Abstract
This paper proposes a method for reducing model errors
in regressions when modelling macroeconomic variables by using machine learning
algorithms and traditional time series regression models. In this paper,
machine learning models are subjected to repeated k-fold cross validation and
hyperparameter tuning. The linear model uses repeated k-fold cross validation,
on the other hand, the traditional time series model Mixed Data Sampling Auto
Regressive Distribution Lag model is run without repeated k-fold cross
validation and hyperparameter tuning. The results show that integrating repeated k-fold cross validation with hyperparameter tuning increases the
overall performance of machine learning algorithms and each model records the
average outcome from all folds and runs. These findings demonstrate how machine
learning models outperform the traditional time series model.
