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Predictive Accuracy Gain from Disaggregate Sampling in ARIMA Models

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  • Nijman, Theo E
  • Palm, Franz 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.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 8 (1990)
Issue (Month): 4 (October)
Pages: 405-15

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Handle: RePEc:bes:jnlbes:v:8:y:1990:i:4:p:405-15

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Cited by:
  1. 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.
  2. 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.
  3. repec:wyi:journl:002175 is not listed on IDEAS
  4. 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.
  5. Eric Ghysels & Joanna Jasiak, 1997. "GARCH for Irregularly Spaced Data: The ACD-GARCH Model," CIRANO Working Papers 97s-06, CIRANO.
  6. Andrea Silvestrini & Matteo Salto & Laurent Moulin & David Veredas, 2008. "Monitoring and forecasting annual public deficit every month: the case of France," Empirical Economics, Springer, vol. 34(3), pages 493-524, June.
  7. Drost, F.C. & Nijman, T.E., 1990. "Temporal aggregation of GARCH processes," Discussion Paper 1990-66, Tilburg University, Center for Economic Research.
  8. 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.
  9. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
  10. 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).
  11. 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.
  12. 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.

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