A class of predictors based on concepts and results of the general theory of optimal algorithms is proposed for time series analysis as a possible alternative approach to classical statistical techniques. In econometric contexts, it frequently happens that time series are relatively short (less than one or two hundred data); in these cases, whenever statistical methods do not provide reliable forecast results, the optimal error predictors can be used effectively. Optimal error predictors are derived both for external and internal univariate and multivariate time series models. In particular, optimal algorithm predictors are presented for two classical economic models: the multiplier accelerator model and the dynamic multivariate Leontief model.
M., M., R., T., & Vicino, A. (1988). Optimal error predictors for economic models. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 19(7), 1189-1200.
|Titolo:||Optimal error predictors for economic models|
|Citazione:||M., M., R., T., & Vicino, A. (1988). Optimal error predictors for economic models. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 19(7), 1189-1200.|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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