A multi-factor GDP nowcast model for India

The post-pandemic period has underscored the need to improve nowcasting models for Indian GDP. This paper consolidates a diverse set of high-frequency economic indicators (HFIs) into multiple factors – nominal, survey-based, labor market, and real economic activity – to nowcast GDP growth. Unlike existing models that primarily focus on overall GDP nowcasts, we evaluate the contribution of each HFI by analyzing its impact on GDP 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, we incorporate the Oxford Stringency Index and propose a novel data transformation based on sigmoid transformation that minimizes model sensitivity to large shocks. This approach enables the models to handle unexpected events more robustly without overreacting. Our methodology improves nowcasting models’ ability to handle outliers, providing valuable insights during volatile period.