用可解释机器学习发现印度通胀存在非线性关系
Non-linear Phillips Curve for India: Evidence from Explainable Machine Learning
- 在新凯恩斯框架下引入可解释机器学习方法
- 模型预测精度显著优于传统线性模型
- 揭示通胀受预期主导、存在阈值效应，适合政策制定者参考
传统线性菲利普斯曲线虽广泛用于政策制定，但在结构突变和内在非线性情况下预测能力不足。本文基于新凯恩斯菲利普斯曲线框架，利用机器学习方法预测印度（主要新兴经济体）整体通胀，并通过可解释机器学习技术进行分析。结果表明，机器学习方法在预测准确性上显著优于标准线性模型。同时，可解释性分析显示，印度的菲利普斯曲线关系高度非线性，表现为关键变量间的阈值效应与交互作用。整体通胀主要由通胀预期驱动，其次为历史通胀和产出缺口，而供给冲击（除降雨外）影响甚微。研究证明机器学习能提升预测精度，并揭示通胀数据中的复杂非线性动态，为政策制定提供重要参考。
原文摘要 · Abstract (English)
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.
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