ABOUT
My research focuses on the development and application of score-driven time-varying parameter models and state-space models within macroeconomics and finance. I also explore volatility modeling using semi-parametric methods with high-frequency data.
My main empirical interests include the estimation of latent macroeconomic variables, such as the natural rate of interest, uncertainty, and other risk-related factors, the study of the interaction of financial and real sectors, quantitative analysis of speculative behavior in housing and financial markets, exploring monetary policy transmission mechanisms, and the analysis of inflation dynamics.
In addition, I have initiated research on structural vector autoregressive (SVAR) models and dynamic multiple quantile (DMQ) models, designing them for empirical macro-finance applications and aiming to deepen the understanding of dynamic economic relationships.
ACADEMIC BIO I completed my Master’s degree in International Business while working in the financial sector in Krakow, Poland, gaining practical experience in financial analysis within an international environment. I began my PhD studies in economics at the University of Valladolid and spent the 2021–2022 academic year as a visiting PhD student at the University of Verona. After completing my first year of coursework, I transferred to my current PhD program in Statistical Methods – Economics and Policy Analysis of Markets and Firms at the University of Salerno to focus more intensively on statistical methodology. During my doctoral studies, I undertook an extended research visit at the University of Malaya in the 2024–2025 academic year, where I initiated several research projects and collaborations, further strengthened through participation and presentations at conferences held in China.
TEACHINGI have experience tutoring at both undergraduate and graduate levels in international academic settings, including BSc-level Macroeconomics at AGH University in Krakow and MSc-level Econometrics at the University of Verona.
RESEARCH INTERESTS
Methodology
- Score-driven models — Development and application for macroeconomic and financial time series analysis.
- State‑space models (Kalman filter) — Implementation for estimating latent macroeconomic variables.
- Dynamic multiple quantile models — Inflation dynamics.
- Financial econometrics and volatility modeling — Construction of score-driven semi-parametric models using high-frequency data to analyze market volatility.
Empirical Applications
- Monetary policy — Inflation dynamics and transmission mechanisms involving the financial intermediary sector.
- Financial sector and the real economy — Bank lending behavior, deleveraging, uncertainty, and other risk-related factors.
- Speculative behavior — Focus on housing markets, real exchange rate behavior, and other financial asset markets.
PUBLICATIONS AND WORKING PAPERS
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Estimating the R-Star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence
Pál, Tibor and Storti, Giuseppe. Working Paper, July, 2025. -
Monetary Policy Models: Lessons from the Eurozone Crisis
Gutiérrez-Diez, Pedro J. and Pál, Tibor. Nature — Humanities and Social Sciences Communications, October 3, 2023. -
The Effects of Monetary Policy on House Prices in Spain: The Role of Economic and Monetary Union Membership in the Housing Bubble
Pál, Tibor. Central European Review of Economics and Management, June 1, 2018.
R-STAR PROJECT
Note: Shaded vertical areas indicate U.S. recessions as dated by the National Bureau of Economic Research (NBER). The Q4 2025 estimates will be available in mid-March.
Source: Calculations use the methodology (aaGAS specification) outlined in Pál, Tibor and Storti, Giuseppe: Estimating the R-Star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence (2025, Working Paper) .