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A factor augmented vector autoregressive model and a stacked de-noising auto-encoders forecast combination to predict the price of oil
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Resumo(s)
The following dissertation aims to show the benefits of a forecast combination between an
econometric and a deep learning approach. On one side, a Factor Augmented Vector Autoregressive
Model (FAVAR) with naming variables identification following Stock and Watson (2016)1; on the
other side, a Stacked De-noising Auto-Encoder with Bagging (SDAE-B) following Zhao, Li and Yu
(2017)2 are implemented. From January 2010 to September 2018 Two-hundred-eighty-one monthly
series are used to predict the price of the West Texas Intermediate (WTI). The model performance is
analysed by Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and
Directional Accuracy (DA). The combination benefits from both SDAE-B’s high accuracy and
FAVAR’s interpretation features through impulse response functions (IRFs) and forecast error
variance decomposition (FEVD).
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Oil industry FAVAR SDAE-B implementation