| Abstract: |
The paper elaborates on machine and deep learning methods, as well as mixed
data sampling regression models, used for GDP nowcasting. The aim is to select
an adequate model that shows better performance on the data used. The paper
provides an answer to the question of whether the use of deep learning methods
can improve GDP nowcasting compared to traditional econometric methods, as
well as whether the use of specific high-frequency indicators improves the
quality of the models used. The paper examines the selection of adequate
indicators – both official and those from alternative sources, presents the
framework of mixed data sampling regression models and deep learning models
used for nowcasting, and gives an assessment of two such models on the example
of Serbian GDP. Serbia’s GDP was modelled for the period Q1 2016 – Q2 2023 and
the end of the observed period (six quarters) was used for the forecast.
Finally, two assessed models were compared – the mixed data sampling
regression model and the LSTM neural network. A special focus is placed on
ways to improve both models. The LSTM recurrent neural network model had a
smaller forecast error, with the use of a combination of official and
alternative (high-frequency) indicators, but the mixed data sampling
regression model also proved to be a good tool for decision-makers, since its
structure allows insight into the ongoing movements impacting GDP dynamics.
The use of alternative indicators in nowcasting improved the projections
through both presented models. |