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Predictive accuracy gain from disaggregate sampling in ARIMA models

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  • Nijman, T.E.

    (Tilburg University)

  • Palm, F.C.

Abstract

We compare the forecast accuracy of autoregressive integrated moving average (ARIMA) models based on data observed with high and low frequency, respectively. We discuss how, for instance, a quarterly model can be used or predict one quarter ahead even if only annual data are available, and we compare the variance of the prediction error in this case with the variance if quarterly observations were indeed available. Results on the expected information gain are presented for a number of ARIMA models including models that describe the seasonally adjusted gross national product (GNP) series in the Netherlands. Disaggregation from annual to quarterly GNP data has reduced the variance of short-run forecast errors considerably, but furter disaggregation from quarterly to monthly data is found to hardly improve the accuracy of monthly forecasts.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Paper provided by Tilburg University in its series Open Access publications from Tilburg University with number urn:nbn:nl:ui:12-153276.

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Date of creation: 1990
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Publication status: Published in Journal of Business and Economic Statistics (1990) v.8, p.405-416
Handle: RePEc:ner:tilbur:urn:nbn:nl:ui:12-153276

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Web page: http://www.tilburguniversity.edu/

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Cited by:
  1. Drost, F.C. & Nijman, T.E., 1992. "Temporal Aggregation of Garch Processes," Papers 9240, Tilburg - Center for Economic Research.
  2. Tokat, Yesim & Rachev, Svetlozar T. & Schwartz, Eduardo S., 2003. "The stable non-Gaussian asset allocation: a comparison with the classical Gaussian approach," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 937-969, April.
  3. Carlos, Thiago C. & Marçal, Emerson Fernandes, 2013. "Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon," Textos para discussão 346, Escola de Economia de São Paulo, Getulio Vargas Foundation (Brazil).
  4. repec:wyi:journl:002175 is not listed on IDEAS
  5. 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.
  6. Pierse, R. G. & Snell, A. J., 1995. "Temporal aggregation and the power of tests for a unit root," Journal of Econometrics, Elsevier, vol. 65(2), pages 333-345, February.
  7. 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).
  8. Eric Ghysels & Joanna Jasiak, 1997. "GARCH for Irregularly Spaced Data: The ACD-GARCH Model," CIRANO Working Papers 97s-06, CIRANO.
  9. Feijoo, Santiago Rodriguez & Caro, Alejandro Rodriguez & Quintana, Delia Davila, 2003. "Methods for quarterly disaggregation without indicators; a comparative study using simulation," Computational Statistics & Data Analysis, Elsevier, vol. 43(1), pages 63-78, May.
  10. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," CORE Discussion Papers 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  11. 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.
  12. Alejandro Rodríguez Caro & Santiago Rodríguez Feijoo & Delia Dávila Quintana, 2003. "La trimestralización de variables flujo. Un estudio de simulación de los métodos de desagregación temporal con indicador," Documentos de trabajo conjunto ULL-ULPGC 2003-01, Facultad de Ciencias Económicas de la ULPGC.

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