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Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts

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  • Ramirez, Octavio A.
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    Abstract

    Simulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data. This allows for novel comparisons including long-order ARMA models, such as those expected with weekly data, under realistic conditions where the parameter values have to be estimated. The ambivalence of past empirical evidence on the benefits of disaggregation is addressed by analyzing four different economic time series for which relatively large sample sizes are available. Because of this, a sufficient number of predictions can be considered to obtain conclusive results from out-of-sample forecasting contests. The validity of the conventional method for inferring the order of the aggregated models is revised.

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    File URL: http://purl.umn.edu/113520
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    Bibliographic Info

    Paper provided by University of Georgia, Department of Agricultural and Applied Economics in its series Faculty Series with number 113520.

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    Date of creation: Aug 2011
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    Handle: RePEc:ags:ugeofs:113520

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    Keywords: Data Aggregation; Efficient Forecasting; Research Methods/ Statistical Methods;

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    1. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
    2. Dimitris Georgoutsos & George Kouretas & Dikaios Tserkezos, . "Temporal Aggregation In Structural Var Models," Working Papers 9505, University of Crete, Department of Economics.
    3. Den Butter, F. A. G., 1976. "The use of monthly and quarterly data in an ARMA model," Journal of Econometrics, Elsevier, vol. 4(4), pages 311-324, November.
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    5. SILVESTRINI, Andrea & VEREDAS, David, . "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).
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    7. Nijman, Theo E & Palm, Franz C, 1990. "Predictive Accuracy Gain from Disaggregate Sampling in ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(4), pages 405-15, October.
    8. Peter C.B. Phillips & Sam Ouliaris & Joon Y. Park, 1988. "Testing for a Unit Root in the Presence of a Maintained Trend," Cowles Foundation Discussion Papers 880, Cowles Foundation for Research in Economics, Yale University.
    9. SILVESTRINI, Andrea & SALTo, Matteo & MOULIN, Laurent & VEREDAS, David, . "Monitoring and forecasting annual public deficit every month: the case of France," CORE Discussion Papers RP -2019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Yue Fang & Sergio G. Koreisha, 2004. "Updating ARMA predictions for temporal aggregates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(4), pages 275-296.
    11. Tiao, G. C. & Guttman, Irwin, 1980. "Forecasting contemporal aggregates of multiple time series," Journal of Econometrics, Elsevier, vol. 12(2), pages 219-230, February.
    12. Rossana, Robert J & Seater, John J, 1995. "Temporal Aggregation and Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 441-51, October.
    13. Wei, William W. S., 1978. "The effect of temporal aggregation on parameter estimation in distributed lag model," Journal of Econometrics, Elsevier, vol. 8(2), pages 237-246, October.
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