Testing for common cycles in non-stationary VARs with varied frecquency data
This paper proposes a new way for detecting the presence of common cyclical featureswhen several time series are observed/sampled at different frequencies, hence generalizingthe common-frequency approach introduced by Engle and Kozicki 1993 and Vahid andEngle 1993. We start with the mixed-frequency VAR representation investigated in Ghysels2012 for stationary time series. For non-stationary time series in levels, we showthat one has to account for the presence of two sets of long-run relationships. The First setis implied by identities stemming from the fact that the differences of the high-frequencyI1 regressors are stationary. The second set comes from possible additional long-run relationshipsbetween one of the high-frequency series and the low-frequency variables. Ourtransformed VECM representations extend the results of Ghysels 2012 and are very importantfor determining the correct set of variables to be used in a subsequent commoncycle investigation. This has some empirical implications both for the behavior of the teststatistics as well as for forecasting. Empirical analyses with the quarterly real GNP andmonthly industrial production indices for, respectively, the U.S. and Germany illustrate ournew approach. This is also investigated in a Monte Carlo study, where we compare our proposedmixed-frequency models with models stemming from classical temporal aggregationmethods.
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- Andrea Silvestrini & David Veredas, 2008.
"Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey,"
Journal of Economic Surveys,
Wiley Blackwell, vol. 22(3), pages 458-497, 07.
- SILVESTRINI, Andrea & VEREDAS, David, "undated". "Temporal aggregation of univariate and multivariate time series models: A survey," CORE Discussion Papers RP 2013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Andrea Silvestrini & David Veredas, 2008. "Temporal aggregation of univariate and multivariate time series models: A survey," Temi di discussione (Economic working papers) 685, Bank of Italy, Economic Research and International Relations Area.
- Andrea Silvestrini & David Veredas, 2008. "Temporal aggregation of univariate and multivariate time series models: a survey," ULB Institutional Repository 2013/136205, ULB -- Universite Libre de Bruxelles.
- Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, 04.
- Götz Thomas & Hecq Alain & Urbain Jean-Pierre, 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Cubadda, Gianluca & Hecq, Alain, 2001. "On non-contemporaneous short-run co-movements," Economics Letters, Elsevier, vol. 73(3), pages 389-397, December.
- Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
- ZELLNER, Arnold & PALM, Franz, "undated". "Time series analysis and simultaneous equation econometric models," CORE Discussion Papers RP 173, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- 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).
- J. Isaac Miller, 2011. "Cointegrating MiDaS Regressions and a MiDaS Test," Working Papers 1104, Department of Economics, University of Missouri.
- Götz Thomas B. & Hecq Alain & Urbain Jean-Pierre, 2012. "Real-Time Forecast Density Combinations (Forecasting US GDP Growth Using Mixed-Frequency Data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- Götz T.B. & Hecq A.W. & Urbain J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
- Warne, A., 1993. "A Common Trends Model: Identification, Estimation and Inference," Papers 555, Stockholm - International Economic Studies.
- Byeongchan Seong & Sung K. Ahn & Peter A. Zadrozny, 2013. "Estimation of vector error correction models with mixed-frequency data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(2), pages 194-205, 03.
- Hecq, Alain, 1998. "Does seasonal adjustment induce common cycles?," Economics Letters, Elsevier, vol. 59(3), pages 289-297, June.
- Alain Hecq & Franz Palm & Jean-Pierre Urbain, 2001. "Testing for Common Cyclical Features in Var Models with Cointegration," CESifo Working Paper Series 451, CESifo Group Munich.
- Zadrozny, Peter, 1988. "Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies," Econometric Theory, Cambridge University Press, vol. 4(01), pages 108-124, April. Full references (including those not matched with items on IDEAS)