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Luca Rossini

Personal Details

First Name:Luca
Middle Name:
Last Name:Rossini
Suffix:
RePEc Short-ID:pro1002
[This author has chosen not to make the email address public]
http://lucarossini.wixsite.com/luca-rossini
Terminal Degree:2017 Dipartimento di Economia; Università Ca' Foscari Venezia (from RePEc Genealogy)

Affiliation

(17%) Dipartimento di Economia
Università Ca' Foscari Venezia

Venezia, Italy
http://www.unive.it/dip.economia
RePEc:edi:dsvenit (more details at EDIRC)

(83%) Afdeling Econometrie and Operations Research
School of Business and Economics
Vrije Universiteit Amsterdam

Amsterdam, Netherlands
https://sbe.vu.nl/nl/afdelingen-en-instituten/econometrie-en-or-nieuw/
RePEc:edi:ectvunl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Niko Hauzenberger & Michael Pfarrhofer & Luca Rossini, 2020. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," Papers 2011.04577, arXiv.org.
  2. Robert C. Smit & Francesco Ravazzolo & Luca Rossini, 2020. "Dynamic Bayesian forecasting of English Premier League match results with the Skellam distribution," BEMPS - Bozen Economics & Management Paper Series BEMPS72, Faculty of Economics and Management at the Free University of Bozen.
  3. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org.
  4. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  5. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Papers 2006.11265, arXiv.org, revised Sep 2020.
  6. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
  7. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
  8. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
  9. Rick Bohte & Luca Rossini, 2019. "Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models," Papers 1909.06599, arXiv.org.
  10. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  11. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.
  12. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse seemingly unrelated regression model (SUR)," Working Papers 2016:20, Department of Economics, University of Venice "Ca' Foscari".
  13. Luciana Dalla Valle & Fabrizio Leisen & Luca Rossini, 2016. "Bayesian Nonparametric Conditional Copula Estimation of Twin Data," Working Papers 2016:08, Department of Economics, University of Venice "Ca' Foscari".

Articles

  1. Luciana Dalla Valle & Fabrizio Leisen & Luca Rossini & Weixuan Zhu, 2020. "Bayesian analysis of immigration in Europe with generalized logistic regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(3), pages 424-438, February.
  2. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
  3. Fabrizio Leisen & Luca Rossini & Cristiano Villa, 2020. "Loss-based approach to two-piece location-scale distributions with applications to dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 309-333, June.
  4. Rossini, Luca & Contarini, Mario & Severini, Maurizio & Speranza, Stefano, 2020. "Reformulation of the Distributed Delay Model to describe insect pest populations using count variables," Ecological Modelling, Elsevier, vol. 436(C).
  5. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
  6. Leisen, Fabrizio & Mena, Ramsés H. & Palma, Freddy & Rossini, Luca, 2019. "On a flexible construction of a negative binomial model," Statistics & Probability Letters, Elsevier, vol. 152(C), pages 1-8.
  7. Rick Bohte & Luca Rossini, 2019. "Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(3), pages 1-18, September.
  8. Fabrizio Leisen & Luca Rossini & Cristiano Villa, 2018. "Objective bayesian analysis of the Yule–Simon distribution with applications," Computational Statistics, Springer, vol. 33(1), pages 99-126, March.
  9. Luciana Dalla Valle & Fabrizio Leisen & Luca Rossini, 2018. "Bayesian non‐parametric conditional copula estimation of twin data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 523-548, April.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

    Cited by:

    1. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

  2. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.

    Cited by:

    1. Florian, Huber & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2021. "Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs," Working Papers 2021-01, Joint Research Centre, European Commission (Ispra site).
    2. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 202108, Federal Reserve Bank of Cleveland.

  3. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.

    Cited by:

    1. Layna Mosley & Victoria Paniagua & Erik Wibbels, 2020. "Moving markets? Government bond investors and microeconomic policy changes," Economics and Politics, Wiley Blackwell, vol. 32(2), pages 197-249, July.

  4. Rick Bohte & Luca Rossini, 2019. "Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models," Papers 1909.06599, arXiv.org.

    Cited by:

    1. Karl Oton Rudolf & Samer Ajour El Zein & Nicola Jackman Lansdowne, 2021. "Bitcoin as an Investment and Hedge Alternative. A DCC MGARCH Model Analysis," Risks, MDPI, Open Access Journal, vol. 9(9), pages 1-22, August.
    2. Stefan Simeonov & Theodor Todorov & Daniel Nikolaev, 2020. "Testing Methods And Models To Forecast Cryptocurrencies Exchange Rate," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 17(1), pages 10-26.
    3. Mauro Bernardi & Stefano Grassi & Francesco Ravazzolo, 2020. "Bayesian Econometrics," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(11), pages 1-2, October.
    4. Daniel Ogachi & Paul Mugambi & Lydia Bares & Zoltan Zeman, 2021. "Idiosyncrasies of Money: 21st Century Evolution of Money," Economies, MDPI, Open Access Journal, vol. 9(1), pages 1-19, March.
    5. Ying Chen & Paolo Giudici & Branka Hadji Misheva & Simon Trimborn, 2020. "Lead Behaviour in Bitcoin Markets," Risks, MDPI, Open Access Journal, vol. 8(1), pages 1-14, January.
    6. Ahmed Ibrahim & Rasha Kashef & Menglu Li & Esteban Valencia & Eric Huang, 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(9), pages 1-21, August.

  5. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2018. "Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration," Working Papers No 2/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

    Cited by:

    1. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, Open Access Journal, vol. 11(10), pages 1-13, October.
    2. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    3. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    4. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    5. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    6. Kostrzewski, Maciej & Kostrzewska, Jadwiga, 2019. "Probabilistic electricity price forecasting with Bayesian stochastic volatility models," Energy Economics, Elsevier, vol. 80(C), pages 610-620.
    7. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org.
    8. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2021. "Short-term risk management for electricity retailers under rising shares of decentralized solar generation," Working Paper Series in Production and Energy 57, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    9. Anna Gloria Billé & Angelica Gianfreda & Filippo Del Grosso & Francesco Ravazzolo, 2021. "Forecasting Electricity Prices with Expert, Linear and Non-Linear Models," Working Paper series 21-20, Rimini Centre for Economic Analysis.

  6. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse VAR models," Papers 1608.02740, arXiv.org, revised Oct 2018.

    Cited by:

    1. Monica Billio & Roberto Casarin & Michele Costola & Lorenzo Frattarolo, 2019. "Opinion Dynamics and Disagreements on Financial Networks," International Association of Decision Sciences, Asia University, Taiwan, vol. 23(4), pages 24-51, December.
    2. Monica Billio & Roberto Casarin & Michele Costola & Matteo Iacopini, 2021. "COVID-19 spreading in financial networks: A semiparametric matrix regression model," Working Papers 2021:05, Department of Economics, University of Venice "Ca' Foscari".
    3. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2020. "Modeling Turning Points In Global Equity Market," DEM Working Papers Series 195, University of Pavia, Department of Economics and Management.
    4. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore & Wing-Keung Wong, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," International Association of Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    5. Hu, Guanyu, 2021. "Spatially varying sparsity in dynamic regression models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 23-34.
    6. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
    7. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    8. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    9. Zhang, Xingmin & Zhang, Shuai, 2021. "Optimal time-varying tail risk network with a rolling window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).

  7. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian nonparametric sparse seemingly unrelated regression model (SUR)," Working Papers 2016:20, Department of Economics, University of Venice "Ca' Foscari".

    Cited by:

    1. Chamberlain Mbah & Kris Peremans & Stefan Van Aelst & Dries F. Benoit, 2019. "Robust Bayesian seemingly unrelated regression model," Computational Statistics, Springer, vol. 34(3), pages 1135-1157, September.

  8. Luciana Dalla Valle & Fabrizio Leisen & Luca Rossini, 2016. "Bayesian Nonparametric Conditional Copula Estimation of Twin Data," Working Papers 2016:08, Department of Economics, University of Venice "Ca' Foscari".

    Cited by:

    1. Arbel, Julyan & Crispino, Marta & Girard, Stéphane, 2019. "Dependence properties and Bayesian inference for asymmetric multivariate copulas," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    2. Maximilian Coblenz & Simon Holz & Hans‐Jörg Bauer & Oliver Grothe & Rainer Koch, 2020. "Modelling fuel injector spray characteristics in jet engines by using vine copulas," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 863-886, August.
    3. Levi, Evgeny & Craiu, Radu V., 2018. "Bayesian inference for conditional copulas using Gaussian Process single index models," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 115-134.
    4. Huihui Lin & N. Rao Chaganty, 2021. "Multivariate distributions of correlated binary variables generated by pair-copulas," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-14, December.

Articles

  1. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    See citations under working paper version above.
  2. Rossini, Luca & Contarini, Mario & Severini, Maurizio & Speranza, Stefano, 2020. "Reformulation of the Distributed Delay Model to describe insect pest populations using count variables," Ecological Modelling, Elsevier, vol. 436(C).

    Cited by:

    1. Rossini, Luca & Bono Rosselló, Nicolás & Speranza, Stefano & Garone, Emanuele, 2021. "A general ODE-based model to describe the physiological age structure of ectotherms: Description and application to Drosophila suzukii," Ecological Modelling, Elsevier, vol. 456(C).

  3. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    See citations under working paper version above.
  4. Rick Bohte & Luca Rossini, 2019. "Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(3), pages 1-18, September. See citations under working paper version above.
  5. Luciana Dalla Valle & Fabrizio Leisen & Luca Rossini, 2018. "Bayesian non‐parametric conditional copula estimation of twin data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 523-548, April.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 15 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FOR: Forecasting (11) 2018-01-22 2018-02-05 2019-04-01 2019-09-23 2020-07-20 2020-07-27 2020-07-27 2020-08-31 2020-09-14 2020-10-05 2020-11-23. Author is listed
  2. NEP-ECM: Econometrics (8) 2016-04-09 2016-08-14 2019-06-17 2020-07-20 2020-07-27 2020-07-27 2020-08-31 2020-11-23. Author is listed
  3. NEP-ENE: Energy Economics (6) 2018-01-22 2018-02-05 2019-04-01 2020-07-27 2020-08-31 2020-11-23. Author is listed
  4. NEP-ETS: Econometric Time Series (5) 2019-06-17 2019-09-23 2020-07-27 2020-07-27 2020-11-23. Author is listed
  5. NEP-ORE: Operations Research (4) 2016-09-04 2019-09-23 2020-07-27 2020-09-14. Author is listed
  6. NEP-REG: Regulation (3) 2018-01-22 2018-02-05 2020-07-27
  7. NEP-MAC: Macroeconomics (1) 2016-09-04
  8. NEP-PAY: Payment Systems & Financial Technology (1) 2019-09-23
  9. NEP-RMG: Risk Management (1) 2020-07-27
  10. NEP-SPO: Sports & Economics (1) 2020-09-14

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