IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v28y1996i2p99-106.html

An algorithm for estimating parameters of state-space models

Author

Listed:
  • Wu, Lilian Shiao-Yen
  • Pai, Jeffrey S.
  • Hosking, J.R.M.

Abstract

We describe an algorithm for estimating the parameters of time-series models expressed in state-space form. The algorithm is based on the EM algorithm, and generalizes an algorithm given by Shumway and Stoffer (1982)

Suggested Citation

  • Wu, Lilian Shiao-Yen & Pai, Jeffrey S. & Hosking, J.R.M., 1996. "An algorithm for estimating parameters of state-space models," Statistics & Probability Letters, Elsevier, vol. 28(2), pages 99-106, June.
    • Handle: RePEc:eee:stapro:v:28:y:1996:i:2:p:99-106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0167-7152(95)00098-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
    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. Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, Department of Economics and Business Economics, Aarhus University.
    2. Alonso Fernández, Andrés Modesto & García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2008. "Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting," DES - Working Papers. Statistics and Econometrics. WS ws081406, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Venter, J.H. & de Jongh, P.J., 2014. "Extended stochastic volatility models incorporating realised measures," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 687-707.
    4. Luke Hartigan & Michelle Wright, 2021. "Financial Conditions and Downside Risk to Economic Activity in Australia," RBA Research Discussion Papers rdp2021-03, Reserve Bank of Australia.
    5. Schulz, Rainer & Werwatz, Axel, 2001. "A state space model for Berlin house prices," SFB 373 Discussion Papers 2001,58, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    6. Rainer Schulz & Hizir Sofyan & Axel Werwatz & Rodrigo Witzel, 2003. "Online Prediction of Berlin Single-Family House Prices," Computational Statistics, Springer, vol. 18(3), pages 449-462, September.
    7. Giuseppe Storti & Cosimo Vitale, 2003. "BL-GARCH models and asymmetries in volatility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(1), pages 19-39, February.
    8. Alessandra Amendola & Giuseppe Storti, 2002. "A non-linear time series approach to modelling asymmetry in stock market indexes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 201-216, June.
    9. Romain Houssa & Lasse Bork & Hans Dewachter, 2008. "Identification of Macroeconomic Factors in Large Panels," Working Papers 1010, University of Namur, Department of Economics.

    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. Mazzocchi, Mario, 2006. "Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2191-2205, May.
    2. Zirogiannis, Nikolaos & Tripodis, Yorghos, "undated". "A Generalized Dynamic Factor Model for Panel Data: Estimation with a Two-Cycle Conditional Expectation-Maximization Algorithm," Working Paper Series 142752, University of Massachusetts, Amherst, Department of Resource Economics.
    3. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    4. Joseph Ndong & Ted Soubdhan, 2022. "Extracting Statistical Properties of Solar and Photovoltaic Power Production for the Scope of Building a Sophisticated Forecasting Framework," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    5. David de Antonio Liedo, 2014. "Nowcasting Belgium," Working Paper Research 256, National Bank of Belgium.
    6. Benjamin Favetto & Adeline Samson, 2010. "Parameter Estimation for a Bidimensional Partially Observed Ornstein–Uhlenbeck Process with Biological Application," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 200-220, June.
    7. Tommaso Proietti, 2008. "Estimation of Common Factors Under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and Its Main Components," Springer Books, in: Paula Brito (ed.), Compstat 2008, pages 547-558, Springer.
    8. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    9. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    10. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    11. Sayat R. Baronyan & İ. İlkay Boduroğlu & Emrah Şener, 2010. "Investigation Of Stochastic Pairs Trading Strategies Under Different Volatility Regimes," Manchester School, University of Manchester, vol. 78(s1), pages 114-134, September.
    12. Grassi, Stefano & Proietti, Tommaso & Frale, Cecilia & Marcellino, Massimiliano & Mazzi, Gianluigi, 2015. "EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries," International Journal of Forecasting, Elsevier, vol. 31(3), pages 712-738.
    13. Necmettin Alpay Koçak, 2020. "The Role of Ecb Speeches in Nowcasting German Gdp," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2020(2), pages 05-20.
    14. T.P.Koirala, Ph.D., 2013. "Time-Varying Parameters of Inflation Model in Nepal: State Space Modeling," NRB Economic Review, Nepal Rastra Bank, Economic Research Department, vol. 25(2), pages 66-77, October.
    15. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    16. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    17. Seiler, Christian & Heumann, Christian, 2013. "Microdata imputations and macrodata implications: Evidence from the Ifo Business Survey," Economic Modelling, Elsevier, vol. 35(C), pages 722-733.
    18. Peña, Daniel & Poncela, Pilar, 1996. "Pooling information and forecasting with dynamic factor analysis," DES - Working Papers. Statistics and Econometrics. WS 10709, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Matteo Barigozzi & Diego Fresoli & Esther Ruiz, 2026. "Mean Square Errors of factors extracted using principal components, linear projections, and Kalman filter," Papers 2601.04087, arXiv.org.
    20. Antonello D’Agostino & Michele Modugno & Chiara Osbat, 2017. "A Global Trade Model for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 1-34, December.

    More about this item

    Keywords

    ;

    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:stapro:v:28:y:1996:i:2:p:99-106. 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.