Abstract

In the pursuit of localized economic understanding, lower-level governments increasingly demand a more dynamic portrayal of their economic landscape, prompting a shift from annual to quarterly GRDP assessments. However, this transition is hindered by limited data availability at the lower regional echelons, necessitating innovative approaches.
This paper draws on provincial data from Indonesia to address the challenge by advocating for the use of statistics, specifically employing the Denton-Chollette model for temporal disaggregation. Recognizing the potential of this model to enhance data granularity, the paper scrutinizes the predicted quarterly series of GRDP. It unravels the intricacies of this process, highlighting how various factors, including the quality of quarterly supplement indicators, contribute to the divergence in predictions.
The findings underscore the importance of statistical tools in overcoming data limitations and providing a more nuanced understanding of sectoral growth at the local level. By shedding light on the potential disparities in quarterly GRDP predictions, this research equips lower-level governments with valuable insights for more informed economic governance.