Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning

2025-04-06

AI Summary

Traditional linear Phillips curve models exhibit poor inflation forecasting performance for India due to structural breaks and inherent nonlinearity. Method: This paper develops an interpretable machine learning model grounded in the New Keynesian framework, integrating XGBoost, SHAP value analysis, partial dependence plots, and nonlinear elasticity estimation. Contribution/Results: We provide the first empirical evidence of a significant threshold effect and critical variable interactions in India’s Phillips curve; identify inflation expectations as the dominant driver and rainfall as the sole statistically significant supply shock variable. The model substantially outperforms linear benchmarks in predictive accuracy. Quantitative decomposition reveals the nonlinear contribution ranking of inflation expectations, lagged inflation, and output gap—thereby overcoming restrictive linearity assumptions. The framework delivers policy-relevant, interpretable, and data-driven insights for India’s monetary policy design and implementation.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The conventional linear Phillips curve model, while widely used in policymaking, often struggles to deliver accurate forecasts in the presence of structural breaks and inherent nonlinearities. This paper addresses these limitations by leveraging machine learning methods within a New Keynesian Phillips Curve framework to forecast and explain headline inflation in India, a major emerging economy. Our analysis demonstrates that machine learning-based approaches significantly outperform standard linear models in forecasting accuracy. Moreover, by employing explainable machine learning techniques, we reveal that the Phillips curve relationship in India is highly nonlinear, characterized by thresholds and interaction effects among key variables. Headline inflation is primarily driven by inflation expectations, followed by past inflation and the output gap, while supply shocks, except rainfall, exert only a marginal influence. These findings highlight the ability of machine learning models to improve forecast accuracy and uncover complex, nonlinear dynamics in inflation data, offering valuable insights for policymakers.

Problem

• Forecasting inflation in India using machine learning
• Addressing nonlinearities in the Phillips curve model
• Explaining key drivers of headline inflation dynamics

Innovation

• Machine learning enhances Phillips Curve forecasting
• Explainable AI reveals nonlinear inflation dynamics
• Key drivers: expectations, past inflation, output gap