IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2008i1p189-195.html
   My bibliography  Save this article

Spectral estimation of a structural thin-plate smoothing model

Author

Listed:
  • Fernández-Macho, Javier

Abstract

A nonstationary structural spatial model that explicitly sets the data to evolve across a rectangular lattice constrained by second-order smoothing restrictions is presented. The model exemplifies the concept of model-based spatial smoothing and, in particular, it provides a rationale for the popular discrete thin-plate smoothing method. It is further shown how to use a frequency-domain approach to estimate the spatial model via maximum likelihood. In essence, the approach allows both dimensions to be treated separately from each other so that the computational burden for the estimation of two-dimensional models is dramatically reduced both in terms of the computing time and the memory required. Besides, this spectral approach allows straightforward construction of analytic derivatives and an expression for the asymptotic variance of the estimated smoothing parameter is derived with which to construct confidence intervals. Some numerical Monte-Carlo evidence and one example illustrate the results given.

Suggested Citation

  • Fernández-Macho, Javier, 2008. "Spectral estimation of a structural thin-plate smoothing model," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 189-195, September.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:1:p:189-195
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00344-7
    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. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    2. Kim, Hyoung-Moon & Mallick, Bani K. & Holmes, C.C., 2005. "Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 653-668, June.
    3. Nobuhisa Kashiwagi, 1993. "On use of the Kalman filter for spatial smoothing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(1), pages 21-34, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fernández Macho, Francisco Javier, 2011. "Stochastic Surface Models for Commodity Futures: A 2D Kalman Filter Approach," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).

    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. Victor Bystrov, 2018. "Measuring the Natural Rates of Interest in Germany and Italy," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(4), pages 333-353, December.
    2. Yukai Yang & Luc Bauwens, 2018. "State-Space Models on the Stiefel Manifold with a New Approach to Nonlinear Filtering," Econometrics, MDPI, vol. 6(4), pages 1-22, December.
    3. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model," Tinbergen Institute Discussion Papers 08-069/4, Tinbergen Institute.
    4. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    5. François R. Velde, 2009. "Chronicle of a Deflation Unforetold," Journal of Political Economy, University of Chicago Press, vol. 117(4), pages 591-634, August.
    6. Chen, Peimin & Wu, Chunchi, 2014. "Default prediction with dynamic sectoral and macroeconomic frailties," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 211-226.
    7. repec:zbw:bofitp:2019_008 is not listed on IDEAS
    8. Yue Zhao & Difang Wan, 2018. "Institutional high frequency trading and price discovery: Evidence from an emerging commodity futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 243-270, February.
    9. Wen Xu, 2016. "Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters," Econometrics, MDPI, vol. 4(4), pages 1-13, October.
    10. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    11. Eric Heyer & Frédéric Reynès & Henri Sterdyniak, 2004. "Observable and unobservable variables in the theory of the equilibrium rate of unemployment, a comparison between France and the United States," Working Papers hal-01027420, HAL.
    12. Hári, Norbert & De Waegenaere, Anja & Melenberg, Bertrand & Nijman, Theo E., 2008. "Estimating the term structure of mortality," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 492-504, April.
    13. Brave, Scott A. & Gascon, Charles & Kluender, William & Walstrum, Thomas, 2021. "Predicting benchmarked US state employment data in real time," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1261-1275.
    14. Alejandro Rodriguez & Esther Ruiz, 2009. "Bootstrap prediction intervals in state–space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 167-178, March.
    15. Parrini, Alessandro, 2013. "Importance Sampling for Portfolio Credit Risk in Factor Copula Models," MPRA Paper 103745, University Library of Munich, Germany.
    16. Salman Huseynov, 2021. "Long and short memory in dynamic term structure models," CREATES Research Papers 2021-15, Department of Economics and Business Economics, Aarhus University.
    17. Bógalo, Juan & Poncela, Pilar & Senra, Eva, 2017. "Automatic Signal Extraction for Stationary and Non-Stationary Time Series by Circulant SSA," MPRA Paper 76023, University Library of Munich, Germany.
    18. Önundur Páll Ragnarsson & Jón Magnús Hannesson & Loftur Hreinsson, 2019. "Financial cycles as early warning indicators - Lessons from the Nordic region," Economics wp80, Department of Economics, Central bank of Iceland.
    19. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    20. Planas, Christophe & Roeger, Werner & Rossi, Alessandro, 2007. "How much has labour taxation contributed to European structural unemployment?," Journal of Economic Dynamics and Control, Elsevier, vol. 31(4), pages 1359-1375, April.
    21. Tommaso Proietti, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Econometrics 0209002, University Library of Munich, Germany.

    More about this item

    Statistics

    Access and download statistics

    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:csdana:v:53:y:2008:i:1:p:189-195. 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/locate/csda .

    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.