Nowcasting has become a useful tool for making timely predictions of gross domestic product (GDP) in a data-rich environment. However, in developing economies this is more challenging due to substantial revisions in GDP data and the limited availability of predictor variables.Taking India as a leading case,we use a dynamic factor model nowcasting method to analyse these two issues. Firstly, we propose to compare nowcasts of the first release of GDP to those of the final release to assess differences in their predictability. Secondly, we expand a standard set of predictors typically used for nowcasting GDP with nominal and international series, in order to proxy the variation in missing employment and service sector variables in India.We find that the factor model improves over several benchmarks, including bridge equations, but only for the final GDP release and not for the first release. Also, the nominal and international series improve predictions over and above real series. This suggests that future studies of nowcasting in developing economies which have similar issues of data revisions and availability as India should be careful in analysing first- vs. final release GDP data, and may find that predictions are improved when additional variables from more timely international data sources are included.
- business cycle