Updating ARMA predictions for temporal aggregates
This article develops and extends previous investigations on the temporal aggregation of ARMA predications. Given a basic ARMA model for disaggregated data, two sets of predictors may be constructed for future temporal aggregates: predictions based on models utilizing aggregated data or on models constructed from disaggregated data for which forecasts are updated as soon as the new information becomes available. We show that considerable gains in efficiency based on mean-square-error-type criteria can be obtained for short-term predications when using models based on updated disaggregated data. However, as the prediction horizon increases, the gain in using updated disaggregated data diminishes substantially. In addition to theoretical results associated with forecast efficiency of ARMA models, we also illustrate our findings with two well-known time series. Copyright © 2004 John Wiley & Sons, Ltd.
Volume (Year): 23 (2004)
Issue (Month): 4 ()
|Contact details of provider:|| Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Brown, Lawrence D., 1993. "Earnings forecasting research: its implications for capital markets research," International Journal of Forecasting, Elsevier, vol. 9(3), pages 295-320, November.
- Zhou, Bin, 1996. "High-Frequency Data and Volatility in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 45-52, January.
- Hasbrouck, Joel, 1993. "Assessing the Quality of a Security Market: A New Approach to Transaction-Cost Measurement," Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 191-212.
- Ali, Ashiq & Zarowin, Paul, 1992. "Permanent versus transitory components of annual earnings and estimation error in earnings response coefficients," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 249-264, August.
- Brown, Lawrence D., 1993. "Reply to commentaries on "Earnings forecasting research: its implications for capital markets research"," International Journal of Forecasting, Elsevier, vol. 9(3), pages 343-344, November.
- Merville, Larry J. & Pieptea, Dan R., 1989. "Stock-price volatility, mean-reverting diffusion, and noise," Journal of Financial Economics, Elsevier, vol. 24(1), pages 193-214, September.
- 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 405-415, October.
- Nijman, T.E. & Palm, F.C., 1987. "Predictive accuracy gain from disaggregate sampling in ARIMA-models," Research Memorandum FEW 273, Tilburg University, School of Economics and Management.
- Nijman, T.E. & Palm, F.C., 1990. "Predictive accuracy gain from disaggregate sampling in ARIMA models," Other publications TiSEM 50a68aea-1b30-497d-b111-6, Tilburg University, School of Economics and Management.
- Sergio G. Koreisha & Yue Fang, 2001. "Generalized least squares with misspecified serial correlation structures," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 515-531.
- Koreisha, Sergio G & Pukkila, Tarmo, 1995. "A Comparison between Different Order-Determination Criteria for Identification of ARIMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 127-131, January. Full references (including those not matched with items on IDEAS)