Nowcasting GDP using data revisions in an emerging economy

This paper analyzes the revision process of monthly GDP growth in Peru using a newly constructed real-time dataset. A unified empirical framework is developed to test rationality by jointly examining serial and vintage correlations in revisions. The results reveal systematic and predictable revision patterns, primarily driven by benchmarking procedures followed by the statistical agency. Motivated by these findings, a simple forecast-adjustment model is proposed that improves early assessments of economic activity by anticipating subsequent revisions. The evidence illustrates how revision dynamics in an emerging economy can be exploited to enhance nowcasting performance.