Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts
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.
|Date of creation:||Aug 2011|
|Date of revision:|
|Contact details of provider:|| Postal: Conner Hall, Athens, GA 30602|
Phone: (706) 542-2481
Fax: (706) 542-0739
Web page: http://www.caes.uga.edu/departments/agecon/index.html
More information through EDIRC
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.:
- 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.
- Man, K.S. & Tiao, G.C., 2006. "Aggregation effect and forecasting temporal aggregates of long memory processes," International Journal of Forecasting, Elsevier, vol. 22(2), pages 267-281.
- Tiao, G. C. & Guttman, Irwin, 1980. "Forecasting contemporal aggregates of multiple time series," Journal of Econometrics, Elsevier, vol. 12(2), pages 219-230, February.
- Andrea Silvestrini & David Veredas, 2008.
"Temporal aggregation of univariate and multivariate time series models: a survey,"
ULB Institutional Repository
2013/136205, ULB -- Universite Libre de Bruxelles.
- Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, 07.
- Andrea Silvestrini & David Veredas, 2008. "Temporal aggregation of univariate and multivariate time series models: A survey," Temi di discussione (Economic working papers) 685, Bank of Italy, Economic Research and International Relations Area.
- 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).
- 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.
- 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.
- 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.
- 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.
- Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
- 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).
- 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.
- 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.
- John J. Seater & Robert J. Rossana, .
"Temporal Aggregation and Economic Time Series,"
Working Paper Series
19, North Carolina State University, Department of Economics.
- Dimitris Georgoutsos & George Kouretas & Dikaios Tserkezos, . "Temporal Aggregation In Structural Var Models," Working Papers 9505, University of Crete, Department of Economics.
- Brewer, K. R. W., 1973. "Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models," Journal of Econometrics, Elsevier, vol. 1(2), pages 133-154, June.
- 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.
When requesting a correction, please mention this item's handle: RePEc:ags:ugeofs:113520. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search)
If references are entirely missing, you can add them using this form.