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Studying Co-Movements in Large Multivariate Data Prior to Multivariate Modelling

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Abstract

For non-stationary vector autoregressive models (VAR hereafter, or VAR with moving average, VARMA hereafter), we show that the presence of common cyclical features or cointegration leads to a reduction of the order of the implied univariate autoregressive-moving average (ARIMA hereafter) models. This finding can explain why we identify parsimonious univariate ARIMA models in applied research although VAR models of typical order and dimension used in macroeconometrics imply nonparsimonious univariate ARIMA representations. Next, we develop a strategy for studying interactions between variables prior to possibly modelling them in a multivariate setting. Indeed, the similarity of the autoregressive roots will be informative about the presence of co-movements in a set of multiple time series. Our results justify both the use of a panel setup with homogeneous autoregression and heterogeneous cross-correlated vector moving average errors and a factor structure, and the use of cross-sectional aggregates of ARIMA series to estimate the homogeneous autoregression.

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Bibliographic Info

Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 125.

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Length: 26 pages
Date of creation: 14 Jul 2008
Date of revision: 14 Jul 2008
Handle: RePEc:rtv:ceisrp:125

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Web: http://www.ceistorvergata.it

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Keywords: Interactions; multiple time series; co-movements; ARIMA; cointegration; common cycles; dynamic panel data.;

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References

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  1. Zellner,Arnold & Palm,Franz C. (ed.), 2004. "The Structural Econometric Time Series Analysis Approach," Cambridge Books, Cambridge University Press, number 9780521814072.
  2. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2007. "Macro-panels and Reality," Research Memorandum 009, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  3. Christoph Schleicher, 2004. "Codependence in Cointegrated Autoregressive Models," Computing in Economics and Finance 2004 286, Society for Computational Economics.
  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. Ralf Brüggemann & Hans-Martin Krolzig & Helmut Lütkepohl, 2003. "Comparison of Model Reduction Methods for VAR Processes," Economics Papers 2003-W13, Economics Group, Nuffield College, University of Oxford.
  6. Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
  7. Jushan Bai & Serena Ng, 2001. "A Panic Attack on Unit Roots and Cointegration," Economics Working Paper Archive 469, The Johns Hopkins University,Department of Economics.
  8. Robert F. Engle & Sharon Kozicki, 1990. "Testing For Common Features," NBER Technical Working Papers 0091, National Bureau of Economic Research, Inc.
  9. Palm, F.C. & Zellner, A., 1978. "Large sample estimation and testing procedures for dynamic equation systems," Serie Research Memoranda 0010, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  10. Marco Centoni & Gianluca Cubadda & Alain Hecq, 2008. "Common Shocks, Common Dynamics, and the International Business Cycle," CEIS Research Paper 106, Tor Vergata University, CEIS, revised 07 Jul 2008.
  11. Palm, Franz, 1977. "On univariate time series methods and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 5(3), pages 379-388, May.
  12. Vahid, Farshid & Engle, Robert F., 1997. "Codependent cycles," Journal of Econometrics, Elsevier, vol. 80(2), pages 199-221, October.
  13. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  14. 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.
  15. 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.
  16. 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.
  17. Cubadda, Gianluca & Hecq, Alain, 2001. "On non-contemporaneous short-run co-movements," Economics Letters, Elsevier, vol. 73(3), pages 389-397, December.
  18. Robert F. Engle & João Victor Issler, 1993. "Common trends and common cycles in Latin America," Revista Brasileira de Economia, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 47(2), pages 149-176, April.
  19. Vahid, F & Engle, Robert F, 1993. "Common Trends and Common Cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 341-60, Oct.-Dec..
  20. repec:cup:cbooks:9780521586412 is not listed on IDEAS
  21. Alain Hecq & Franz Palm & Jean-Pierre Urbain, 2002. "Separation, Weak Exogeneity, And P-T Decomposition In Cointegrated Var Systems With Common Features," Econometric Reviews, Taylor & Francis Journals, vol. 21(3), pages 273-307.
  22. Wallis, Kenneth F, 1977. "Multiple Time Series Analysis and the Final Form of Econometric Models," Econometrica, Econometric Society, vol. 45(6), pages 1481-97, September.
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Citations

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Cited by:
  1. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2010. "The conditional autoregressive wishart model for multivariate stock market volatility," Economics Working Papers 2010,07, Christian-Albrechts-University of Kiel, Department of Economics.
  2. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
  3. Smeekes Stephan & Urbain Jean-Pierre, 2011. "On the Applicability of the Sieve Bootstrap in Time series Panels," Research Memorandum 055, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  4. Marco Centoni & Gianluca Cubadda, 2011. "Modelling comovements of economic time series: a selective survey," Statistica, Department of Statistics, University of Bologna, vol. 71(2), pages 267-294.
  5. Gianluca Cubadda & Alain Hecq, 2011. "Testing for common autocorrelation in data‐rich environments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 325-335, April.

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