The aim of this paper is to estimate short-term forecasts of the US GDP components by expenditure approach sooner than they are officially released by the national institutions of statistics . For this reason, nowcasts along with 1- and 2-quarter forecasts are estimated by using available monthly information , officially released with a considerably smaller delay. The high-dimensionality problem of the monthly dataset used is solved by assuming sparse structures for the choice of leading indicators, capable of adequately explaining the dynamics of the GDP components. Variable selection and the estimation of the forecasts is performed by using the LASSO method, together with some of its popular modifications. Additionally, a modification of the LASSO is proposed, combining the methods of LASSO and principal components , in order to further improve the forecasting performance. Forecast accuracy of the models is evaluated by conducting pseudo-real-time forecasting exercises for four components of the GDP over the sample of 2005-2015, and compared with the benchmark ARMA models. The main results suggest that LASSO is able to outperform ARMA models when forecasting the GDP components and to identify leading explanatory variables. The proposed modification of the LASSO in some cases show further improvement in forecast accuracy.
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.
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The aim of this paper is to estimate short-term forecasts of
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
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for the choice
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the GDP components. Variable selection and the
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is performed by using the LASSO method, together with some of its popular modifications. Additionally, a modification of the LASSO is proposed, combining the methods of LASSO and principal components , in order to further
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Forecast accuracy of the models is evaluated by
We evaluate forecasting performance
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forecasting exercises for four components of the GDP
experiments for gross fixed capital formation, private consumption, imports and exports
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2005-2015, and compared with the benchmark ARMA
2005-2019, compared with benchmark ARMA and factor
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LASSO is able to outperform ARMA models when forecasting the GDP components
sparse methods can outperform the benchmarks
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leading
reasonable subsets of
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modification of the LASSO in some cases
LASSO-PC modification