  This paper aims to examine the use of sparse methods to forecast the real, in
the chain-linked volume sense, expenditure components of the US and EU GDP in
the short-run sooner than the national institutions of statistics officially
release the data. We estimate current quarter nowcasts along with 1- and
2-quarter forecasts by bridging quarterly data with available monthly
information announced with a much smaller delay. We solve the
high-dimensionality problem of the monthly dataset by assuming sparse
structures of leading indicators, capable of adequately explaining the dynamics
of analyzed data. For variable selection and estimation of the forecasts, we
use the sparse methods - LASSO together with its recent modifications. We
propose an adjustment that combines LASSO cases with principal components
analysis that deemed to improve the forecasting performance. We evaluate
forecasting performance conducting pseudo-real-time experiments for gross fixed
capital formation, private consumption, imports and exports over the sample of
2005-2019, compared with benchmark ARMA and factor models. The main results
suggest that sparse methods can outperform the benchmarks and to identify
reasonable subsets of explanatory variables. The proposed LASSO-PC modification
show further improvement in forecast accuracy.
