Estimating the R-Star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence

Pál, Tibor and Storti, Giuseppe (2025): Estimating the R-Star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence.

Abstract

This paper analyses the dynamics of the natural rate of interest (r-star) in the US using a score-driven state-space model within the Laubach–Williams structural framework. Compared to standard score-driven specifications, the proposed model enhances flexibility in variance adjustment by assigning time-varying weights to both the conditional likelihood score and the inertia coefficient in the volatility updating equations. The improved state dependence of volatility dynamics effectively accounts for sudden shifts in volatility persistence induced by highly volatile unexpected events. In addition, allowing time variation in the IS and Phillips curve relationships enables the analysis of structural changes in the US economy that are relevant to monetary policy. The results indicate that the advanced models improve the precision of r-star estimates by responding more effectively to changes in macroeconomic conditions.

• Item Type: MPRA Paper
• Language: English
• Keywords: r-star, state-space, Kalman filter, score-driven models
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy

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