# Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run

## TL;DR

This paper explores the use of sparse methods, specifically LASSO combined with principal components analysis, to improve short-term GDP component forecasts by leveraging high-dimensional monthly data.

## Contribution

It introduces a novel LASSO-PC approach that enhances forecasting accuracy of GDP components using sparse structures and combines variable selection with principal components analysis.

## Key findings

- Sparse methods outperform benchmark models in forecasting accuracy.
- LASSO-PC modification further improves forecast performance.
- Sparse structures effectively identify relevant explanatory variables.

## Abstract

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.