A Multi-Task Deep Learning Model for Inflation Forecasting : Dynamic Phillips Curve Neural Network

Managing inflation is vital for a stable economy, but forecasting remains challenging. ML methods, like neural networks, have shown promise in forecasting inflation and other macroeconomic variables. In this paper, I propose DPCNet, a deep multi-task learning model, to jointly forecast inflation and unemployment. DPCNet incorporates economic structure and memory of dynamic interactions among macroeconomic variables, leading to improved out-of-sample forecast accuracy compared to traditional benchmarks and state-of-the-art ML models