​​Paolo Gorgi​

Refereed articles

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1. Gorgi, P., Lauria, C.S.A., and Luati, A. (in press). On the optimality of score-driven models. Biometrika.

2. Blasques, F., van Brummelen, J., Gorgi, P., and Koopman, S. J. (2024). Maximum Likelihood Estimation for Non-Stationary Location Models with Mixture of Normal Distributions. Journal of Econometrics, 238(1), 105575.

3. Blasques, F., van Brummelen, J., Gorgi, P., and Koopman, S. J. (2024). A robust Beveridge–Nelson decomposition using a score-driven approach with an application. Economics Letters, 236, 111588.

4. Gorgi, P., and Koopman, S. J. (2023). Beta observation-driven models with exogenous regressors: a joint analysis of realized correlation and leverage effects. Journal of Econometrics, 237(2), 105177.

5. 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.

6. 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)

7. 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.​

8. 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.

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

10. 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.

11. 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).

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

13. 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.

14. 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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• Armillotta, M., and Gorgi, P. (2023). Pseudo-variance quasi-maximum likelihood estimation of semi-parametric time series models.

• 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., 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 CREST, Paris, France (November 2023). Conditional score residuals and diagnostic analysis of serial dependence in time series models.

• 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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