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A Multivariate Time Series Analysis of Some Flour Price Data

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  • Howard Grubb

Abstract

In this paper we develop and compare several multivariate models for some multiple time series data. The data are indices of the price of flour at three sites in the USA and have been used for illustration in a recent methodological paper. The models all come from the vector autoregressive moving average class so that comparisons between them can easily be made using criteria such as Akaike's information criterion. It is particularly interesting to compare the models produced by relatively complicated model specification procedures with those developed by using more straightforward techniques to see whether we gain any worthwhile improvements in fit.

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  • Howard Grubb, 1992. "A Multivariate Time Series Analysis of Some Flour Price Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 95-107, March.
    • Handle: RePEc:bla:jorssc:v:41:y:1992:i:1:p:95-107
    DOI: 10.2307/2347620
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    Cited by:

    1. George Athanasopoulos & Farshid Vahid, 2008. "A complete VARMA modelling methodology based on scalar components," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 533-554, May.
    2. Ribeiro Ramos, Francisco Fernando, 2003. "Forecasts of market shares from VAR and BVAR models: a comparison of their accuracy," International Journal of Forecasting, Elsevier, vol. 19(1), pages 95-110.
    3. Kato, Hiroko & Naniwa, Sadao & Ishiguro, Makio, 1996. "A bayesian multivariate nonstationary time series model for estimating mutual relationships among variables," Journal of Econometrics, Elsevier, vol. 75(1), pages 147-161, November.
    4. Xu Xiaojie, 2018. "Using Local Information to Improve Short-Run Corn Price Forecasts," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 16(1), pages 1-15, January.
    5. Lutkepohl, Helmut & Poskitt, D S, 1996. "Specification of Echelon-Form VARMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 69-79, January.
    6. Alfredo García Hiernaux & Miguel Jerez & José Casals, 2005. "Deteccióon de Raíces Unitarias y Cointegración mediante Métodos de Subespacios," Documentos de Trabajo del ICAE 0503, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    7. Francisco F. R. Ramos, 1996. "Forecasting market shares using VAR and BVAR models: A comparison of their forecasting performance," Econometrics 9601003, University Library of Munich, Germany.
    8. Galeano, Pedro & Peña, Daniel, 2004. "Variance changes detection in multivariate time series," DES - Working Papers. Statistics and Econometrics. WS ws041305, Universidad Carlos III de Madrid. Departamento de Estadística.

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