| Abstract: |
Partial least squares (PLS) is a simple factorisation method that works well
with high dimensional problems in which the number of observations is limited
given the number of independent variables. In this article, we show that PLS
can perform better than ordinary least squares (OLS), least absolute shrinkage
and selection operator (LASSO) and ridge regression in forecasting quarterly
gross domestic product (GDP) growth, covering the period from 2000 to 2023. In
fact, through dimension reduction, PLS proved to be effective in lowering the
out-of-sample forecasting error, specially since 2020. For the period
2000-2019, the four methods produce similar results, suggesting that PLS is a
valid regularisation technique like LASSO or ridge. |