What do we know about comparing aggregate and disaggregate forecasts?
AbstractThis paper compares the performance of "aggregate" and "disaggregate" predictors in forecasting contemporaneously aggregated vector ARMA processes. An aggregate predictor is built by forecasting directly the aggregate process, as it results from contemporaneous aggregation of the data generating vector process. A disaggregate predictor is obtained by aggregating univariate forecasts for the individual components of the data generating vector process. The necessary and sufficient condition for the equality of mean squared errors associated with the two competing methods is provided in the bivariate VMA(1) case. Furthermore, it is argued that the condition of equality of predictors as stated in Lütkepohl (1984b, 1987, 2004) is only sufficient (not necessary) for the equality of mean squared errors. Finally, it is shown that the equality of forecasting accuracy for the two predictors can be achieved using specific assumptions on the parameters of the VMA(1) structure. Monte Carlo simulations are in line with the analytical results. An empirical application that involves the problem of forecasting the Italian monetary aggregate M1 in the pre-EMU period is presented to illustrate the main findings.
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2009020.
Date of creation: 01 Mar 2009
Date of revision:
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contemporaneous aggregation; forecasting;
Find related papers by JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-03-28 (All new papers)
- NEP-ECM-2010-03-28 (Econometrics)
- NEP-FOR-2010-03-28 (Forecasting)
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- Helmut Luetkepohl, 2009.
"Forecasting Aggregated Time Series Variables: A Survey,"
Economics Working Papers
ECO2009/17, European University Institute.
- Helmut Lütkepohl, 2010. "Forecasting Aggregated Time Series Variables: A Survey," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, vol. 2010(2), pages 1-26.
- Helmut Lütkepohl, 2012. "Fundamental Problems with Nonfundamental Shocks," Discussion Papers of DIW Berlin 1230, DIW Berlin, German Institute for Economic Research.
- Barrera, Carlos, 2013. "El sistema de predicción desagregada: Una evaluación de las proyecciones de inflación 2006-2011," Working Papers 2013-009, Banco Central de Reserva del Perú.
- Giacomo Sbrana & Andrea Silvestrini, 2012. "Comparing aggregate and disaggregate forecasts of first order moving average models," Statistical Papers, Springer, vol. 53(2), pages 255-263, May.
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