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Studying Co-movements in Large Multivariate Models Without Multivariate Modelling

Author

Listed:
  • Cubadda Gianluca
  • Hecq Alain
  • Palm Franz C.

    (METEOR)

Abstract

We propose an approach for checking the data admissibility of non-stationary multivariate time series models (VAR or VARMA) through that of their implied individual ARIMA specifications. In particular we show that the presence of different kinds of common cyclical features restrictions, leading to reduced rank in the short-run dynamics, explains why we identify parsimonious univariate ARIMA models in applied research, a paradox that the profession had difficulties to explain. We develop a new strategy for studying interactions between variables prior to possibly modelling them in a multivariate setting. Indeed, we provide tools to study features of individual time series with the aim to infer features of the complete system, as individual series keep a print of the system as a whole. The similarity of the autoregressive roots will be informative about co-movements existing in a vector autoregressive model as well as convergence between series for different economies. It will allow us to forecast series, to build business cycle indices, to unravel trends from cycles in a way that is consistent with the full multivariate system. Our results justify both the use of an homogeneous panel with hetegoneous cross-correlated vector moving average (VMA) errors and a factor structure, and the cross-sectional aggregation of ARIMA series. The advantages of our approach are many: 1) determining co-movements also when we cannot work with a complete system, 2) enhancing the accuracy of forecasts, 3) the ease of its implementation in complex situations, 4) the potential empirical studies in many fields.

Suggested Citation

  • Cubadda Gianluca & Hecq Alain & Palm Franz C., 2007. "Studying Co-movements in Large Multivariate Models Without Multivariate Modelling," Research Memorandum 032, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  • Handle: RePEc:unm:umamet:2007032
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    File URL: https://cris.maastrichtuniversity.nl/portal/files/940527/content
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    References listed on IDEAS

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    1. Maravall, Agustin & Mathis, Alexandre, 1994. "Encompassing univariate models in multivariate time series : A case study," Journal of Econometrics, Elsevier, vol. 61(2), pages 197-233, April.
    2. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(06), pages 1113-1141, December.
    3. Palm, Franz, 1977. "On univariate time series methods and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 5(3), pages 379-388, May.
    4. Hecq, Alain & Palm, Franz C. & Urbain, Jean-Pierre, 2006. "Common cyclical features analysis in VAR models with cointegration," Journal of Econometrics, Elsevier, vol. 132(1), pages 117-141, May.
    5. Cubadda, Gianluca, 2007. "A unifying framework for analysing common cyclical features in cointegrated time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 896-906, October.
    6. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    7. Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
    8. Christoph Schleicher, 2007. "Codependence in cointegrated autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 137-159.
    9. Palm, Franz & Zellner, Arnold, 1981. "Large sample estimation and testing procedures for dynamic equation systems," Journal of Econometrics, Elsevier, vol. 17(1), pages 131-138, September.
    10. Wallis, Kenneth F, 1977. "Multiple Time Series Analysis and the Final Form of Econometric Models," Econometrica, Econometric Society, vol. 45(6), pages 1481-1497, September.
    11. Zellner,Arnold & Palm,Franz C. (ed.), 2004. "The Structural Econometric Time Series Analysis Approach," Cambridge Books, Cambridge University Press, number 9780521814072, December.
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    Citations

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    Cited by:

    1. Hecq Alain & Palm Franz C. & Laurent Sébastien, 2016. "On the Univariate Representation of BEKK Models with Common Factors," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 91-113, July.
    2. Franchi, Massimo & Paruolo, Paolo, 2011. "A characterization of vector autoregressive processes with common cyclical features," Journal of Econometrics, Elsevier, vol. 163(1), pages 105-117, July.
    3. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2008. "Macro-panels and reality," Economics Letters, Elsevier, vol. 99(3), pages 537-540, June.
    4. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    5. Hecq A.W. & Urbain J.R.Y.J. & Götz T.B., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).

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    Keywords

    macroeconomics ;

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