A Multi-Factor Nowcasting Model for India

In this presentation, Kaustubh addresses the imperative to enhance nowcasting models for Indian GDP in the post-pandemic era. He brings together a diverse set of high-frequency economic indicators (HFIs) into multiple factors—nominal, survey-based, labor-market, and real economic activity—to produce GDP growth nowcasts. Unlike existing models that primarily focus on aggregate estimates, he evaluates each HFI’s contribution by analyzing its impact on nowcast revisions following new data releases.

In addition, the COVID-19 pandemic introduced outliers in HFIs, distorting model parameters and reducing forecasting accuracy. To address this, Kaustubh discusses incorporating the Oxford Stringency Index and proposes a novel sigmoid-based data transformation that minimizes model sensitivity to large shocks. This approach enables models to handle unexpected events more robustly without overreacting. This methodology improves nowcasting models’ resilience to outliers, providing valuable insights during volatile periods.