IDEAS home Printed from https://ideas.repec.org/a/tsj/stataj/v3y2003i2p148-156.html
   My bibliography  Save this article

Adaptive kernel density estimation

Author

Listed:
  • Philippe Van Kerm

    (CEPS/INSTEAD, Luxembourg)

Abstract

This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an "adaptive kernel" approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated density functions. Copyright 2003 by Stata Corporation.

Suggested Citation

  • Philippe Van Kerm, 2003. "Adaptive kernel density estimation," Stata Journal, StataCorp LP, vol. 3(2), pages 148-156, June.
  • Handle: RePEc:tsj:stataj:v:3:y:2003:i:2:p:148-156
    as

    Download full text from publisher

    File URL: http://www.stata-journal.com/software/sj3-2/st0037/
    Download Restriction: no

    File URL: http://www.stata-journal.com/sjpdf.html?articlenum=st0037
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Isaias H. Salgado-Ugarte & Marco A. Perez-Hernandez, 2003. "Exploring the use of variable bandwidth kernel density estimators," Stata Journal, StataCorp LP, vol. 3(2), pages 133-147, June.
    2. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, December.
    3. Isaias Hazarmabeth Salgado-Ugarte & Makoto Shimizu & Toru Taniuchi, 1996. "Practical rules for bandwidth selection in univariate density estimation," Stata Technical Bulletin, StataCorp LP, vol. 5(27).
    4. Isaias Hazarmabeth Salgado-Ugarte & Makoto Shimizu & Toru Taniuchi, 1994. "Exploring the shape of univariate data using kernel density estimators," Stata Technical Bulletin, StataCorp LP, vol. 3(16).
    Full references (including those not matched with items on IDEAS)

    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. Vivek Dehejia & Marcel Voia, 2008. "International Income Comparisons and Location Choice: Methodology, Analysis, and Implications," Carleton Economic Papers 08-02, Carleton University, Department of Economics.
    2. Vaona, A. & Schiavo, S., 2007. "Nonparametric and semiparametric evidence on the long-run effects of inflation on growth," Economics Letters, Elsevier, vol. 94(3), pages 452-458, March.
    3. Sebastian Weber, 2009. "European Financial Market Integration: A Closer Look at Government Bonds in Eurozone Countries," Working Paper / FINESS 1.1b, DIW Berlin, German Institute for Economic Research.
    4. Harding, Don & Pagan, Adrian, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 86-95.
    5. Menzel, Konrad, 2014. "Consistent estimation with many moment inequalities," Journal of Econometrics, Elsevier, vol. 182(2), pages 329-350.
    6. Ural Marchand, Beyza, 2012. "Tariff pass-through and the distributional effects of trade liberalization," Journal of Development Economics, Elsevier, vol. 99(2), pages 265-281.
    7. 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.
    8. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    9. Eileen Wright & Patrick Royston, 1998. "Age-specific reference intervals for normally distributed data," Stata Technical Bulletin, StataCorp LP, vol. 7(38).
    10. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    11. David Fairris & Gurleen Popli & Eduardo Zepeda, 2008. "Minimum Wages and the Wage Structure in Mexico," Review of Social Economy, Taylor & Francis Journals, vol. 66(2), pages 181-208.
    12. Javier Parada Gómez Urquiza & Alejandro López-Feldman, 2013. "Poverty dynamics in rural Mexico: What does the future hold?," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 55-74, November.
    13. Joseph G. Altonji & Rosa L. Matzkin, 2001. "Panel Data Estimators for Nonseparable Models with Endogenous Regressors," NBER Technical Working Papers 0267, National Bureau of Economic Research, Inc.
    14. Inanoglu, Hulusi & Jacobs, Michael, Jr. & Liu, Junrong & Sickles, Robin, 2015. "Analyzing Bank Efficiency: Are "Too-Big-to-Fail" Banks Efficient?," Working Papers 15-016, Rice University, Department of Economics.
    15. Joel L. Horowitz, 2012. "Nonparametric additive models," CeMMAP working papers CWP20/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Robert Breunig & Alison Stegman, 2005. "Testing For Regime Switching In Singaporean Business Cycles," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 50(01), pages 25-34.
    17. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December.
    18. George Kapetanios, 2002. "Measuring Conditional Persistence in Time Series," Working Papers 474, Queen Mary University of London, School of Economics and Finance.
    19. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.
    20. Martins-Filho, Carlos & Yao, Feng & Torero, Maximo, 2018. "Nonparametric Estimation Of Conditional Value-At-Risk And Expected Shortfall Based On Extreme Value Theory," Econometric Theory, Cambridge University Press, vol. 34(1), pages 23-67, February.

    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:tsj:stataj:v:3:y:2003:i:2:p:148-156. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum) or (Lisa Gilmore). General contact details of provider: http://www.stata-journal.com/ .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.