IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v50y2000i1p63-73.html
   My bibliography  Save this article

Finite sample performance of density estimators from unequally spaced data

Author

Listed:
  • Vilar, José A.
  • Vilar, Juan M.

Abstract

For broad classes of deterministic and random sampling schemes {[tau]k}, exact mean integrated squared error (MISE) expressions for the kernel estimator of the marginal density of a first-order continuous-time autoregressive process are derived. The obtained expressions show that the effect on MISE due to both the sampling scheme and the sampling rate is significant for finite samples. The results are also extended to a case where the irregular observations are generated from a mixture of first-order continuous-time processes.

Suggested Citation

  • Vilar, José A. & Vilar, Juan M., 2000. "Finite sample performance of density estimators from unequally spaced data," Statistics & Probability Letters, Elsevier, vol. 50(1), pages 63-73, October.
    • Handle: RePEc:eee:stapro:v:50:y:2000:i:1:p:63-73
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(00)00083-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J. Vilar, 1995. "Kernel estimation of the regression function with random sampling times," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 4(1), pages 137-178, June.
    2. Robinson, P. M., 1977. "Estimation of a time series model from unequally spaced data," Stochastic Processes and their Applications, Elsevier, vol. 6(1), pages 9-24, November.
    3. Mielniczuk, Jan, 1997. "On the asymptotic mean integrated squared error of a kernel density estimator for dependent data," Statistics & Probability Letters, Elsevier, vol. 34(1), pages 53-58, May.
    4. Wand, M. P., 1992. "Finite sample performance of density estimators under moving average dependence," Statistics & Probability Letters, Elsevier, vol. 13(2), pages 109-115, January.
    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. Philip Hans Franses, 2021. "Estimating persistence for irregularly spaced historical data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2177-2187, December.
    2. Pritsker, Matt, 1998. "Nonparametric Density Estimation and Tests of Continuous Time Interest Rate Models," The Review of Financial Studies, Society for Financial Studies, vol. 11(3), pages 449-487.
    3. Yacine Ait--Sahalia & Per A. Mykland, 2003. "The Effects of Random and Discrete Sampling when Estimating Continuous--Time Diffusions," Econometrica, Econometric Society, vol. 71(2), pages 483-549, March.
    4. Delgado, Miguel A. & Robinson, Peter M., 2015. "Non-nested testing of spatial correlation," Journal of Econometrics, Elsevier, vol. 187(1), pages 385-401.
    5. Wand, M. P., 1998. "Finite sample performance of deconvolving density estimators," Statistics & Probability Letters, Elsevier, vol. 37(2), pages 131-139, February.
    6. Oscar Jorda & Massimiliano Marcellino, "undated". "Stochastic Processes Subject To Time Scale Transformations: An Application To High-Frequency Fx Data," Department of Economics 00-02, California Davis - Department of Economics.
    7. Robinson, Peter, 2008. "Correlation testing in time series, spatial and cross-sectional data," LSE Research Online Documents on Economics 25470, London School of Economics and Political Science, LSE Library.
    8. Robinson, Peter, 2007. "On discrete sampling of time-varying continuous-time systems," LSE Research Online Documents on Economics 6795, London School of Economics and Political Science, LSE Library.
    9. Robinson, Peter, 2019. "Spatial long memory," LSE Research Online Documents on Economics 102182, London School of Economics and Political Science, LSE Library.
    10. Fernandez, J. M. Vilar & Manteiga, W. Gonzalez, 2000. "Resampling for checking linear regression models via non-parametric regression estimation," Computational Statistics & Data Analysis, Elsevier, vol. 35(2), pages 211-231, December.
    11. Massimiliano Marcellino & Oscar Jorda, "undated". "Stochastic Processes Subject to Time-Scale Transformations: An Application to High-Frequency FX Data," Working Papers 164, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    12. P. Thomson, 1992. "Signal estimation using stochastic velocity models and irregular arrays," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 13-25, March.
    13. Peter Robinson, 2007. "Correlation testing in time series, spatial and cross-sectional data," CeMMAP working papers CWP01/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. repec:cep:stiecm:/2013/568 is not listed on IDEAS
    15. Saavedra, Ángeles & Cao, Ricardo, 1999. "Rate of convergence of a convolution-type estimator of the marginal density of a MA(1) process," Stochastic Processes and their Applications, Elsevier, vol. 80(2), pages 129-155, April.
    16. Aleksandr Beknazaryan & Hailin Sang & Peter Adamic, 2023. "On the integrated mean squared error of wavelet density estimation for linear processes," Statistical Inference for Stochastic Processes, Springer, vol. 26(2), pages 235-254, July.
    17. Robinson, P.M., 2011. "Asymptotic theory for nonparametric regression with spatial data," Journal of Econometrics, Elsevier, vol. 165(1), pages 5-19.
    18. Peter Robinson, 2007. "On Discrete Sampling Of Time-Varyingcontinuous-Time Systems," STICERD - Econometrics Paper Series 520, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    19. Matthew Pritsker, 1997. "Nonparametric density estimation and tests of continuous time interest rate models," Finance and Economics Discussion Series 1997-26, Board of Governors of the Federal Reserve System (U.S.).
    20. Peter M Robinson, 2009. "Correlation Testing in Time Series, SpatialandCross-Sectional Data," STICERD - Econometrics Paper Series 530, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    21. Robinson, P.M., 2008. "Correlation testing in time series, spatial and cross-sectional data," Journal of Econometrics, Elsevier, vol. 147(1), pages 5-16, November.

    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:eee:stapro:v:50:y:2000:i:1:p:63-73. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.