Virbickaite Audrone - Faculty of CUNEF University

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Virbickaite Audrone

Virbickaite, Audrone

Quantitative Methods

PhD: Universidad Carlos III de Madrid

Office E7.4

audrone.virbickaite@cunef.edu

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BIO

Audrone Virbickaite joined CUNEF, where she is an Assistant Professor (on Tenure Track) of Statistics and Econometrics, in September 2020. Her research interests include Bayesian econometrics, time series analysis and volatility models, among others. Before joining CUNEF, she worked as an Assistant Professor of Econometrics at Universidad de las Islas Baleares (Palma de Mallorca) and, prior to that, she completed a two-year postdoctoral stay at Universität Konstanz (Germany). During her doctoral programme at the Department of Statistics of Universidad Carlos III de Madrid, Audra did a predoctoral research stay at the Chicago Booth School of Business. She has been recognised for one six-year research period or sexenio, participated in numerous international congresses and conferences, and published in internationally recognized scientific journals.

Research IDs:

Orcid: 0000-0002-7117-6906

Scopus: 55847624500

Publications in Scientific Journals

Nguyen, Hoang; Virbickaite, Audrone: “Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models”, Energy Economics, Art. 106738, 2023.

Rosselló, Jaume; Sansó, Andreu; Virbickaite, Audrone: “How local tourism managers can benefit from national surveys: Estimating tourism and restaurant expenditures for small market segments”, Current Issues in Tourism, 24(24), 3433-3449, 2021.

Virbickaite, Audrone; Ausín, Concepción; Galeano, Pedro: “Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction”, Energy Economics, 92, 104961, 2020.

Virbickaite, Audrone; Lopes, Hedibert F.: “Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model”, Applied Stochastic Models in Business and Industry, 35(4), 978-997, 2019.

Virbickaite, Audrone; Lopes, Hedibert F.; Ausín, Concepción; Galeano, Pedro: “Particle learning for Bayesian semi-parametric stochastic volatility model”, Econometric Reviews, 38(9), 1007-1023, 2019.

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