Shovon Sengupta, Bhanu Pratap, Amit Pawar
arXiv 6 Apr 2025 · Econometrics · publishedComputational Economics (2025) · 1 citations (OpenAlex)
arXiv:2504.05350 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lundberg SM, Lee SI (2017) A Unified Approach to Interpreting Model Predictions | 0.843 | 3 | 3 | 100% |
| 2 | Shapley L (1953) A value for n-person games | 0.843 | 3 | 3 | 100% |
| 3 | Štrumbelj E, Kononenko I (2010) An efficient explanation of individual classifications using game theory | 0.843 | 3 | 3 | 100% |
| 4 | Bicchal M, Durai SR (2019) Rationality of inflation expectations: an interpretation of Google Trends data | 0.737 | 4 | 3 | 50% |
| 5 | Joseph A (2019) Parametric inference with universal function approximators | 0.737 | 3 | 2 | 100% |
| 6 | Sengupta S, Chakraborty T, Singh SK (2024) Forecasting CPI inflation under economic policy and geopolitical uncertainties | 0.737 | 3 | 2 | 100% |
| 7 | Friedman JH (2001) Greedy function approximation: A gradient boosting machine | 0.644 | 2 | 2 | 100% |
| 8 | Gali J, Gertler M (1999) Inflation dynamics: A structural econometric analysis | 0.644 | 2 | 2 | 100% |
| 9 | Phillips AW (1958) The relation between unemployment and the rate of change of money wage rates in the United Kingdom, 1861-1957 | 0.644 | 2 | 2 | 100% |
| 10 | Taylor JB (1980) Aggregate dynamics and staggered contracts | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 92 scored citations.