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Digital Public Infrastructure and Bayesian Nowcasting of India’s GDP

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  • Bagavathinathan, Karan Singh
  • Tandley Omprakash, Sridevi

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.

Suggested Citation

  • Bagavathinathan, Karan Singh & Tandley Omprakash, Sridevi, 2026. "Digital Public Infrastructure and Bayesian Nowcasting of India’s GDP," MPRA Paper 129197, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:129197
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    References listed on IDEAS

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    • E00 - Macroeconomics and Monetary Economics - - General - - - General

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