ABOUT

Tibor Pál I am an economist with a focus on macroeconomics, monetary policy, and time series econometrics. I study inflation dynamics, monetary policy transmission, financial volatility, and macro-financial interactions, with particular emphasis on latent macroeconomic variables such as the natural rate of interest and output gap. I develop and apply flexible econometric methods to study how macroeconomic and financial relationships evolve over time and under different macroeconomic conditions. My methodological work focuses on score-driven time-varying parameter models, state-space models, dynamic multiple quantile models, and volatility modeling using semiparametric methods and high-frequency data.

My PhD thesis, Essays on the Development and Application of Score-Driven Models in Finance and Macroeconomics, develops and applies score-driven models to selected problems in macroeconomics and finance. It covers univariate financial volatility modeling, multivariate state-space analysis with the Kalman filter, and dynamic quantile modeling using parametric and semiparametric approaches. View PhD thesis

ACADEMIC BIO I hold a PhD in Economics from the University of Salerno, specializing in Statistical Methods. I previously completed a 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. During my doctoral studies, I spent research periods at the University of Verona and the University of Malaya, where I developed research projects and collaborations. I have also presented my research at international conferences, including conferences in China.

TEACHING I have teaching experience at both undergraduate and graduate levels, including BSc-level Macroeconomics at AGH University in Krakow and MSc-level Econometrics at the University of Verona. My teaching interests include Macroeconomics, Econometrics, Time Series Econometrics, Financial Econometrics, and Monetary Economics.

CV / LinkedIn

RESEARCH INTERESTS

Empirical Applications

  • Monetary policy and macroeconomic dynamics — Inflation dynamics, monetary policy transmission, and the estimation of latent macroeconomic variables such as the natural rate of interest.
  • Financial sector and the real economy — Bank lending, deleveraging, uncertainty, and other risk-related variables.
  • Financial markets and volatility — Financial market volatility, risk, and forecasting using high-frequency data.

Methodology

  • Score-driven models — Development and application to macroeconomic and financial time series.
  • State-space models and Kalman filtering — Estimation of latent macroeconomic variables.
  • Dynamic multiple quantile models — Modeling dynamics across different parts of the conditional distribution.
  • Financial econometrics and volatility modeling — Development of score-driven semiparametric models using high-frequency data for volatility analysis and forecasting.

PUBLICATIONS AND WORKING PAPERS

PhD THESIS

Essays on the Development and Application of Score-Driven Models in Finance and Macroeconomics

University: University of Salerno (DISES)  |  Supervisor: Prof. Giuseppe Storti

View PhD thesis

Abstract: The thesis develops and applies score-driven models for particular problems in macroeconomics and finance. It takes the reader from univariate financial volatility modeling, through multivariate state-space analysis with the Kalman filter, to dynamic quantile modeling with less restrictive distributional assumptions. Both univariate and multivariate time-series settings are considered, using parametric and semiparametric approaches.

Chapters and Related Working Papers

  • Chapter 1: Modeling Volatility in Finance: Realized Dynamic Score Exponential GARCH
    with Giuseppe Storti and Kok Haur Ng.
    Status: Working Paper (Expected Autumn 2026)
  • Chapter 2: Estimating the R-star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence
    with Giuseppe Storti.
    Related Working Paper: "Estimating the R-star in the US: A Score-Driven State-Space Model with Time-Varying Volatility Persistence" [Current Version PDF]
    Status: Under Revision — Revised Version Expected Autumn 2026  |  View Interactive Results Page
  • Chapter 3: Inflation Dynamics and the Phillips Curve in the US: The Smoothed Dynamic Multiple Quantile Model
    (Solo-authored; Job Market Paper.)
    Related to Working Paper: "The Shape of the Phillips Curve: Beyond the Mean" [Current Version PDF]
    Status: Under Revision — Revised Version Expected Autumn 2026  |  View Interactive Results Page

R-STAR PROJECT

Shaded vertical areas indicate U.S. recessions as dated by the National Bureau of Economic Research (NBER). The shaded bands represent confidence intervals based on 5,000 simulated series. The Q3 2026 estimates will be available by mid-December.

The natural rate of interest (r-star) is the real short-term interest rate expected to prevail when the economy is operating at its full sustainable level and inflation is stable. The output gap measures the extent to which the economy is operating above or below this sustainable level. The IS curve measures the sensitivity of the output gap to deviations of the real short-term interest rate from its natural rate. Inflation volatility is associated with uncertainty around inflation.

HLW denotes the Holston-Laubach-Williams estimates reported by the Federal Reserve Bank of New York in Measuring the Natural Rate of Interest . CBO denotes the Congressional Budget Office estimate of the U.S. output gap.

Source: Author's calculations. The aaGAS estimates 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) .

INFLATION DYNAMICS AND THE PHILLIPS CURVE

Source: Calculations use the methodology outlined in Pál, Tibor: The Shape of the Phillips Curve: Beyond the Mean (2026, Working Paper). The working paper is currently being revised; the estimates shown here are based on the revised estimation and may differ from those reported in the current version.