Predictive Accuracy Gain from Disaggregate Sampling in ARIMA Models.
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.
Year of publication: |
1990
|
---|---|
Authors: | Nijman, Theo E ; Palm, Franz C |
Published in: |
Journal of Business & Economic Statistics. - American Statistical Association. - Vol. 8.1990, 4, p. 405-15
|
Publisher: |
American Statistical Association |
Saved in:
Saved in favorites
Similar items by person
-
Premia in Forward Foreign Exchange as Unobserved Components: A Note.
Nijman, Theo E, (1993)
-
Missing Observations in the Dynamic Regression Model.
Palm, Franz C, (1984)
-
Personal Pensions with Risk sharing: Affordable, Adequate and Stable Private Pensions in Europe
Bovenberg, A Lans, (2015)
- More ...