A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks
Article Abstract:
A study aimed at determining whether a class of adaptive prediction models or artificial neural networks is efficient in forecasting future values of nine macroeconomic variables adopted a model selection approach. Various out-of-sample forecast-based model selection criteria, including forecast error methods and forecast direction accuracy were utilized. Results obtained in real-time forecasting based on rolling window prediction measures reveal multivariate adaptive linear vector autoregression models usually surpass various models that are presented.
Publication Name: Review of Economics and Statistics
Subject: Mathematics
ISSN: 0034-6535
Year: 1997
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Encompassing the forecasts of U.S. trade balance models
Article Abstract:
An analysis describing the concept of forecast encompassing is presented. The concept refers to the absence of additional information in another economic model's forecasts. A test statistic is applied to another model's forecasts based on another model's error regression. A generalized statistic which is based on sets of dynamic nonlinear models with uncertain estimated coefficients is tested. It is shown that forecast application on US data involving trade balance of the generalized statistic indicates mis-specification.
Publication Name: Review of Economics and Statistics
Subject: Mathematics
ISSN: 0034-6535
Year: 1993
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Conditional forecasts in dynamic multivariate models
Article Abstract:
Bayesian methods are applied for the finite-sample distribution of conditional forecasts, an analytic approach which is useful for both vector autoregression and structural models.
Publication Name: Review of Economics and Statistics
Subject: Mathematics
ISSN: 0034-6535
Year: 1999
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