A bottom-up approach for forecasting GDP in a data-rich environment

ABSTRACT

In an increasingly data-rich environment, the use of factor models for forecasting purposes has gained prominence in the literature and among practitioners. Herein, we assess the forecasting behaviour of factor models to predict several GDP components and investigate the performance of a bottom-up approach to forecast GDP growth in Portugal, which was one of the hardest hit economies during the latest economic and financial crisis. We find supporting evidence of the usefulness of factor models and noteworthy forecasting gains when conducting a bottom-approach drawing on the main aggregates of GDP.

KEYWORDS:

• Forecasting
• factor models
• bottom-up approach
• GDP

JEL CLASSIFICATION:

• C22
• C53

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ In line with the findings of Stock and Watson (2002b) for real variables, we also find that for most variables under study, the model without autoregressive dynamics outperforms the one with autoregressive terms.

² The aggregate GDP growth forecast based on the bottom-up approach is obtained by summing up the implicit forecast levels of the GDP components and computing the corresponding rate of change of the resulting aggregate.