​​Paolo Gorgi​

Refereed articles

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1. Gorgi, P., and Koopman, S. J. (in press). Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects. Journal of Econometrics, https://doi.org/10.1016/j.jeconom.2021.06.010.

2. Gorgi, P., Koopman, S. J., and Lit, R. (2023). Estimation of final standings in football competitions with premature ending: the case of COVID-19. AStA Advances in Statistical Analysis, 107, 233-250.

3. Blasques, F., Gorgi, P., and Koopman, S. J. (2021). Missing observations in observation-driven time series models. Journal of Econometrics, 221, 542-568. (R code available here)

4. Gorgi, P. (2020). Beta–negative binomial auto‐regressions for modelling integer‐valued time series with extreme observations. Journal of the Royal Statistical Society: Series B, 82, 1325-1347.​

5. Gorgi, P., Koopman, S. J., and Lit, R. (2019). The analysis and forecasting of tennis matches by using a high dimensional dynamic model. Journal of the Royal Statistical Society: Series A, 182, 1393-1409.

6. Blasques, F., Gorgi, and P., Koopman, S. J. (2019). Accelerating score-driven time series models. Journal of Econometrics, 212, 359-376.

7. Gorgi, P., Koopman, S. J., and Li, M. (2019). Forecasting economic time series using score-driven dynamic models with mixed data sampling. International Journal of Forecasting, 35, 1735-1747.

8. Gorgi, P., Hansen, P. R., Janus, P., and Koopman, S. J. (2019). Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model. Journal of Financial Econometrics, 17, 1-32. (R code available here).

9. Angelini, G., and Gorgi, P. (2018). DSGE Models with observation-driven time-varying volatility Economics Letters, 171, 169-171.

10. Blasques, F., Gorgi, P., Koopman, S. J., and Wintenberger, O. (2018). Feasible invertibility conditions and maximum likelihood estimation of observation-driven models. Electronic Journal of Statistics, 12, 1019-1052.

11. Gorgi, P. (2018). Integer-valued autoregressive models with survival probability driven by a stochastic recurrence equation. Journal of Time Series Analysis, 39, 150-171.

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Working papers

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• Blasques, F., Gorgi, P., Koopman, S. J., and Sampi J. (2023). Does trade integration imply growth in Latin America? Evidence from a dynamic spatial spillover model.

• Blasques, F., Brummelen, J., Gorgi, P., and Koopman, S. J. (2022). Maximum Likelihood Estimation for Non-Stationary Location Models with Mixture of Normal Distributions.

• Blasques, F., Gorgi, P., and Koopman, S. J. (2021). Conditional score residuals and diagnostic analysis of serial dependence in time series models.

• Gorgi, P., Koopman, S. J., and Schaumburg, J. (2021). Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors.​​

• Blasques, F., Gorgi, P., Koopman, S. J., and Wintenberger, O. (2015). A note on "Continuous Invertibility and stable QMLE of the EGARCH(1,1) model".

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Talks in seminars and conferences

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• Seminar at department of Statistical Sciences, University of Padova, Italy (February 2023). Conditional score residuals and diagnostic analysis of serial dependence in time series models.

• Seminar at department of Economics, University of Verona, Italy (February 2023). Conditional score residuals and diagnostic analysis of serial dependence in time series models.

• SoFiE 2022 Annual Conference, Cambridge, UK (June 2022). Conditional score residuals and diagnostic analysis of serial dependence in time series models.

• Finance research seminar, University of Copenhagen, Denmark (October 2021). Conditional score residuals and diagnostic analysis of serial dependence in time series models.

• AMASES 2019 meeting, Perugia, Italy (September 2019). Forecasting economic time series using score-driven models with mixed-data sampling.

• IAAE 2019 conference, Nicosia, Cyprus (June 2019). Missing observations in observation-driven time series models.

• Cambridge-INET: conference on score-driven models, Cambridge, UK (March 2019). A general class of observation-driven time series models for bounded data: theory and applications.

• RMSE workshop, Koblenz, Germany (October 2018). Missing observations in observation-driven time series models.

• RCEA workshop, Rimini, Italy (June 2018). Forecasting economic time series using score-driven models with mixed-data sampling.

• NESG 2018 conference, Amsterdam, The Netherlands (May 2018). Missing observations in observation-driven time series models.

• Central Bank Forecasting, Federal Reserve Bank of St. Louis, USA (November 2017). Forecasting economic time series using score-driven models with mixed-data sampling.

• ESEM 2017 conference, Lisbon, Portugal (August 2019). Feasible invertibility conditions and MLE of observation-driven models.

• COMPSTAT 2016 conference, Oviedo, Spain (August 2016). Integer-valued autoregressive models with dynamic coefficient driven by a stochastic recurrence equation.

• NESG 2016 conference, KU Leuven, Belgium (June 2016). Accelerating Score-Driven Models: Optimality, Estimation and Forecasting.

• Seminar at VU Amsterdam, The Netherlands (February 2016). On the consistency of the MLE for observation-driven models.

• Seminar at the University of Padua, Italy (December 2015). Observation-driven models: theory and methods.

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