IDEAS home Printed from https://ideas.repec.org/a/jof/jforec/v26y2007i1p53-76.html

Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approach

Author

Listed:
  • Fabio H. Nieto

    (Department of Statistics, National University of Colombia, Bogotá, COLOMBIA)

Abstract

A methodology for estimating high-frequency values of an unobserved multivariate time series from low-frequency values of and related information to it is presented in this paper. This is an optimal solution, in the multivariate setting, to the problem of ex post prediction, disaggregation, benchmarking or signal extraction of an unobservable stochastic process. Also, the problem of extrapolation or ex ante prediction is optimally solved and, in this context, statistical tests are developed for checking online the ocurrence of extreme values of the unobserved time series and consistency of future benchmarks with the present and past observed information. The procedure is based on structural or unobserved component models, whose assumptions and specification are validated with the data alone. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Fabio H. Nieto, 2007. "Ex post and ex ante prediction of unobserved multivariate time series: a structural-model based approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 53-76.
    • Handle: RePEc:jof:jforec:v:26:y:2007:i:1:p:53-76
    DOI: 10.1002/for.1017
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/for.1017
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.1017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Estela Bee Dagum & Pierre A. Cholette & Zhao‐Guo Chen, 1998. "A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series," International Statistical Review, International Statistical Institute, vol. 66(3), pages 245-269, December.
    2. Nieto, Fabio H. & Guerrero, Victor M., 1995. "Kalman filter for singular and conditional state-space models when the system state and the observational error are correlated," Statistics & Probability Letters, Elsevier, vol. 22(4), pages 303-310, March.
    3. Víctor Guerrero & Fabio Nieto, 1999. "Temporal and contemporaneous disaggregation of multiple economic time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 459-489, December.
    4. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    5. J. Durbin & B. Quenneville, 1997. "Benchmarking by State Space Models," International Statistical Review, International Statistical Institute, vol. 65(1), pages 23-48, April.
    6. F. Javier Fernandez Macho & Andrew C. Harvey & James H. Stock, 1987. "Forecasting and Interpolation Using Vector Autoregressions with Common Trends," Annals of Economics and Statistics, GENES, issue 6-7, pages 279-287.
    7. repec:adr:anecst:y:1987:i:6-7:p:12 is not listed on IDEAS
    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. Ivonne Caridad Perez Correa & Juan Miguel Martinez Buendia, 2013. "Desagregación multivariada del PIB sectorial del departamento de Bolívar," Revista Economía y Región, Universidad Tecnológica de Bolívar, vol. 7(1), pages 139-167.
    2. Kosei Fukuda, 2009. "Related-variables selection in temporal disaggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 343-357.

    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. Ivonne Caridad Perez Correa & Juan Miguel Martinez Buendia, 2013. "Desagregación multivariada del PIB sectorial del departamento de Bolívar," Revista Economía y Región, Universidad Tecnológica de Bolívar, vol. 7(1), pages 139-167.
    2. Christian Caamaño-Carrillo & Sergio Contreras-Espinoza & Orietta Nicolis, 2023. "Reconstructing the Quarterly Series of the Chilean Gross Domestic Product Using a State Space Approach," Mathematics, MDPI, vol. 11(8), pages 1-14, April.
    3. Peter Fuleky & Carl Bonham, 2010. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 2010-17R1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2013.
    4. Peter Fuleky & Carl, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 2013-5, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    5. Hathroubi Salem, 2015. "A Methodological Note on the Construction of High Frequency Macroeconomic Series: Evidence from Tunisia," Asian Journal of Economics and Empirical Research, Asian Online Journal Publishing Group, vol. 2(1), pages 47-51.
    6. José Casals & Miguel Jerez & Sonia Sotoca, 2009. "Modelling and forecasting time series sampled at different frequencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 316-342.
    7. Baoline Chen, 2007. "An Empirical Comparison of Methods for Temporal Distribution and Interpolation at the National Accounts," BEA Papers 0077, Bureau of Economic Analysis.
    8. Ricci L. Reber & Sarah J. Pack, 2014. "Methods of Temporal Disaggregation for Estimating Output of the Insurance Industry," BEA Working Papers 0115, Bureau of Economic Analysis.
    9. Enrique M. Quilis, 2018. "Temporal disaggregation of economic time series: The view from the trenches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 447-470, November.
    10. Justin Doran & Bernard Fingleton, 2014. "Economic shocks and growth: Spatio-temporal perspectives on Europe's economies in a time of crisis," Papers in Regional Science, Wiley Blackwell, vol. 93, pages 137-165, November.
    11. Caruso, Alberto & Reichlin, Lucrezia & Ricco, Giovanni, 2019. "Financial and fiscal interaction in the Euro Area crisis: This time was different," European Economic Review, Elsevier, vol. 119(C), pages 333-355.
    12. Hartwell, Christopher A., 2014. "The impact of institutional volatility on financial volatility in transition economies : a GARCH family approach," BOFIT Discussion Papers 6/2014, Bank of Finland, Institute for Economies in Transition.
    13. Jérôme Héricourt & Iuliana Matei, 2007. "Transmission de la politique monétaire dans les pays d'E urope centrale et orientale : que savons-nous vraiment ?," Economie & Prévision, La Documentation Française, vol. 0(4), pages 221-238.
    14. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    15. Benati, Luca, 2007. "Drift and breaks in labor productivity," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2847-2877, August.
    16. Sun, Hang & Bos, Jaap W.B. & Li, Zhuo, 2017. "In the Nick of Time: A Heteroskedastic SVAR Model and Its Application to the Crude Oil Futures Market," Research Memorandum 019, Maastricht University, Graduate School of Business and Economics (GSBE).
    17. repec:ptu:bdpart:b200407 is not listed on IDEAS
    18. Campbell Leith & Jim Malley, 2007. "A Sectoral Analysis of Price-Setting Behavior in U.S. Manufacturing Industries," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 335-342, May.
    19. Christian Schoder, 2014. "The fundamentals of sovereign debt sustainability: evidence from 15 OECD countries," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(2), pages 247-271, May.
    20. Fabio Pammolli & Francesco Porcelli & Francesco Vidoli & Monica Auteri & Guido Borà, 2017. "La spesa sanitaria delle Regioni in Italia - Saniregio2017," Working Papers CERM 01-2017, Competitività, Regole, Mercati (CERM).
    21. Massimo Gerli & Giovanni Marini, 2006. "Spatial and Temporal Time Series Conversion: A Consistent Estimator of the Error Variance-Covariance Matrix," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 373-405.

    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:jof:jforec:v:26:y:2007:i:1:p:53-76. 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: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.