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Forecasting crude oil prices with DSGE models

Rubaszek, Michał - In: International journal of forecasting 37 (2021) 2, pp. 531-546

Persistent link: https://www.econbiz.de/10012792850

A comparison of monthly global indicators for forecasting growth

Baumeister, Christiane; Guérin, Pierre - In: International journal of forecasting 37 (2021) 3, pp. 1276-1295

Persistent link: https://www.econbiz.de/10012794858

Machine learning with parallel neural networks for analyzing and forecasting electricity demand

Chen, Yi-Ting; Sun, Edward W.; Lin, Yi-Bing - In: Computational economics 56 (2020) 2, pp. 569-597

Persistent link: https://www.econbiz.de/10012272048

Does the foreign sector help forecast domestic variables in DSGE models?

Kolasa, Marcin; Rubaszek, Michał - In: International journal of forecasting 34 (2018) 4, pp. 809-821

Persistent link: https://www.econbiz.de/10012031114

Semiparametric quantile averaging in the presence of high-dimensional predictors

Gooijer, Jan G. de; Zerom Godefay, Dawit - In: International journal of forecasting 35 (2019) 3, pp. 891-909

Persistent link: https://www.econbiz.de/10012305189

Demystifying the Meese-Rogoff puzzle : structural breaks or measures of forecasting accuracy?

Burns, Kelly; Moosa, Imad A. - In: Applied economics 49 (2017) 48, pp. 4897-4910

Persistent link: https://www.econbiz.de/10011844813

The random walk as a forecasting benchmark : drift or no drift?

Moosa, Imad A.; Burns, Kelly - In: Applied economics 48 (2016) 43/45, pp. 4131-4142

Persistent link: https://www.econbiz.de/10011639995

Jump detection and noise separation by a singular wavelet method for predictive analytics of high-frequency data

Chen, Yi-Ting; Lai, Wan-Ni; Sun, Edward W. - In: Computational economics 54 (2019) 2, pp. 809-844

Persistent link: https://www.econbiz.de/10012134380

Risk modelling and management : an overview

Chang, Chia-Lin; Allen, David E.; McAleer, Michael; … - 2013

Persistent link: https://www.econbiz.de/10009767001

Asymmetries in conditional mean and variance : modelling stock returns by asMA-asQGARCH

Brännäs, Kurt; Gooijer, Jan G. de - 2000

variancefunctions. In a genuine out-of-sample forecasting experiment theperformance of the best fitted asMA-asQGARCH model is compared … topure asMA and no-change forecasts. This is done both in terms ofconditional mean forecasting as well as in terms of risk … forecasting. …

Persistent link: https://www.econbiz.de/10011303289