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Giacomo Sbrana

Personal Details

First Name:Giacomo
Middle Name:
Last Name:Sbrana
Suffix:
RePEc Short-ID:psb12
[This author has chosen not to make the email address public]

Affiliation

Neoma Business School

Rouen/Reims, France
http://www.neoma-bs.com/
RePEc:edi:neomafr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Matteo Pelagatti & Giacomo Sbrana, 2020. "Estimating high dimensional multivariate stochastic volatility models," Working Papers 428, University of Milano-Bicocca, Department of Economics, revised Jan 2020.
  2. Morana, Claudio & Sbrana, Giacomo, 2018. "Some Financial Implications of Global Warming: an Empirical Assessment," CSI: Climate and Sustainable Innovation 268728, Fondazione Eni Enrico Mattei (FEEM).
  3. Morana, Claudio & Sbrana, Giacomo, 2017. "Temperature Anomalies, Radiative Forcing and ENSO," MITP: Mitigation, Innovation and Transformation Pathways 253732, Fondazione Eni Enrico Mattei (FEEM).
  4. Giacomo Sbrana & Andrea Silvestrini & Fabrizio Venditti, 2015. "Short term inflation forecasting: the M.E.T.A. approach," Temi di discussione (Economic working papers) 1016, Bank of Italy, Economic Research and International Relations Area.
  5. Giacomo Sbrana & Andrea Silvestrini, 2014. "Random switching exponential smoothing and inventory forecasting," Temi di discussione (Economic working papers) 971, Bank of Italy, Economic Research and International Relations Area.
  6. Giacomo Sbrana & Andrea Silvestrini, 2013. "Forecasting aggregate demand: analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," Temi di discussione (Economic working papers) 929, Bank of Italy, Economic Research and International Relations Area.
  7. Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Post-Print hal-00809247, HAL.
  8. Francesco Grigoli & Giacomo Sbrana, 2012. "Determinants and dynamics of schooling and child labor in Bolivia," Post-Print hal-00779674, HAL.
  9. Giacomo Sbrana, 2010. "Forecasting damped trend exponential smoothing: an algebraic viewpoint," Working Papers 10-08, Association Française de Cliométrie (AFC).
  10. Giacomo Sbrana, 2010. "The exact linkage between the Beveridge-Nelson decomposition and other permanent-transitory decompositions," Working Papers 10-09, Association Française de Cliométrie (AFC).
  11. SBRANA, Giacomo & SILVESTRINI, Andrea, 2010. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," LIDAM Discussion Papers CORE 2010039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  12. SBRANA, Giacomo & SILVESTRINI, Andrea, 2009. "What do we know about comparing aggregate and disaggregate forecasts?," LIDAM Discussion Papers CORE 2009020, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

Articles

  1. Sbrana, Giacomo & Pelagatti, Matteo, 2024. "Optimal hierarchical EWMA forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 616-625.
  2. Giacomo Sbrana, 2023. "Modelling intermittent time series and forecasting COVID-19 spread in the USA," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(2), pages 465-475, February.
  3. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.
  4. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
  5. Sbrana, Giacomo & Silvestrini, Andrea, 2020. "Forecasting with the damped trend model using the structural approach," International Journal of Production Economics, Elsevier, vol. 226(C).
  6. Morana, Claudio & Sbrana, Giacomo, 2019. "Climate change implications for the catastrophe bonds market: An empirical analysis," Economic Modelling, Elsevier, vol. 81(C), pages 274-294.
  7. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
  8. Poloni, Federico & Sbrana, Giacomo, 2019. "Closed-form results for vector moving average models with a univariate estimation approach," Econometrics and Statistics, Elsevier, vol. 10(C), pages 27-52.
  9. Poloni, Federico & Sbrana, Giacomo, 2017. "Multivariate Trend–Cycle Extraction With The Hodrick–Prescott Filter," Macroeconomic Dynamics, Cambridge University Press, vol. 21(6), pages 1336-1360, September.
  10. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
  11. Poloni, Federico & Sbrana, Giacomo, 2015. "A note on forecasting demand using the multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 162(C), pages 143-150.
  12. Poloni, Federico & Sbrana, Giacomo, 2014. "Feasible generalized least squares estimation of multivariate GARCH(1, 1) models," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 151-159.
  13. Sbrana, Giacomo & Silvestrini, Andrea, 2014. "Random switching exponential smoothing and inventory forecasting," International Journal of Production Economics, Elsevier, vol. 156(C), pages 283-294.
  14. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
  15. Sbrana, Giacomo, 2013. "The exact linkage between the Beveridge–Nelson decomposition and other permanent-transitory decompositions," Economic Modelling, Elsevier, vol. 30(C), pages 311-316.
  16. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
  17. Sbrana, Giacomo & Poloni, Federico, 2013. "A closed-form estimator for the multivariate GARCH(1,1) model," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 152-162.
  18. Francesco Grigoli & Giacomo Sbrana, 2013. "Determinants And Dynamics Of Schooling And Child Labour In Bolivia," Bulletin of Economic Research, Wiley Blackwell, vol. 65, pages 17-37, May.
  19. Giacomo Sbrana, 2012. "Forecasting Aggregated Moving Average Processes with an Application to the Euro Area Real Interest Rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(1), pages 85-98, January.
  20. Giacomo Sbrana & Andrea Silvestrini, 2012. "Comparing aggregate and disaggregate forecasts of first order moving average models," Statistical Papers, Springer, vol. 53(2), pages 255-263, May.
  21. Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 93-107, March.
  22. Giacomo Sbrana, 2012. "Aggregation and marginalization of GARCH processes: some further results," METRON, Springer;Sapienza Università di Roma, vol. 70(2), pages 165-172, August.
  23. Giacomo Sbrana & Andrea Silvestrini, 2011. "Measuring core inflation in Italy comparing aggregate vs. disaggregate price data," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 5(3), pages 239-258, October.
  24. Giacomo Sbrana, 2011. "Structural time series models and aggregation: some analytical results," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 315-316, May.
  25. Giacomo Sbrana, 2008. "On the use of area-wide models in the Euro-zone," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(4), pages 499-518, October.
  26. Giacomo Sbrana, 2007. "Testing for Model Selection in Predicting Aggregate Variables," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 66(1), pages 3-28, March.

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. Morana, Claudio & Sbrana, Giacomo, 2018. "Some Financial Implications of Global Warming: an Empirical Assessment," CSI: Climate and Sustainable Innovation 268728, Fondazione Eni Enrico Mattei (FEEM).

    Cited by:

    1. Morana, Claudio, 2019. "Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices," Econometrics and Statistics, Elsevier, vol. 12(C), pages 42-65.
    2. Billio, Monica & Costola, Michele & Hristova, Iva & Latino, Carmelo & Pelizzon, Loriana, 2022. "Sustainable finance: A journey toward ESG and climate risk," SAFE Working Paper Series 349, Leibniz Institute for Financial Research SAFE.
    3. Monasterolo, Irene & Roventini, Andrea & Foxon, Tim J., 2019. "Uncertainty of climate policies and implications for economics and finance: An evolutionary economics approach," Ecological Economics, Elsevier, vol. 163(C), pages 177-182.
    4. Dunz, Nepomuk & Naqvi, Asjad & Monasterolo, Irene, 2019. "Climate Transition Risk, Climate Sentiments, and Financial Stability in a Stock-Flow Consistent approach," Ecological Economic Papers 23, WU Vienna University of Economics and Business.
    5. Bressan, Giacomo Maria & Romagnoli, Silvia, 2021. "Climate risks and weather derivatives: A copula-based pricing model," Journal of Financial Stability, Elsevier, vol. 54(C).
    6. Dunz, Nepomuk & Naqvi, Asjad & Monasterolo, Irene, 2021. "Climate sentiments, transition risk, and financial stability in a stock-flow consistent model," Journal of Financial Stability, Elsevier, vol. 54(C).

  2. Morana, Claudio & Sbrana, Giacomo, 2017. "Temperature Anomalies, Radiative Forcing and ENSO," MITP: Mitigation, Innovation and Transformation Pathways 253732, Fondazione Eni Enrico Mattei (FEEM).

    Cited by:

    1. Claudio Morana, 2016. "Macroeconomic and Financial Effects of Oil Price Shocks: Evidence for the Euro Area," Working Papers 2016.23, Fondazione Eni Enrico Mattei.

  3. Giacomo Sbrana & Andrea Silvestrini & Fabrizio Venditti, 2015. "Short term inflation forecasting: the M.E.T.A. approach," Temi di discussione (Economic working papers) 1016, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. James H. Stock & Mark W. Watson, 2015. "Core Inflation and Trend Inflation," NBER Working Papers 21282, National Bureau of Economic Research, Inc.
    2. Poloni, Federico & Sbrana, Giacomo, 2019. "Closed-form results for vector moving average models with a univariate estimation approach," Econometrics and Statistics, Elsevier, vol. 10(C), pages 27-52.
    3. Cogoljević, Dušan & Gavrilović, Milan & Roganović, Miloš & Matić, Ivana & Piljan, Ivan, 2018. "Analyzing of consumer price index influence on inflation by multiple linear regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 941-944.
    4. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
    5. Sbrana, Giacomo & Pelagatti, Matteo, 2024. "Optimal hierarchical EWMA forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 616-625.

  4. Giacomo Sbrana & Andrea Silvestrini, 2014. "Random switching exponential smoothing and inventory forecasting," Temi di discussione (Economic working papers) 971, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Wang, Renhe & Wang, Tong & Qian, Zhiyong & Hu, Shulan, 2023. "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
    4. Tsionas, Mike G., 2022. "Random and Markov switching exponential smoothing models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    6. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
    7. Hamidreza Mirtaheri & Piero Macaluso & Maurizio Fantino & Marily Efstratiadi & Sotiris Tsakanikas & Panagiotis Papadopoulos & Andrea Mazza, 2021. "Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids," Energies, MDPI, vol. 14(21), pages 1-22, November.

  5. Giacomo Sbrana & Andrea Silvestrini, 2013. "Forecasting aggregate demand: analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," Temi di discussione (Economic working papers) 929, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    2. Chun-Cheng Lin & Rou-Xuan He & Wan-Yu Liu, 2018. "Considering Multiple Factors to Forecast CO 2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach," Energies, MDPI, vol. 11(12), pages 1-25, December.
    3. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    4. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
    5. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    6. Scarpel, Rodrigo Arnaldo, 2015. "An integrated mixture of local experts model for demand forecasting," International Journal of Production Economics, Elsevier, vol. 164(C), pages 35-42.
    7. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
    8. Hakeem‐Ur Rehman & Guohua Wan & Raza Rafique, 2023. "A hybrid approach with step‐size aggregation to forecasting hierarchical time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 176-192, January.
    9. Poloni, Federico & Sbrana, Giacomo, 2015. "A note on forecasting demand using the multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 162(C), pages 143-150.

  6. Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Post-Print hal-00809247, HAL.

    Cited by:

    1. Hang Zhao & Jun Zhang & Xiaohui Wang & Hongxia Yuan & Tianlu Gao & Chenxi Hu & Jing Yan, 2021. "The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    2. Riccardo De Bonis & Andrea Silvestrini, 2014. "The Italian financial cycle: 1861-2011," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 8(3), pages 301-334, September.

  7. Francesco Grigoli & Giacomo Sbrana, 2012. "Determinants and dynamics of schooling and child labor in Bolivia," Post-Print hal-00779674, HAL.

    Cited by:

    1. Carla Canelas & Miguel Niño‐Zarazúa, 2019. "Schooling and Labor Market Impacts of Bolivia's Bono Juancito Pinto Program," Population and Development Review, The Population Council, Inc., vol. 45(S1), pages 155-179, December.
    2. Bredl, Sebastian, 2012. "Child Quality and Child Quantity: Evidence from Bolivian Household Surveys," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62065, Verein für Socialpolitik / German Economic Association.
    3. Ghulam Abid & Binish Khan & Zeeshan Rafiq & Alia Ahmed, 2015. "Child Trade-Off Theory: A Theoretical Discussion on the Structure, Causes, Consequences and Eradication of Child Labor," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 4(1), pages 24-34, March.
    4. Diego A. Vera Cossio, 2011. "Enrollment and child labor in Bolivia," Development Research Working Paper Series 11/2011, Institute for Advanced Development Studies.
    5. Carla Canelas & Miguel Niño-Zarazúa, 2018. "Schooling and labour market impacts of Bolivia's Bono Juancito Pinto," WIDER Working Paper Series wp-2018-36, World Institute for Development Economic Research (UNU-WIDER).

  8. Giacomo Sbrana, 2010. "The exact linkage between the Beveridge-Nelson decomposition and other permanent-transitory decompositions," Working Papers 10-09, Association Française de Cliométrie (AFC).

    Cited by:

    1. Murasawa, Yasutomo, 2015. "The multivariate Beveridge--Nelson decomposition with I(1) and I(2) series," MPRA Paper 66319, University Library of Munich, Germany.
    2. Riccardo De Bonis & Andrea Silvestrini, 2014. "The Italian financial cycle: 1861-2011," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 8(3), pages 301-334, September.

  9. SBRANA, Giacomo & SILVESTRINI, Andrea, 2010. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," LIDAM Discussion Papers CORE 2010039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    2. Ana María Iregui B. & Luis Fernando Melo V. & María Teresa Ramírez G. & Carmen Cecilia Delgado R., 2013. "El efecto de la volatilidad y del desalineamiento de la tasa de cambio real sobre la actividad de las empresas en Colombia," Borradores de Economia 11106, Banco de la Republica.
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2014. "Random switching exponential smoothing and inventory forecasting," International Journal of Production Economics, Elsevier, vol. 156(C), pages 283-294.

  10. SBRANA, Giacomo & SILVESTRINI, Andrea, 2009. "What do we know about comparing aggregate and disaggregate forecasts?," LIDAM Discussion Papers CORE 2009020, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    Cited by:

    1. Barrera, Carlos, 2013. "El sistema de predicción desagregada: Una evaluación de las proyecciones de inflación 2006-2011," Working Papers 2013-009, Banco Central de Reserva del Perú.
    2. Helmut Lütkepohl, 2012. "Fundamental Problems with Nonfundamental Shocks," Discussion Papers of DIW Berlin 1230, DIW Berlin, German Institute for Economic Research.
    3. Giacomo Sbrana & Andrea Silvestrini, 2012. "Comparing aggregate and disaggregate forecasts of first order moving average models," Statistical Papers, Springer, vol. 53(2), pages 255-263, May.
    4. Julius Stakenas, 2015. "Forecasting Lithuanian Inflation," Bank of Lithuania Working Paper Series 17, Bank of Lithuania.
    5. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.

Articles

  1. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.

    Cited by:

    1. Giacomo Sbrana & Andrea Silvestrini, 2024. "The structural Theta method and its predictive performance in the M4-Competition," Temi di discussione (Economic working papers) 1457, Bank of Italy, Economic Research and International Relations Area.

  2. Sbrana, Giacomo & Silvestrini, Andrea, 2020. "Forecasting with the damped trend model using the structural approach," International Journal of Production Economics, Elsevier, vol. 226(C).

    Cited by:

    1. Wang, Renhe & Wang, Tong & Qian, Zhiyong & Hu, Shulan, 2023. "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
    4. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.

  3. Morana, Claudio & Sbrana, Giacomo, 2019. "Climate change implications for the catastrophe bonds market: An empirical analysis," Economic Modelling, Elsevier, vol. 81(C), pages 274-294.

    Cited by:

    1. Ameur, Hachmi Ben & Han, Xuyuan & Liu, Zhenya & Peillex, Jonathan, 2022. "When did global warming start? A new baseline for carbon budgeting," Economic Modelling, Elsevier, vol. 116(C).
    2. Karl Demers‐Bélanger & Van Son Lai, 2020. "Diversification benefits of cat bonds: An in‐depth examination," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 29(5), pages 165-228, December.
    3. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Morana, Claudio, 2019. "Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices," Econometrics and Statistics, Elsevier, vol. 12(C), pages 42-65.
    5. Baiardi, Donatella & Morana, Claudio, 2021. "Climate change awareness: Empirical evidence for the European Union," Energy Economics, Elsevier, vol. 96(C).
    6. Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.
    7. Yuan, Zhengrong & Ding, Hai & Yu, Qiuzuo, 2024. "High temperature, bargaining power and within-firm wage inequality: Evidence from China," Economic Modelling, Elsevier, vol. 135(C).
    8. Claudio Morana, 2021. "A new macro-financial condition index for the euro area," Working Paper series 21-07, Rimini Centre for Economic Analysis, revised Sep 2021.
    9. Reimund Schwarze & Oleksandr Sushchenko, 2022. "Climate Insurance for Agriculture in Europe: On the Merits of Smart Contracts and Distributed Ledger Technologies," JRFM, MDPI, vol. 15(5), pages 1-16, May.
    10. Stefano Battiston & Petr Jakubik & Irene Monasterolo & Keywan Riahi & Bas van Ruijven, 2019. "Climate Risk Assessment of the Sovereign Bond Portfolio of European Insurers," EIOPA Financial Stability Report - Thematic Articles 15, EIOPA, Risks and Financial Stability Department.
    11. Kyungsik Nam, 2021. "Nonlinear Cointegrating Regression of the Earth’s Surface Mean Temperature Anomalies on Total Radiative Forcing," Econometrics, MDPI, vol. 9(1), pages 1-25, February.
    12. Thomas Mutsvene & Heinz Eckart Klingelhöfer, 2024. "Development of New Products for Climate Change Resilience in South Africa—The Catastrophe Resilience Bond Introduction," JRFM, MDPI, vol. 17(5), pages 1-17, May.
    13. Fernanda Valente & Márcio Laurini, 2020. "Tornado Occurrences in the United States: A Spatio-Temporal Point Process Approach," Econometrics, MDPI, vol. 8(2), pages 1-26, June.
    14. Braga, Joao Paulo & Semmler, Willi & Grass, Dieter, 2021. "De-risking of green investments through a green bond market – Empirics and a dynamic model," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    15. Xiaowei Chen & Hong Li & Yufan Lu & Rui Zhou, 2024. "Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective," Papers 2405.00697, arXiv.org, revised Aug 2024.
    16. Curcio, Domenico & Gianfrancesco, Igor & Vioto, Davide, 2023. "Climate change and financial systemic risk: Evidence from US banks and insurers," Journal of Financial Stability, Elsevier, vol. 66(C).
    17. Xiaodong Zhu & Zijing Jin & Shunsuke Managi & XiRong Xun, 2021. "How meteorological disasters affect the labor market? The moderating effect of government emergency response policy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2625-2640, July.
    18. Manveer Kaur Mangat & Erhard Reschenhofer, 2020. "Frequency-Domain Evidence for Climate Change," Econometrics, MDPI, vol. 8(3), pages 1-15, July.
    19. Irene Monasterolo, 2020. "Embedding Finance in the Macroeconomics of Climate Change: Research Challenges and Opportunities Ahead," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 21(04), pages 25-32, November.

  4. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.

    Cited by:

    1. Sbrana, Giacomo & Silvestrini, Andrea, 2020. "Forecasting with the damped trend model using the structural approach," International Journal of Production Economics, Elsevier, vol. 226(C).
    2. Wang, Renhe & Wang, Tong & Qian, Zhiyong & Hu, Shulan, 2023. "A Bayesian estimation approach of random switching exponential smoothing with application to credit forecast," Finance Research Letters, Elsevier, vol. 58(PC).
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
    4. Tsionas, Mike G., 2022. "Random and Markov switching exponential smoothing models," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Sbrana, Giacomo & Silvestrini, Andrea, 2023. "The RWDAR model: A novel state-space approach to forecasting," International Journal of Forecasting, Elsevier, vol. 39(2), pages 922-937.
    6. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    7. Min Zhao & Yu Fang & Debao Dai, 2023. "Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios," Sustainability, MDPI, vol. 15(5), pages 1-22, February.

  5. Poloni, Federico & Sbrana, Giacomo, 2017. "Multivariate Trend–Cycle Extraction With The Hodrick–Prescott Filter," Macroeconomic Dynamics, Cambridge University Press, vol. 21(6), pages 1336-1360, September.

    Cited by:

    1. Poloni, Federico & Sbrana, Giacomo, 2019. "Closed-form results for vector moving average models with a univariate estimation approach," Econometrics and Statistics, Elsevier, vol. 10(C), pages 27-52.
    2. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
    3. Martin Boďa & Mariana Považanová, 2023. "How credible are Okun coefficients? The gap version of Okun’s law for G7 economies," Economic Change and Restructuring, Springer, vol. 56(3), pages 1467-1514, June.

  6. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
    See citations under working paper version above.
  7. Poloni, Federico & Sbrana, Giacomo, 2015. "A note on forecasting demand using the multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 162(C), pages 143-150.

    Cited by:

    1. Poloni, Federico & Sbrana, Giacomo, 2019. "Closed-form results for vector moving average models with a univariate estimation approach," Econometrics and Statistics, Elsevier, vol. 10(C), pages 27-52.
    2. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
    3. Sbrana, Giacomo & Pelagatti, Matteo, 2024. "Optimal hierarchical EWMA forecasting," International Journal of Forecasting, Elsevier, vol. 40(2), pages 616-625.

  8. Poloni, Federico & Sbrana, Giacomo, 2014. "Feasible generalized least squares estimation of multivariate GARCH(1, 1) models," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 151-159.

    Cited by:

    1. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.

  9. Sbrana, Giacomo & Silvestrini, Andrea, 2014. "Random switching exponential smoothing and inventory forecasting," International Journal of Production Economics, Elsevier, vol. 156(C), pages 283-294.
    See citations under working paper version above.
  10. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
    See citations under working paper version above.
  11. Sbrana, Giacomo, 2013. "The exact linkage between the Beveridge–Nelson decomposition and other permanent-transitory decompositions," Economic Modelling, Elsevier, vol. 30(C), pages 311-316.
    See citations under working paper version above.
  12. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, vol. 146(1), pages 185-198.
    See citations under working paper version above.
  13. Sbrana, Giacomo & Poloni, Federico, 2013. "A closed-form estimator for the multivariate GARCH(1,1) model," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 152-162.

    Cited by:

    1. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    2. Li, Qi & Lian, Heng & Zhu, Fukang, 2016. "Robust closed-form estimators for the integer-valued GARCH (1,1) model," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 209-225.
    3. Poloni, Federico & Sbrana, Giacomo, 2014. "Feasible generalized least squares estimation of multivariate GARCH(1, 1) models," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 151-159.

  14. Francesco Grigoli & Giacomo Sbrana, 2013. "Determinants And Dynamics Of Schooling And Child Labour In Bolivia," Bulletin of Economic Research, Wiley Blackwell, vol. 65, pages 17-37, May.
    See citations under working paper version above.
  15. Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 93-107, March.
    See citations under working paper version above.
  16. Giacomo Sbrana, 2012. "Aggregation and marginalization of GARCH processes: some further results," METRON, Springer;Sapienza Università di Roma, vol. 70(2), pages 165-172, August.

    Cited by:

    1. Morana, Claudio, 2019. "Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices," Econometrics and Statistics, Elsevier, vol. 12(C), pages 42-65.

  17. Giacomo Sbrana, 2011. "Structural time series models and aggregation: some analytical results," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 315-316, May.

    Cited by:

    1. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.
    2. Giacomo Sbrana & Andrea Silvestrini, 2012. "Temporal aggregation of cyclical models with business cycle applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 93-107, March.
    3. Sbrana, Giacomo & Silvestrini, Andrea, 2014. "Random switching exponential smoothing and inventory forecasting," International Journal of Production Economics, Elsevier, vol. 156(C), pages 283-294.
    4. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
    5. Riccardo De Bonis & Andrea Silvestrini, 2014. "The Italian financial cycle: 1861-2011," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 8(3), pages 301-334, September.

  18. Giacomo Sbrana, 2008. "On the use of area-wide models in the Euro-zone," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(4), pages 499-518, October.

    Cited by:

    1. Libero Monteforte, 2004. "Aggregation bias in macro models: does it matter foir the euro area?," Temi di discussione (Economic working papers) 534, Bank of Italy, Economic Research and International Relations Area.

  19. Giacomo Sbrana, 2007. "Testing for Model Selection in Predicting Aggregate Variables," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 66(1), pages 3-28, March.

    Cited by:

    1. Giacomo Sbrana, 2008. "On the use of area-wide models in the Euro-zone," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(4), pages 499-518, October.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 18 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-ENV: Environmental Economics (9) 2017-02-19 2017-02-19 2017-03-05 2017-03-19 2018-01-08 2018-03-05 2018-04-23 2019-01-14 2019-04-01. Author is listed
  2. NEP-ECM: Econometrics (7) 2010-03-28 2010-10-02 2011-01-16 2013-10-18 2014-11-07 2015-06-20 2020-02-24. Author is listed
  3. NEP-FOR: Forecasting (6) 2010-03-28 2010-10-02 2011-01-16 2013-10-18 2014-11-07 2015-06-20. Author is listed
  4. NEP-ETS: Econometric Time Series (5) 2011-01-16 2011-01-16 2013-10-18 2014-11-07 2020-02-24. Author is listed
  5. NEP-ORE: Operations Research (2) 2015-06-20 2020-02-24
  6. NEP-RMG: Risk Management (2) 2010-10-02 2020-02-24
  7. NEP-EDU: Education (1) 2011-01-30
  8. NEP-EEC: European Economics (1) 2015-06-20
  9. NEP-LAB: Labour Economics (1) 2011-01-30
  10. NEP-MAC: Macroeconomics (1) 2015-06-20
  11. NEP-MON: Monetary Economics (1) 2015-06-20
  12. NEP-URE: Urban and Real Estate Economics (1) 2011-01-30

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