Stefano Grassi
Stefano Grassi was a postdoctoral research fellow at CREATES from January 2011 to April 2012. He completed his PhD studies at University of Rome "Tor Vergata" , Italy (May 2010). During his PhD program, he was a visiting scholar at VU University of Amsterdam.
His research interests in the field of time series econometrics include state space methods, dynamic factor model, Bayesian estimation and testing, Sequential Monte Carlo Methods, stochastic volatility.
Belotti, F., Casini, A., Catania, L., Grassi, S. & Perron, P. (2023). Simultaneous Bandwidths Determination for DK-HAC Estimators and Long-Run Variance Estimation in Nonparametric Settings. Econometric Reviews, 42(3), 281-306. adv. onlinepublikation. https://doi.org/10.1080/07474938.2023.2178138
Catania, L. & Grassi, S. (2022). Forecasting cryptocurrency volatility. International Journal of Forecasting, 38(3), 878-894. adv. onlinepublikation. https://doi.org/10.1016/j.ijforecast.2021.06.005
Baştürk, N., Borowska, A., Grassi, S., Hoogerheide, L. & van Dijk, H. K. (2019). Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics, 210(1), 170-186. https://doi.org/10.1016/j.jeconom.2018.11.011
Catania, L., Grassi, S. & Ravazzolo, F. (2019). Forecasting Cryptocurrencies Under Model and Parameter Instability. International Journal of Forecasting, 35(2), 485-501. https://doi.org/10.1016/j.ijforecast.2018.09.005
Ferroni, F., Grassi, S. & León-Ledesma, M. A. (2019). Selecting structural innovations in DSGE models. Journal of Applied Econometrics, 34(2), 205-220. https://doi.org/10.1002/jae.2664
Marczak, M., Proietti, T. & Grassi, S. (2018). A data-cleaning augmented Kalman filter for robust estimation of state space models. Econometrics and Statistics, 5(1), 107-123. https://doi.org/10.1016/j.ecosta.2017.02.002
Catania, L., Grassi, S. & Ravazzolo, F. (2018). Predicting the Volatility of Cryptocurrency Time–Series. I M. Corazza, M. Durbán, A. Grané, C. Perna & M. Sibillo (red.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, MAF 2018 (s. 203-207). Springer. https://brage.bibsys.no/xmlui/bitstream/handle/11250/2482825/WP_CAMP_3_2018.pdf
Grassi, S., Nonejad, N. & Santucci de Magistris, P. (2017). Forecasting with the Standardized Self-Perturbed Kalman Filter. Journal of Applied Econometrics, 32(2), 318–341. https://doi.org/10.1002/jae.2522
Grassi, S., Proietti, T., Frale, C., Marcellino, M. & Mazzi , G. (2015). EuroMInd-C: A disaggregate monthly indicator of economic activity for the Euro area and member countries. International Journal of Forecasting, 31(3), 712–738. https://doi.org/10.1016/j.ijforecast.2014.08.015
Grassi, S. & Santucci de Magistris, P. (2015). It’s all about volatility of volatility: Evidence from a two-factor stochastic volatility model. Journal of Empirical Finance, 30, 62-78. https://doi.org/10.1016/j.jempfin.2014.11.007
Casarin, R., Grassi, S., Ravazzolo, F. & Dijk, H. K. V. (2015). Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox. Journal of Statistical Software, 68(3). https://doi.org/10.18637/jss.v068.i03
Proietti, T. & Grassi, S. (2015). Stochastic trends and seasonality in economic time series: new evidence from Bayesian stochastic model specification search. Empirical Economics, 48(3), 983-1011. https://doi.org/10.1007/s00181-014-0821-y
Grassi, S. & Proietti, T. (2014). Characterising economic trends by Bayesian stochastic model specification search. Computational Statistics & Data Analysis, 71, 359-374. https://doi.org/10.1016/j.csda.2013.02.024
Dziubinski, M. P. & Grassi, S. (2014). Heterogeneous Computing in Economics: A Simplified Approach. Computational Economics, 43(4), 485-495. https://doi.org/10.1007/s10614-013-9362-2
Grassi, S. & Santucci de Magistris, P. (2014). When long memory meets the Kalman filter: A comparative study. Computational Statistics & Data Analysis, 76(2), 301-319. https://doi.org/10.1016/j.csda.2012.10.018
Proietti, T. & Grassi, S. (2012). Bayesian stochastic model specification search for seasonal and calendar effects. I W. R. Bell, S. H. Holan & T. S. McElroy (red.), Economic Time Series: Modeling and Seasonality (s. 431-455). CRC Press. http://www.statsbiblioteket.dk/au/showrecord.jsp?record_id=sb_5575496
