Lecture by Tibor Pál, a Doctoral Student at the University of Salerno

The study entitled „Estimating the R-Star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence” will be presented by Tibor Pál, a doctoral student at the University of Salerno. (The abstract of the presentation is provided below).

All those interested in the topic are cordially invited to attend the seminar on 6 November. The event will take place at 17:00 in room B104 at the Faculty of Economic Sciences.

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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.