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A unifying framework for analysing common cyclical features in cointegrated time series

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  • Cubadda, Gianluca

Abstract

This paper provides a unifying framework in which the coexistence of different form of common cyclical features can be tested and imposed to a cointegrated VAR model. This goal is reached by introducing a new notion of common cyclical features, namely the weak form of polynomial serial correlation common features, which encompasses most of the previous ones. Statistical inference is obtained by means of reduced-rank regression, and alternative forms of common cyclical features are detected by means of tests for over-identifying restrictions on the parameters of the new model. Some iterative estimation procedures are then proposed for simultaneously modelling different forms of common features. Concepts and methods are illustrated by an empirical investigation of the US business cycle indicators.
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  • 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.
    • Handle: RePEc:eee:csdana:v:52:y:2007:i:2:p:896-906
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    Cited by:

    1. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    2. 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.
    3. Lindenberg, Nannette & Westermann, Frank, 2012. "Common trends and common cycles among interest rates of the G7-countries," Journal of Macroeconomics, Elsevier, vol. 34(4), pages 1125-1140.
    4. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
    5. Marco Centoni & Gianluca Cubadda, 2015. "Common Feature Analysis of Economic Time Series: An Overview and Recent Developments," CEIS Research Paper 355, Tor Vergata University, CEIS, revised 05 Oct 2015.
    6. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High‐Dimensional Vector Autoregressive Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1123-1152, October.
    7. Gianluca Cubadda & Alain Hecq & Sean Telg, 2019. "Detecting Co‐Movements in Non‐Causal Time Series," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(3), pages 697-715, June.
    8. Nannette Lindenberg & Frank Westermann, 2009. "How Strong is the Case for Dollarization in Costa Rica? A Note on the Business Cycle Comovements with the United States," IEER Working Papers 79, Institute of Empirical Economic Research, Osnabrueck University.
    9. Cubadda, G. & Hecq, A.W. & Palm, F.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).
    10. Justyna Wróblewska, 2012. "Bayesian Analysis of Weak Form Polynomial Reduced Rank Structures in VEC Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(4), pages 253-267, December.
    11. Bernardini, Emmanuela & Cubadda, Gianluca, 2015. "Macroeconomic forecasting and structural analysis through regularized reduced-rank regression," International Journal of Forecasting, Elsevier, vol. 31(3), pages 682-691.
    12. Espasa, Antoni & Mayo-Burgos, Iván, 2013. "Forecasting aggregates and disaggregates with common features," International Journal of Forecasting, Elsevier, vol. 29(4), pages 718-732.
    13. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2013. "A general to specific approach for constructing composite business cycle indicators," Economic Modelling, Elsevier, vol. 33(C), pages 367-374.
    14. Justyna Wróblewska, 2011. "Bayesian Analysis of Weak Form Reduced Rank Structure in VEC Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 3(3), pages 169-186, September.
    15. Nannette Lindenberg & Frank Westermann, 2012. "How strong is the case for dollarization in Central America? An empirical analysis of business cycles, credit market imperfections and the exchange rate," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 17(2), pages 147-166, April.
    16. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
    17. Carlomagno Real, Guillermo & Espasa, Antoni, 2017. "Discovering pervasive and non-pervasive common cycles," DES - Working Papers. Statistics and Econometrics. WS 25392, Universidad Carlos III de Madrid. Departamento de Estadística.
    18. Gianluca Cubadda & Alain Hecq, 2020. "Dimension Reduction for High Dimensional Vector Autoregressive Models," Papers 2009.03361, arXiv.org, revised Feb 2022.

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    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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