estimation for positive time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 245-261, February.
    3. Hagmann, M. & Scaillet, O., 2007. "Local multiplicative bias correction for asymmetric kernel density estimators," Journal of Econometrics, Elsevier, vol. 141(1), pages 213-249, November.
    4. Abadir, Karim M. & Lawford, Steve, 2004. "Optimal asymmetric kernels," Economics Letters, Elsevier, vol. 83(1), pages 61-68, April.
    5. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    6. Xiaodong Jin & Janusz Kawczak, 2003. "Birnbaum-Saunders and Lognormal Kernel Estimators for Modelling Durations in High Frequency Financial Data," Annals of Economics and Finance, Society for AEF, vol. 4(1), pages 103-124, May.
    7. George Kapetanios, 2005. "Tests for Deterministic Parametric Structural Change in Regression Models," Working Papers 539, Queen Mary University of London, School of Economics and Finance.
    8. Hirukawa, Masayuki, 2010. "Nonparametric multiplicative bias correction for kernel-type density estimation on the unit interval," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 473-495, February.
    9. Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
    10. Kapetanios, George, 2008. "Bootstrap-based tests for deterministic time-varying coefficients in regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 534-545, December.
    11. Ané, Thierry & Métais, Carole, 2009. "The distribution of realized variances: Marginal behaviors, asymmetric dependence and contagion effects," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 134-150, June.

    More about this item

    Keywords

    Boundary bias; Inverse Gaussian kernel; Reciprocal inverse Gaussian kernel; Gamma kernel; Variable kernel; Density estimation;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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