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Forecasting GDP growth : a comprehensive comparison of employing machine learning algorithms and time series regression models

Premraj, Pirasant
Master thesis
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https://hdl.handle.net/11250/2644655
Utgivelsesdato
2019
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  • Master Thesis [3384]
Sammendrag
In this paper, we do a comprehensive comparison of forecasting Gross Domestic

Product (GDP) growth using Machine Learning algorithms and traditional time

series regression models on the following economies: Australia, Canada, Euro Area,

Germany, Spain, France, Japan, Sweden, Great Britain and USA. The ML algorithms

we employ are Bayesian Additive Trees Regression Trees (BART), Elastic-Net

Regularized Generalized Linear Models (GLMNET), Stochastic Gradient Boosting

(GBM) and eXtreme Gradient Boosting (XGBoost), while Autoregressive (AR) models,

Autoregressive Integrated Moving Average (ARIMA) models and Vector Autoregressive

(VAR) models represents the traditional time series regression methods. The results

assert that the multivariate VAR models are superior, indicating the chosen variables’

and the models’ suitability of forecasting GDP growth. Furthermore, we also do

an assessment of the top three variables that drives the best performing Machine

Learning algorithm of XGBoost to investigate whether it suggests the same variables

in forecasting GDP growth as macroeconomic theory. In general we do see some

evidence, but in many cases the algorithm emphasizes other variables than what

macroeconomic theory suggests.

Keywords – Time Series, Machine Learning, Econometric, GDP, Forecast

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