Reduced-rank Envelope Vector Autoregressive Models
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- Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017.
"A vector heterogeneous autoregressive index model for realized volatility measures,"
International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
- Cubadda, G. & Guardabascio, B. & Hecq, A.W., 2015. "A Vector Heterogeneous Autoregressive Index model for realized volatility measures," Research Memorandum 033, Maastricht University, Graduate School of Business and Economics (GSBE).
- Gianluca Cubadda & Barbara Guardabascio & Alain Hecq, 2016. "A Vector Heterogeneous Autoregressive Index Model for Realized Volatily Measures," CEIS Research Paper 391, Tor Vergata University, CEIS, revised 23 Jul 2016.
- Z. Su & G. Zhu & X. Chen & Y. Yang, 2016. "Sparse envelope model: efficient estimation and response variable selection in multivariate linear regression," Biometrika, Biometrika Trust, vol. 103(3), pages 579-593.
- Franchi, Massimo & Paruolo, Paolo, 2011. "A characterization of vector autoregressive processes with common cyclical features," Journal of Econometrics, Elsevier, vol. 163(1), pages 105-117, July.
- De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008.
"Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?,"
Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
- Reichlin, Lucrezia & Giannone, Domenico & De Mol, Christine, 2006. "Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components?," CEPR Discussion Papers 5829, C.E.P.R. Discussion Papers.
- De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?," Discussion Paper Series 1: Economic Studies 2006,32, Deutsche Bundesbank.
- Giannone, Domenico & Reichlin, Lucrezia & De Mol, Christine, 2006. "Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components?," Working Paper Series 700, European Central Bank.
- Marco Centoni & Gianluca Cubadda, 2015. "Common Feature Analysis of Economic Time Series: An Overview and Recent Developments," CEIS Research Paper 355, Tor Vergata University, CEIS, revised 05 Oct 2015.
- Barbara Brune & Wolfgang Scherrer & Efstathia Bura, 2022. "A state-space approach to time-varying reduced-rank regression," Econometric Reviews, Taylor & Francis Journals, vol. 41(8), pages 895-917, September.
- Hecq, Alain & Palm, Franz C. & Urbain, Jean-Pierre, 2006. "Common cyclical features analysis in VAR models with cointegration," Journal of Econometrics, Elsevier, vol. 132(1), pages 117-141, May.
- Minji Lee & Zhihua Su, 2020. "A Review of Envelope Models," International Statistical Review, International Statistical Institute, vol. 88(3), pages 658-676, December.
- Di Wang & Yao Zheng & Heng Lian & Guodong Li, 2022. "High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1338-1356, September.
- Kun Chen & Hongbo Dong & Kung-Sik Chan, 2013. "Reduced rank regression via adaptive nuclear norm penalization," Biometrika, Biometrika Trust, vol. 100(4), pages 901-920.
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Cited by:
- Alain Hecq & Ivan Ricardo & Ines Wilms, 2024. "Reduced-Rank Matrix Autoregressive Models: A Medium $N$ Approach," Papers 2407.07973, arXiv.org.
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- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- David de Antonio Liedo & Elena Fernández Muñoz, 2010. "Nowcasting Spanish GDP growth in real time: "One and a half months earlier"," Working Papers 1037, Banco de España.
- Giannone, Domenico & Lenza, Michele & Momferatou, Daphne & Onorante, Luca, 2014.
"Short-term inflation projections: A Bayesian vector autoregressive approach,"
International Journal of Forecasting, Elsevier, vol. 30(3), pages 635-644.
- Domenico Giannone & Michèle Lenza & Daphné Momferatu & Luca Onorante, 2010. "Short-term inflation projections: a Bayesian vector autoregressive approach," Working Papers ECARES ECARES 2010-011, ULB -- Universite Libre de Bruxelles.
- Giannone, Domenico & Lenza, Michele & Onorante, Luca & Momferatou, Daphne, 2010. "Short-Term Inflation Projections: a Bayesian Vector Autoregressive approach," CEPR Discussion Papers 7746, C.E.P.R. Discussion Papers.
- Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
- Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009.
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- Gianluca Cubadda & Alain Hecq & Franz C. Palm, 2008. "Studying Co-Movements in Large Multivariate Data Prior to Multivariate Modelling," CEIS Research Paper 125, Tor Vergata University, CEIS, revised 14 Jul 2008.
- Chen, Xiaoshan & Mills, Terence C., 2009. "Evaluating growth cycle synchronisation in the EU," Economic Modelling, Elsevier, vol. 26(2), pages 342-351, March.
- Dennis Cook, R. & Forzani, Liliana, 2023. "On the role of partial least squares in path analysis for the social sciences," Journal of Business Research, Elsevier, vol. 167(C).
- Tomasz Woźniak, 2016. "Bayesian Vector Autoregressions," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(3), pages 365-380, September.
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2023-10-16 (Econometrics)
- NEP-ETS-2023-10-16 (Econometric Time Series)
- NEP-GER-2023-10-16 (German Papers)
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