Digital Public Infrastructure and Bayesian Nowcasting of India’s GDP

Karan Singh Bagavathinathan and Sridevi Tandley Omprakash

MPRA Paper from University Library of Munich, Germany

Abstract: India’s post-2017 digital public infrastructure — GST, UPI, electricity, and bank credit — provides high-frequency administrative data weeks ahead of the 45-to-60-day quarterly GDP release. We build a 26-quarter panel of thirteen indicators (2019Q3–2025Q4) and evaluate seven nested specifications under a Ridge–Gradient-Boosting–Bayesian state-space ensemble. Adding UPI transaction volume yields the lowest out-of-sample RMSE (0.75 vs. 0.80 for the official-only baseline) over an eleven-quarter post-COVID window; the Clark–West gain is in the predicted direction but not conventionally significant (p = 0.18). A variance decomposition attributes 84 percent of forecast uncertainty to reducible components, indicating a power-limited rather than noise-dominated framework.

Keywords: GDP nowcasting; digital public infrastructure; Bayesian state-space models; high-frequency data; India (search for similar items in EconPapers)
JEL-codes: E00 (search for similar items in EconPapers)
Date: 2026-05-21
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