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Temporal aggregation of univariate and multivariate time series models: A survey

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  • SILVESTRINI, Andrea
  • VEREDAS, David

Abstract

We present a unified and up-to-date overview of temporal aggregation techniques for univariate and multivariate time series models explaining in detail how these techniques are employed. Some empirical applications illustrate the main issues.

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File URL: http://dx.doi.org/10.1111/j.1467-6419.2007.00538.x
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Bibliographic Info

Paper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers RP with number -2013.

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Handle: RePEc:cor:louvrp:-2013

Note: In : Journal of Economic Surveys, 22(3), 458-497, 2008
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Cited by:
  1. SBRANA, Giacomo & SILVESTRINI, Andrea, 2010. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," CORE Discussion Papers 2010039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
  3. Hui Jun ZHANG & Jean-Marie DUFOUR & John W. GALBRAITH, 2013. "Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons," Cahiers de recherche 14-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  4. Ramirez, Octavio A., 2012. "Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 123470, Agricultural and Applied Economics Association.
  5. Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Statistical Methods and Applications, Springer, vol. 21(1), pages 93-107, March.
  6. Sacht, Stephen, 2014. "Analysis of various shocks within the high-frequency versions of the baseline New-Keynesian model," Economics Working Papers 2014-02, Christian-Albrechts-University of Kiel, Department of Economics.
  7. Ramirez, Octavio A., 2011. "Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts," Faculty Series 113520, University of Georgia, Department of Agricultural and Applied Economics.
  8. 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).
  9. Yamin Ahmad & Ivan Paya, 2014. "Temporal Aggregation of Random Walk Processes and Implications for Asset Prices," Working Papers 14-01, UW-Whitewater, Department of Economics.
  10. Hassler, Uwe, 2011. "Estimation of fractional integration under temporal aggregation," Journal of Econometrics, Elsevier, vol. 162(2), pages 240-247, June.
  11. Steven Clark & T. Coggin, 2011. "Are U.S. stock prices mean reverting? Some new tests using fractional integration models with overlapping data and structural breaks," Empirical Economics, Springer, vol. 40(2), pages 373-391, April.
  12. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.

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