Publication: Sparse partial least squares in time series for macroeconomic forecasting
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2012-08
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Abstract
Factor models have been applied extensively for forecasting when high dimensional datasets are available.
In this case, the number of variables can be very large. For instance, usual dynamic factor models in central
banks handle over 100 variables. However, there is a growing body of the literature that indicates that more
variables do not necessarily lead to estimated factors with lower uncertainty or better forecasting results.
This paper investigates the usefulness of partial least squares techniques, that take into account the variable
to be forecasted when reducing the dimension of the problem from a large number of variables to a smaller
number of factors. We propose different approaches of dynamic sparse partial least squares as a means of
improving forecast efficiency by simultaneously taking into account the variable forecasted while forming
an informative subset of predictors, instead of using all the available ones to extract the factors. We use the
well-known Stock and Watson database to check the forecasting performance of our approach. The
proposed dynamic sparse models show a good performance in improving the efficiency compared to widely
used factor methods in macroeconomic forecasting.
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Factor Models, Forecasting, Large Datasets, Partial Least Squares, Sparsity, Variable Selection