IDEAS home Printed from https://ideas.repec.org/h/eee/ecofch/1-06.html
   My bibliography  Save this book chapter

Forecasting with VARMA Models

In: Handbook of Economic Forecasting

Author

Listed:
  • Lutkepohl, Helmut

Abstract

Vector autoregressive moving-average (VARMA) processes are suitable models for producing linear forecasts of sets of time series variables. They provide parsimonious representations of linear data generation processes. The setup for these processes in the presence of stationary and cointegrated variables is considered. Moreover, unique or identified parameterizations based on the echelon form are presented. Model specification, estimation, model checking and forecasting are discussed. Special attention is paid to forecasting issues related to contemporaneously and temporally aggregated VARMA processes. Predictors for aggregated variables based alternatively on past information in the aggregated variables or on disaggregated information are compared.

Suggested Citation

  • Lutkepohl, Helmut, 2006. "Forecasting with VARMA Models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 6, pages 287-325, Elsevier.
  • Handle: RePEc:eee:ecofch:1-06
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/B7P5J-4JSMTWJ-9/2/bacb4cdea598cb5bd0f1681b0004017d
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Other versions of this item:

    Citations

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


    Cited by:

    1. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    3. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    4. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
    5. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    6. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
    7. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Discussion Papers 02/2018, Deutsche Bundesbank.
    8. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    10. Albis, Manuel Leonard F. & Mapa, Dennis S., 2014. "Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models," MPRA Paper 55902, University Library of Munich, Germany.
    11. Helmut Lütkepohl, 2013. "Vector autoregressive models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 6, pages 139-164, Edward Elgar Publishing.
    12. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    13. repec:zbw:cfswop:wp200508 is not listed on IDEAS
    14. Oscar Hernán Cerquera Losada & Juan Pablo Murcia Arias & jonas.conde@contraloria.gov.co, 2018. "Relationship between the Consumer Price Index and the Producer Price Index for Six South American Countries," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, vol. 37(66), pages 39-74, June.
    15. Emre Kahraman & Gazanfer Unal, 2016. "Multiple Wavelet Coherency Analysis and Forecasting of Metal Prices," Papers 1602.01960, arXiv.org.
    16. William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

    More about this item

    JEL classification:

    • B0 - Schools of Economic Thought and Methodology - - General

    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:ecofch:1-06. 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: . General contact details of provider: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description .

    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.

    We have no bibliographic references for this item. You can help adding them by using 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/bookseriesdescription.cws_home/BS_HE/description .

    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.