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Forecasting with VARMA Models

In: Handbook of Economic Forecasting

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Mathew Ekundayo Rotimi & Mishelle Doorasamy & Udi Joshua & Grace Gift Rotimi & Confort Omolayo Rotimi & Gabriel Samuel & Gbenga Adeyemi & Ayodele Solomon Alemayo & Alfred Kimea, 2022. "ARDL Analysis of Remittance and Per Capita Growth Nexus in Oil Dependent Economy: The Nigeria’s Experience," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 15(3), pages 38-51, December.
    11. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Discussion Papers 02/2018, Deutsche Bundesbank.
    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. 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.
    14. Emre Kahraman & Gazanfer Unal, 2016. "Multiple Wavelet Coherency Analysis and Forecasting of Metal Prices," Papers 1602.01960, arXiv.org.
    15. 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.
    16. 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.

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    JEL classification:

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

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