Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/192046
Title: THE EFFECTIVENESS OF NEURAL NETWORKS IN NOWCASTING GDP
Authors: LEE XIAN WEI IVAN
Keywords: Nowcasting
Neural Networks
Dynamic Factor Model
Bayesian VAR
Mixed-Frequency Modelling
Issue Date: 5-Apr-2021
Citation: LEE XIAN WEI IVAN (2021-04-05). THE EFFECTIVENESS OF NEURAL NETWORKS IN NOWCASTING GDP. ScholarBank@NUS Repository.
Abstract: My paper adopts current advanced Neural Network methodologies which have been used and proven effective in other sectors and applies them to nowcasting GDP growth. More specifically, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are being reviewed. Looking at plots to determine the ability of models to time changes in GDP growth as well as looking at the root mean squared error (RMSE) of the nowcasts, my paper concludes with mixed results. RMSE of Neural Network models are similar to benchmark autoregressive (AR) model but Neural Network models are more volatile and move in tandem to changes in macroeconomic variables, giving more timely nowcasts of GDP. This is shown especially in analyses of two recent recessionary periods - the great recession of 2008-2009 as well as during the COVID-19 pandemic in 2020.
URI: https://scholarbank.nus.edu.sg/handle/10635/192046
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