IDEAS home Printed from https://ideas.repec.org/f/c/pba354.html
   My authors  Follow this author

Matteo Barigozzi

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Matteo Barigozzi & Giuseppe Cavaliere & Graziano Moramarco, 2022. "Factor Network Autoregressions," Papers 2208.02925, arXiv.org, revised Feb 2024.

    Mentioned in:

    1. Factor Network Autoregressions
      by Francis Diebold in No Hesitations on 2022-09-18 22:10:00
  2. Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.

    Mentioned in:

    1. NBER/NSF Time-Series Conference: Retrospect and Prospect
      by Francis Diebold in No Hesitations on 2013-10-25 18:14:00
    2. Network Estimation for Time Series
      by Francis Diebold in No Hesitations on 2013-10-16 16:41:00

Working papers

  1. Matteo Barigozzi & Filippo Pellegrino, 2023. "Multidimensional dynamic factor models," Papers 2301.12499, arXiv.org.

    Cited by:

    1. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised Dec 2023.

  2. Matteo Barigozzi & Giuseppe Cavaliere & Graziano Moramarco, 2022. "Factor Network Autoregressions," Papers 2208.02925, arXiv.org, revised Feb 2024.

    Cited by:

    1. Bastianin, Andrea & Casoli, Chiara & Galeotti, Marzio, 2023. "The connectedness of Energy Transition Metals," FEEM Working Papers 336984, Fondazione Eni Enrico Mattei (FEEM).
    2. Marko Mlikota, 2022. "Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications," Papers 2211.13610, arXiv.org, revised Dec 2023.
    3. Francis X. Diebold & Kamil Yilmaz, 2022. "On the Past, Present, and Future of the Diebold-Yilmaz Approach to Dynamic Network Connectedness," Koç University-TUSIAD Economic Research Forum Working Papers 2207, Koc University-TUSIAD Economic Research Forum.

  3. Matteo Barigozzi & Daniele Massacci, 2022. "Modelling Large Dimensional Datasets with Markov Switching Factor Models," Papers 2210.09828, arXiv.org, revised Dec 2023.

    Cited by:

    1. Matteo Barigozzi, 2022. "On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis," Papers 2211.01921, arXiv.org, revised Jul 2023.

  4. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano, 2019. "Identification of global and local shocks in international financial markets via general dynamic factor models," LSE Research Online Documents on Economics 86932, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Riccardo Borghi & Eric Hillebrand & Jakob Mikkelsen & Giovanni Urga, 2018. "The dynamics of factor loadings in the cross-section of returns," CREATES Research Papers 2018-38, Department of Economics and Business Economics, Aarhus University.
    2. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    3. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    4. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
    5. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2020. "Time-varying general dynamic factor models and the measurement of financial connectedness," LIDAM Reprints ISBA 2020015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Thomas Lux & Duc Thi Luu & Boyan Yanovski, 2020. "An analysis of systemic risk in worldwide economic sentiment indices," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 909-928, November.
    7. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    8. Matteo Barigozzi & Marc Hallin, 2018. "Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals," Papers 1811.10045, arXiv.org, revised Jul 2019.
    9. Niţoi, Mihai & Pochea, Maria Miruna, 2022. "The nexus between bank connectedness and investors’ sentiment," Finance Research Letters, Elsevier, vol. 44(C).

  5. Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models," Papers 1910.09841, arXiv.org.

    Cited by:

    1. Matteo Barigozzi & Daniele Massacci, 2022. "Modelling Large Dimensional Datasets with Markov Switching Factor Models," Papers 2210.09828, arXiv.org, revised Dec 2023.
    2. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
    3. Hie Joo Ahn & Matteo Luciani, 2021. "Relative prices and pure inflation since the mid-1990s," Finance and Economics Discussion Series 2021-069, Board of Governors of the Federal Reserve System (U.S.).
    4. Fresoli, Diego & Poncela, Pilar & Ruiz, Esther, 2023. "Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models," Economics Letters, Elsevier, vol. 230(C).
    5. Luke Hartigan & Michelle Wright, 2021. "Financial Conditions and Downside Risk to Economic Activity in Australia," RBA Research Discussion Papers rdp2021-03, Reserve Bank of Australia.
    6. Poncela, Pilar & Ruiz, Esther, 2020. "A comment on the dynamic factor model with dynamic factors," Economics Discussion Papers 2020-7, Kiel Institute for the World Economy (IfW Kiel).
    7. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Sep 2023.
    8. Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023. "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, vol. 26(C), pages 3-16.
    9. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    10. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised Dec 2023.
    11. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
    12. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    13. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    14. Matteo Barigozzi & Angelo Cuzzola & Marco Grazzi & Daniele Moschella, 2021. "Factoring in the micro: a transaction-level dynamic factor approach to the decomposition of export volatility," LEM Papers Series 2021/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

  6. Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Papers 1910.03821, arXiv.org, revised Feb 2022.

    Cited by:

    1. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023. "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers 1385, Board of Governors of the Federal Reserve System (U.S.).
    2. Hie Joo Ahn & Matteo Luciani, 2021. "Relative prices and pure inflation since the mid-1990s," Finance and Economics Discussion Series 2021-069, Board of Governors of the Federal Reserve System (U.S.).
    3. Linton, O. B. & Tang, H. & Wu, J., 2022. "A Structural Dynamic Factor Model for Daily Global Stock Market Returns," Cambridge Working Papers in Economics 2237, Faculty of Economics, University of Cambridge.
    4. Poncela, Pilar & Ruiz, Esther, 2020. "A comment on the dynamic factor model with dynamic factors," Economics Discussion Papers 2020-7, Kiel Institute for the World Economy (IfW Kiel).
    5. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    6. Filippo Pellegrino, 2021. "Factor-augmented tree ensembles," Papers 2111.14000, arXiv.org, revised Jun 2023.
    7. Linton, O. B. & Tang, H. & Wu, J., 2022. "A Structural Dynamic Factor Model for Daily Global Stock Market Returns," Janeway Institute Working Papers camjip:2214, Faculty of Economics, University of Cambridge.

  7. Barigozzi, M. & Hallin, M. & Soccorsi, S. & Von Sachs, R., 2019. "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness," LIDAM Discussion Papers ISBA 2019024, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    2. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    3. Vidal-Llana, Xenxo & Uribe, Jorge M. & Guillén, Montserrat, 2023. "European stock market volatility connectedness: The role of country and sector membership," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    4. Matteo Barigozzi & Marc Hallin & Matteo Luciani & Paolo Zaffaroni, 2021. "Inferential Theory for Generalized Dynamic Factor Models," Working Papers ECARES 2021-20, ULB -- Universite Libre de Bruxelles.
    5. Jonas Krampe & Luca Margaritella, 2024. "Global bank network connectedness revisited: What is common, idiosyncratic and when?," Papers 2402.02482, arXiv.org.
    6. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    7. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    8. Katerina Rigana & Ernst-Jan Camiel Wit & Samantha Cook, 2021. "Using Network-based Causal Inference to Detect the Sources of Contagion in the Currency Market," Papers 2112.13127, arXiv.org.
    9. Rajae Azrak & Guy Mélard, 2022. "Autoregressive Models with Time-Dependent Coefficients—A Comparison between Several Approaches," Stats, MDPI, vol. 5(3), pages 1-21, August.
    10. Marc Hallin, 2022. "Manfred Deistler and the General Dynamic Factor Model Approach to the Analysis of High-Dimensional Time Series," Working Papers ECARES 2022-30, ULB -- Universite Libre de Bruxelles.
    11. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    12. Marc Hallin, 2022. "Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series," Econometrics, MDPI, vol. 10(4), pages 1-9, December.
    13. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    14. Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting Growth-at-Risk of the United States: Housing Price versus Housing Sentiment or Attention," Working Papers 202401, University of Pretoria, Department of Economics.

  8. Barigozzi, Matteo, 2018. "On the stability of euro area money demand and its implications for monetary policy," LSE Research Online Documents on Economics 87283, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Stephen M. Miller & Luis F. Martins & Rangan Gupta, 2014. "A Time-Varying Approach of the US Welfare Cost of Inflation," Working papers 2014-11, University of Connecticut, Department of Economics.
    2. Raouf Boucekkine & Mohammed Laksaci & Mohamed Touati-Tliba, 2021. "Long-run stability of money demand and monetary policy: the case of Algeria," Working Papers halshs-03120699, HAL.
    3. Jung, Alexander & Carcel Villanova, Hector, 2020. "The empirical properties of euro area M3, 1980-2017," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 37-49.
    4. Conti, Antonio M., 2021. "Resurrecting the Phillips Curve in Low-Inflation Times," Economic Modelling, Elsevier, vol. 96(C), pages 172-195.

  9. Matteo Barigozzi & Lorenzo Trapani, 2018. "Determining the dimension of factor structures in non-stationary large datasets," Papers 1806.03647, arXiv.org.

    Cited by:

    1. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    2. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.

  10. Campi, Mercedes & Dueñas, Marco & Barigozzi, Matteo & Fagiolo, Giorgio, 2018. "Intellectual property rights, imitation, and development. The effect on cross-border mergers and acquisitions," LSE Research Online Documents on Economics 90203, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Mercedes Campi & Alessandro Nuvolari, 2020. "Intellectual property rights and agricultural development: evidence from a worldwide index of IPRS in agriculture (1961-2018)," Documentos de trabajo del Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET) 2020-51, Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET).
    2. Yan Yan & Jiatao Li & Jingjing Zhang, 2022. "Protecting intellectual property in foreign subsidiaries: An internal network defense perspective," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 53(9), pages 1924-1944, December.
    3. Gnangnon, Sèna Kimm, 2023. "Aid for Trade flows, Patent Rights Protection and Total Factor Productivity," EconStor Preprints 274650, ZBW - Leibniz Information Centre for Economics.
    4. Bazel-Shoham, Ofra & Lee, Sang Mook & Ahammad, Mohammad Faisal & Tarba, Shlomo Y. & Alon, Ilan, 2023. "IP protection and ownership in cross-border acquisitions," International Business Review, Elsevier, vol. 32(3).

  11. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," LSE Research Online Documents on Economics 88110, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Matteo Barigozzi & Daniele Massacci, 2022. "Modelling Large Dimensional Datasets with Markov Switching Factor Models," Papers 2210.09828, arXiv.org, revised Dec 2023.
    2. Duan, Jiangtao & Bai, Jushan & Han, Xu, 2023. "Quasi-maximum likelihood estimation of break point in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 233(1), pages 209-236.
    3. Anastasiou, Andreas & Cribben, Ivor & Fryzlewicz, Piotr, 2022. "Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity," LSE Research Online Documents on Economics 112148, London School of Economics and Political Science, LSE Library.
    4. Zhang, Lyuou & Zhou, Wen & Wang, Haonan, 2021. "A semiparametric latent factor model for large scale temporal data with heteroscedasticity," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    5. Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    6. Matteo Barigozzi, 2022. "On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis," Papers 2211.01921, arXiv.org, revised Jul 2023.
    7. Bai, Jushan & Duan, Jiangtao & Han, Xu, 2024. "The likelihood ratio test for structural changes in factor models," Journal of Econometrics, Elsevier, vol. 238(2).
    8. Christis Katsouris, 2023. "Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models," Papers 2308.13915, arXiv.org.
    9. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," PSE Working Papers halshs-02235543, HAL.
    10. Matteo Barigozzi & Lorenzo Trapani, 2018. "Sequential testing for structural stability in approximate factor models," Discussion Papers 18/04, University of Nottingham, Granger Centre for Time Series Econometrics.
    11. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2020. "Time-varying general dynamic factor models and the measurement of financial connectedness," LIDAM Reprints ISBA 2020015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Fernandez, Julian, 2020. "Exchange Rate Uncertainty and the Interest Rate Parity," MPRA Paper 116010, University Library of Munich, Germany, revised 2022.
    13. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    14. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," LSE Research Online Documents on Economics 88110, London School of Economics and Political Science, LSE Library.
    15. Qing Yang & Yu-Ning Li & Yi Zhang, 2020. "Change point detection for nonparametric regression under strongly mixing process," Statistical Papers, Springer, vol. 61(4), pages 1465-1506, August.
    16. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    17. Thomas Despois & Catherine Doz, 2021. "Identifying and interpreting the factors in factor models via sparsity: Different approaches," Working Papers halshs-02235543, HAL.
    18. Wang, Lu & Wu, Jianhong, 2022. "Estimation of high-dimensional factor models with multiple structural changes," Economic Modelling, Elsevier, vol. 108(C).
    19. Mengjia Yu & Xiaohui Chen, 2021. "Finite sample change point inference and identification for high‐dimensional mean vectors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 247-270, April.
    20. Xialu Liu & Elynn Y. Chen, 2022. "Identification and estimation of threshold matrix‐variate factor models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1383-1417, September.
    21. Cui, Junfeng & Wang, Guanghui & Zou, Changliang & Wang, Zhaojun, 2023. "Change-point testing for parallel data sets with FDR control," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    22. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    23. Ma, Chenchen & Tu, Yundong, 2023. "Shrinkage estimation of multiple threshold factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1876-1892.
    24. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    25. Wang, Hanchao & Peng, Bin & Li, Degui & Leng, Chenlei, 2021. "Nonparametric estimation of large covariance matrices with conditional sparsity," Journal of Econometrics, Elsevier, vol. 223(1), pages 53-72.
    26. Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Papers 1910.03821, arXiv.org, revised Feb 2022.
    27. Ma, Chenchen & Tu, Yundong, 2023. "Group fused Lasso for large factor models with multiple structural breaks," Journal of Econometrics, Elsevier, vol. 233(1), pages 132-154.

  12. Matteo Barigozzi & Matteo Luciani, 2018. "Do National Account Statistics Underestimate US Real Output Growth?," FEDS Notes 2018-01-09-1, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Kenneth E. Poole & Allison Forbes & Nichelle Williams, 2023. "Applied Regional Economic Research Can Improve Development Strategies and Drive Better Outcomes," Economic Development Quarterly, , vol. 37(1), pages 85-95, February.
    2. Zheng Liu & Mark M. Spiegel & Eric Tallman, 2018. "Is GDP Overstating Economic Activity?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.

  13. Matteo Barigozzi & Marc Hallin, 2018. "Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals," Papers 1811.10045, arXiv.org, revised Jul 2019.

    Cited by:

    1. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
    2. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    3. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    4. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    5. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Sep 2023.
    6. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised Dec 2023.
    7. Gunawan, David & Kohn, Robert & Nott, David, 2021. "Variational Bayes approximation of factor stochastic volatility models," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1355-1375.
    8. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    9. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    10. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.
    11. Marc Hallin, 2022. "Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series," Econometrics, MDPI, vol. 10(4), pages 1-9, December.

  14. Barigozzi, Matteo & Hallin, Marc, 2017. "A network analysis of the volatility of high-dimensionalfinancial series," LSE Research Online Documents on Economics 67456, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    2. Hai-Chuan Xu & Fredj Jawadi & Jie Zhou & Wei-Xing Zhou, 2023. "Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework," Empirical Economics, Springer, vol. 65(1), pages 93-110, July.
    3. Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.
    4. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    5. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    6. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.
    7. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    8. Pagnottoni, Paolo, 2023. "Superhighways and roads of multivariate time series shock transmission: Application to cryptocurrency, carbon emission and energy prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    9. Christian Gross & Pierre L. Siklos, 2020. "Analyzing credit risk transmission to the nonfinancial sector in Europe: A network approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 61-81, January.
    10. Krampe, J. & Paparoditis, E. & Trenkler, C., 2023. "Structural inference in sparse high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 234(1), pages 276-300.
    11. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2020. "Time-varying general dynamic factor models and the measurement of financial connectedness," LIDAM Reprints ISBA 2020015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," LSE Research Online Documents on Economics 88110, London School of Economics and Political Science, LSE Library.
    13. Herculano, Miguel C. & Lütkebohmert, Eva, 2023. "Investor sentiment and global economic conditions," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 134-152.
    14. Jonas Krampe & Luca Margaritella, 2024. "Global bank network connectedness revisited: What is common, idiosyncratic and when?," Papers 2402.02482, arXiv.org.
    15. Ahelegbey, Daniel Felix & Giudici, Paolo & Hashem, Shatha Qamhieh, 2021. "Network VAR models to measure financial contagion," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    16. Geraci, Marco Valerio & Gnabo, Jean-Yves, 2018. "Measuring Interconnectedness between Financial Institutions with Bayesian Time-Varying Vector Autoregressions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(3), pages 1371-1390, June.
    17. Katerina Rigana & Ernst-Jan Camiel Wit & Samantha Cook, 2021. "Using Network-based Causal Inference to Detect the Sources of Contagion in the Currency Market," Papers 2112.13127, arXiv.org.
    18. Kumar, Sudarshan & Bansal, Avijit & Chakrabarti, Anindya S., 2019. "Ripples on financial networks," IIMA Working Papers WP 2019-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    19. Etesami, Jalal & Habibnia, Ali & Kiyavash, Negar, 2017. "Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity," LSE Research Online Documents on Economics 70769, London School of Economics and Political Science, LSE Library.
    20. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    21. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
    22. Yaya Su & Zhehao Huang & Benjamin M. Drakeford, 2019. "Monetary Policy, Industry Heterogeneity and Systemic Risk—Based on a High Dimensional Network Analysis," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    23. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    24. Christis Katsouris, 2023. "Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models," Papers 2305.11282, arXiv.org, revised Jul 2023.
    25. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.
    26. Niţoi, Mihai & Pochea, Maria Miruna, 2022. "The nexus between bank connectedness and investors’ sentiment," Finance Research Letters, Elsevier, vol. 44(C).
    27. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    28. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.
    29. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
    30. Baek, Changryong & Gates, Katheleen M. & Leinwand, Benjamin & Pipiras, Vladas, 2021. "Two sample tests for high-dimensional autocovariances," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

  15. Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2020.

    Cited by:

    1. Matteo Barigozzi & Daniele Massacci, 2022. "Modelling Large Dimensional Datasets with Markov Switching Factor Models," Papers 2210.09828, arXiv.org, revised Dec 2023.
    2. Xin-Bing Kong & Yong-Xin Liu & Long Yu & Peng Zhao, 2022. "Matrix Quantile Factor Model," Papers 2208.08693, arXiv.org, revised May 2023.
    3. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    4. Lorenzo Trapani & Emily Whitehouse, 2020. "Sequential monitoring for cointegrating regressions," Papers 2003.12182, arXiv.org.

  16. Dueñas, Marco & Mastrandrea, Rossana & Barigozzi, Matteo & Fagiolo, Giorgio, 2017. "Spatio-temporal patterns of the international merger and acquisition network," LSE Research Online Documents on Economics 84092, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. A. Baronchelli & T.E. Uberti, 2018. "Exports and FDI: comparing networks in the new millennium," Working Paper CRENoS 201813, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    2. Brózda-Wilamek Dominika, 2023. "The global cross-border mergers and acquisitions network between 1990 and 2021," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 59(4), pages 333-348, December.
    3. Mercedes Campi & Marco Dueñas & Matteo Barigozzi & Giorgio Fagiolo, 2019. "Intellectual property rights, imitation, and development. The effect on cross-border mergers and acquisitions," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 28(2), pages 230-256, February.

  17. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.

  18. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Alessi, Lucia & Kerssenfischer, Mark, 2016. "The response of asset prices to monetary policy shocks: stronger than thought," Working Paper Series 1967, European Central Bank.
    2. Cristina Conflitti and Matteo Luciani, 2019. "Oil Price Pass-through into Core Inflation," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    3. Kose, M. Ayhan & Ha, Jongrim & Ohnsorge, Franziska, 2021. "One-Stop Source: A Global Database of Inflation," CEPR Discussion Papers 16327, C.E.P.R. Discussion Papers.
    4. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    5. Panagiotidis, Theodore & Printzis, Panagiotis, 2020. "What is the investment loss due to uncertainty?," Global Finance Journal, Elsevier, vol. 45(C).
    6. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).
    7. Panagiotidis, Theodore & Printzis, Panagiotis, 2021. "Investment and uncertainty: Are large firms different from small ones?," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 302-317.
    8. Hsiang-Hsi Liu & Chien-Kuo Tseng, 2022. "Common Components in Co-integrated System and Its Estimation and Application: Evidence from Five Stock Markets in Asia-Pacific Chinese Region," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 101-121.
    9. Francesca Di Iorio & Stefano Fachin, 2017. "Evaluating Restricted Common Factor models for non-stationary data," DSS Empirical Economics and Econometrics Working Papers Series 2017/2, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    10. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    11. Maxime Leroux & Rachidi Kotchoni & Dalibor Stevanovic, 2017. "Forecasting economic activity in data-rich environment," Working Papers hal-04141668, HAL.
    12. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    13. Martina Hengge & Seton Leonard, 2017. "Factor Models for Non-Stationary Series: Estimates of Monthly U.S. GDP," IHEID Working Papers 13-2017, Economics Section, The Graduate Institute of International Studies.
    14. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    15. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    16. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Dynamic Factor Models, Cointegration, and Error Correction Mechanisms," Finance and Economics Discussion Series 2016-018, Board of Governors of the Federal Reserve System (U.S.).

  19. Barigozzi, Matteo & Moneta, Alessio, 2016. "Identifying the independent sources of consumption variation," LSE Research Online Documents on Economics 60979, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Tommaso Ciarli & Andre Lorentz & Marco Valente & Maria Savona, 2017. "Structural Changes and Growth Regime," Working Papers of BETA 2017-19, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    2. Christophe Faugère, 2021. "Connectalism: A new paradigm for human choice," Systems Research and Behavioral Science, Wiley Blackwell, vol. 38(6), pages 866-889, November.
    3. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised Dec 2023.
    4. Liqiong Chen & Antonio F. Galvao & Suyong Song, 2021. "Quantile Regression with Generated Regressors," Econometrics, MDPI, vol. 9(2), pages 1-35, April.

  20. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Papers 1510.05118, arXiv.org, revised Jul 2016.

    Cited by:

    1. Affinito, Massimiliano & Franco Pozzolo, Alberto, 2017. "The interbank network across the global financial crisis: Evidence from Italy," Journal of Banking & Finance, Elsevier, vol. 80(C), pages 90-107.
    2. Matteo Barigozzi & Marc Hallin, 2018. "Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals," Papers 1811.10045, arXiv.org, revised Jul 2019.

  21. Matteo Barigozzi & Marc Hallin, 2015. "Generalized Dynamic Factor Models and Volatilities: Estimation and Forecasting," Working Papers ECARES ECARES 2015-22, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    2. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    3. Valentina Aprigliano & Lorenzo Bencivelli, 2013. "Ita-coin: a new coincident indicator for the Italian economy," Temi di discussione (Economic working papers) 935, Bank of Italy, Economic Research and International Relations Area.
    4. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    5. Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Bontempi, Maria Elena & Mammi, Irene, 2012. "A strategy to reduce the count of moment conditions in panel data GMM," MPRA Paper 40720, University Library of Munich, Germany.
    7. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
    8. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    9. Gaoke Liao & Peng Hou & Xiaoyan Shen & Khaldoon Albitar, 2021. "The impact of economic policy uncertainty on stock returns: The role of corporate environmental responsibility engagement," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4386-4392, July.
    10. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    11. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    12. Duc Thi Luu, 2022. "Portfolio Correlations in the Bank-Firm Credit Market of Japan," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 529-569, August.
    13. Kumar, Sudarshan & Bansal, Avijit & Chakrabarti, Anindya S., 2019. "Ripples on financial networks," IIMA Working Papers WP 2019-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    14. Jari Miettinen & Markus Matilainen & Klaus Nordhausen & Sara Taskinen, 2020. "Extracting Conditionally Heteroskedastic Components using Independent Component Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 293-311, March.
    15. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
    16. Ma, Yan-Ran & Zhang, Dayong & Ji, Qiang & Pan, Jiaofeng, 2019. "Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?," Energy Economics, Elsevier, vol. 81(C), pages 536-544.
    17. Kris Boudt & Dries Cornilly & Tim Verdonck, 2019. "Nearest Comoment Estimation With Unobserved Factors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 19/970, Ghent University, Faculty of Economics and Business Administration.
    18. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
    19. Marc Hallin, 2022. "Manfred Deistler and the General Dynamic Factor Model Approach to the Analysis of High-Dimensional Time Series," Working Papers ECARES 2022-30, ULB -- Universite Libre de Bruxelles.
    20. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.
    21. Marc Hallin, 2022. "Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series," Econometrics, MDPI, vol. 10(4), pages 1-9, December.
    22. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2020. "Research on China's financial systemic risk contagion under jump and heavy-tailed risk," International Review of Financial Analysis, Elsevier, vol. 72(C).
    23. Matteo Barigozzi & Marc Hallin, 2018. "Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals," Papers 1811.10045, arXiv.org, revised Jul 2019.
    24. Kim, Donggyu & Fan, Jianqing, 2019. "Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction," Journal of Econometrics, Elsevier, vol. 208(2), pages 395-417.
    25. Cipollini, Fabrizio & Gallo, Giampiero M., 2019. "Modeling Euro STOXX 50 volatility with common and market-specific components," Econometrics and Statistics, Elsevier, vol. 11(C), pages 22-42.
    26. Tingting Lan & Liuguo Shao & Hua Zhang & Caijun Yuan, 2023. "The impact of pandemic on dynamic volatility spillover network of international stock markets," Empirical Economics, Springer, vol. 65(5), pages 2115-2144, November.
    27. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.
    28. Krupskii, Pavel & Joe, Harry, 2020. "Flexible copula models with dynamic dependence and application to financial data," Econometrics and Statistics, Elsevier, vol. 16(C), pages 148-167.
    29. Lübbers, Johannes & Posch, Peter N., 2016. "Commodities' common factor: An empirical assessment of the markets' drivers," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 28-40.

  22. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2014. "Dynamic Factor Models, Cointegration and Error Correction Mechanisms," Working Papers ECARES ECARES 2014-14, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    2. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).
    3. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).
    4. Francisco Corona & Pilar Poncela & Esther Ruiz, 2020. "Estimating Non-stationary Common Factors: Implications for Risk Sharing," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 37-60, January.
    5. Martina Hengge & Seton Leonard, 2017. "Factor Models for Non-Stationary Series: Estimates of Monthly U.S. GDP," IHEID Working Papers 13-2017, Economics Section, The Graduate Institute of International Studies.
    6. Carlo A. Favero & Alessandro Melone, 2019. "Asset Pricing vs Asset Expected Returning in Factor Models," Working Papers 651, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).

  23. Matteo Barigozzi & Marc Hallin, 2014. "Generalized Dynamic Factor Models and Volatilities. Recovering the Market Volatility Shocks," Working Papers ECARES ECARES 2014-52, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
    2. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    3. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    4. Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.
    5. Pourkhanali, Armin & Tafakori, Laleh & Bee, Marco, 2023. "Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    7. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    8. Liang Chen & Juan Jose Dolado & Jesus Gonzalo, 2019. "Quantile Factor Models," Papers 1911.02173, arXiv.org, revised Sep 2020.
    9. Bouri, Elie & Gabauer, David & Gupta, Rangan & Tiwari, Aviral Kumar, 2021. "Volatility connectedness of major cryptocurrencies: The role of investor happiness," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    10. Ma, Yan-Ran & Ji, Qiang & Wu, Fei & Pan, Jiaofeng, 2021. "Financialization, idiosyncratic information and commodity co-movements," Energy Economics, Elsevier, vol. 94(C).
    11. Trucíos Maza, Carlos César & Hotta, Luiz Koodi & Pereira, Pedro L. Valls, 2018. "On the robustness of the principal volatility components," Textos para discussão 474, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    12. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    13. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
    14. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    15. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    16. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    17. Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
    18. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    19. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    20. Jari Miettinen & Markus Matilainen & Klaus Nordhausen & Sara Taskinen, 2020. "Extracting Conditionally Heteroskedastic Components using Independent Component Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 293-311, March.
    21. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
    22. Ma, Yan-Ran & Zhang, Dayong & Ji, Qiang & Pan, Jiaofeng, 2019. "Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?," Energy Economics, Elsevier, vol. 81(C), pages 536-544.
    23. Kris Boudt & Dries Cornilly & Tim Verdonck, 2019. "Nearest Comoment Estimation With Unobserved Factors," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 19/970, Ghent University, Faculty of Economics and Business Administration.
    24. Marc Hallin, 2022. "Manfred Deistler and the General Dynamic Factor Model Approach to the Analysis of High-Dimensional Time Series," Working Papers ECARES 2022-30, ULB -- Universite Libre de Bruxelles.
    25. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021. "Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning," Working Papers 202111, University of Pretoria, Department of Economics.
    26. Marc Hallin, 2022. "Manfred Deistler and the General-Dynamic-Factor-Model Approach to the Statistical Analysis of High-Dimensional Time Series," Econometrics, MDPI, vol. 10(4), pages 1-9, December.
    27. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhang, Wei, 2020. "Research on China's financial systemic risk contagion under jump and heavy-tailed risk," International Review of Financial Analysis, Elsevier, vol. 72(C).
    28. Matteo Barigozzi & Marc Hallin, 2018. "Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals," Papers 1811.10045, arXiv.org, revised Jul 2019.
    29. Kim, Donggyu & Fan, Jianqing, 2019. "Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction," Journal of Econometrics, Elsevier, vol. 208(2), pages 395-417.
    30. Karmous, Aida & Boubaker, Heni & Belkacem, Lotfi, 2019. "A dynamic factor model with stylized facts to forecast volatility for an optimal portfolio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    31. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    32. Cipollini, Fabrizio & Gallo, Giampiero M., 2019. "Modeling Euro STOXX 50 volatility with common and market-specific components," Econometrics and Statistics, Elsevier, vol. 11(C), pages 22-42.
    33. Yunus Emre Ergemen, 2022. "Parametric Estimation of Long Memory in Factor Models," CREATES Research Papers 2022-10, Department of Economics and Business Economics, Aarhus University.
    34. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2021. "Next Generation Models for Portfolio Risk Management: An Approach Using Financial Big Data," Papers 2102.12783, arXiv.org, revised Feb 2022.
    35. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2022. "Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1049-1064, September.
    36. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.
    37. Lübbers, Johannes & Posch, Peter N., 2016. "Commodities' common factor: An empirical assessment of the markets' drivers," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 28-40.

  24. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2014. "Disentangling Systematic and Idiosyncratic Dynamics in Panels of Volatility Measures," Econometrics Working Papers Archive 2014_02, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", revised Feb 2014.

    Cited by:

    1. Zhang, Xingmin & Zhang, Shuai & Lu, Liping, 2022. "The banking instability and climate change: Evidence from China," Energy Economics, Elsevier, vol. 106(C).
    2. Driton Kuçi, 2015. "Contemporary Models of Organization of Power and the Macedonian Model of Organization of Power," European Journal of Interdisciplinary Studies Articles, Revistia Research and Publishing, vol. 1, September.
    3. Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.
    4. Fabrizio Cipollini & Giampiero M. Gallo & Edoardo Otranto, 2019. "Realized Volatility Forecasting: Robustness to Measurement Errors," Econometrics Working Papers Archive 2019_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    5. Jozef Barun'ik & Tobias Kley, 2015. "Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables," Papers 1510.06946, arXiv.org, revised Dec 2018.
    6. Francisco Blasques & Enzo D'Innocenzo & Siem Jan Koopman, 2021. "Common and Idiosyncratic Conditional Volatility Factors: Theory and Empirical Evidence," Tinbergen Institute Discussion Papers 21-057/III, Tinbergen Institute.
    7. Le, Trung Hai & Do, Hung Xuan & Nguyen, Duc Khuong & Sensoy, Ahmet, 2021. "Covid-19 pandemic and tail-dependency networks of financial assets," Finance Research Letters, Elsevier, vol. 38(C).
    8. Bernard Herskovic & Bryan Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2020. "Firm Volatility in Granular Networks," Journal of Political Economy, University of Chicago Press, vol. 128(11), pages 4097-4162.
    9. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
    10. Marko Mlikota, 2022. "Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications," Papers 2211.13610, arXiv.org, revised Dec 2023.
    11. Giampiero M. Gallo & Edoardo Otranto, 2018. "Combining sharp and smooth transitions in volatility dynamics: a fuzzy regime approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(3), pages 549-573, April.
    12. Matteo Barigozzi & Marc Hallin, 2016. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-60, February.
    13. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    14. Amendola, A. & Candila, V. & Cipollini, F. & Gallo, G.M., 2024. "Doubly multiplicative error models with long- and short-run components," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    15. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    16. Duc Thi Luu, 2022. "Portfolio Correlations in the Bank-Firm Credit Market of Japan," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 529-569, August.
    17. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
    18. Fabrizio Cipollini & Giampiero M. Gallo, 2021. "Multiplicative Error Models: 20 years on," Papers 2107.05923, arXiv.org.
    19. Paul G. Egan & Anthony J. Leddin, 2016. "Examining Monetary Policy Transmission in the People's Republic of China–Structural Change Models with a Monetary Policy Index," Asian Development Review, MIT Press, vol. 33(1), pages 74-110, March.
    20. KALNINA, Ilze & TEWOU, Kokouvi, 2015. "Cross-sectional dependence in idiosyncratic volatility," Cahiers de recherche 2015-04, Universite de Montreal, Departement de sciences economiques.
    21. Cipollini, Fabrizio & Gallo, Giampiero M., 2019. "Modeling Euro STOXX 50 volatility with common and market-specific components," Econometrics and Statistics, Elsevier, vol. 11(C), pages 22-42.
    22. Philip L. H. Yu & W. K. Li & F. C. Ng, 2017. "The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 513-527, October.
    23. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2017. "Identification of Global and National Shocks in International Financial Markets via General Dynamic Factor Models," Working Papers ECARES ECARES 2017-10, ULB -- Universite Libre de Bruxelles.

  25. Matteo Barigozzi & Christian Brownlees, 2013. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.

    Cited by:

    1. Xu, Qifa & Li, Mengting & Jiang, Cuixia, 2021. "Network-augmented time-varying parametric portfolio selection: Evidence from the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    2. René Böheim & Philipp Stöllinger, 2020. "Decomposition of the Gender Wage Gap using the LASSO Estimator," Economics working papers 2020-03, Department of Economics, Johannes Kepler University Linz, Austria.
    3. Carlo Campajola & Fabrizio Lillo & Daniele Tantari, 2019. "Unveiling the relation between herding and liquidity with trader lead-lag networks," Papers 1909.10807, arXiv.org, revised Mar 2020.
    4. P. Giudici & A. Spelta, 2016. "Graphical Network Models for International Financial Flows," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 128-138, January.
    5. Ariana Paola Cortés Ángel & Mustafa Hakan Eratalay, 2022. "Deep diving into the S&P Europe 350 index network and its reaction to COVID-19," Journal of Computational Social Science, Springer, vol. 5(2), pages 1343-1408, November.
    6. Zhang, Xingmin & Zhang, Shuai & Lu, Liping, 2022. "The banking instability and climate change: Evidence from China," Energy Economics, Elsevier, vol. 106(C).
    7. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    8. Paolo Giudici & Laura Parisi, 2016. "Bail in or Bail out? The Atlante example from a systemic risk perspective," DEM Working Papers Series 124, University of Pavia, Department of Economics and Management.
    9. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    10. Hai-Chuan Xu & Fredj Jawadi & Jie Zhou & Wei-Xing Zhou, 2023. "Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework," Empirical Economics, Springer, vol. 65(1), pages 93-110, July.
    11. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2015. "Estimating Global Bank Network Connectedness," PIER Working Paper Archive 15-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Jul 2015.
    12. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    13. Bastianin, Andrea & Casoli, Chiara & Galeotti, Marzio, 2023. "The connectedness of Energy Transition Metals," FEEM Working Papers 336984, Fondazione Eni Enrico Mattei (FEEM).
    14. 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.
    15. Paolo Giudici & Laura Parisi, 2016. "CoRisk: measuring systemic risk through default probability contagion," DEM Working Papers Series 116, University of Pavia, Department of Economics and Management.
    16. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.
    17. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    18. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    19. Andrieş, Alin Marius & Ongena, Steven & Sprincean, Nicu & Tunaru, Radu, 2022. "Risk spillovers and interconnectedness between systemically important institutions," Journal of Financial Stability, Elsevier, vol. 58(C).
    20. Hué, Sullivan & Lucotte, Yannick & Tokpavi, Sessi, 2019. "Measuring network systemic risk contributions: A leave-one-out approach," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 86-114.
    21. Mustafa Hakan Eratalay & Ariana Paola Cortés à ngel, 2022. "The Impact Of Esg Ratings On The Systemic Risk Of European Blue-Chip Firms," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 139, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    22. Timo Bettendorf & Reinhold Heinlein, 2023. "Connectedness between G10 currencies: Searching for the causal structure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3938-3959, October.
    23. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    24. Gabriele Fiorentini & Enrique Sentana, 2020. "Discrete Mixtures of Normals Pseudo Maximum Likelihood Estimators of Structural Vector Autoregressions," Working Papers wp2020_2023, CEMFI.
    25. Liao, Yin & Pan, Zheyao, 2022. "Extreme risk connectedness among global major financial institutions: Links to globalization and emerging market fear," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    26. Abbassi, Puriya & Brownlees, Christian & Hans, Christina & Podlich, Natalia, 2016. "Credit risk interconnectedness: What does the market really know?," Discussion Papers 09/2016, Deutsche Bundesbank.
    27. Kamil Yilmaz, 2018. "Bank Volatility Connectedness in South East Asia," Koç University-TUSIAD Economic Research Forum Working Papers 1807, Koc University-TUSIAD Economic Research Forum.
    28. Mikhail Stolbov & Daniil Parfenov, 2023. "Credit risk linkages in the international banking network, 2000–2019," Risk Management, Palgrave Macmillan, vol. 25(3), pages 1-38, September.
    29. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring Network Systemic Risk Contributions: A Leave-one-out Approach," LEO Working Papers / DR LEO 2608, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    30. Ben R. Craig & Martin Saldias Zambrana, 2016. "Spatial Dependence and Data-Driven Networks of International Banks," Working Papers (Old Series) 1627, Federal Reserve Bank of Cleveland.
    31. Vidal-Llana, Xenxo & Uribe, Jorge M. & Guillén, Montserrat, 2023. "European stock market volatility connectedness: The role of country and sector membership," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    32. Dahlqvist, Carl-Henrik & Gnabo, Jean-Yves, 2018. "Effective network inference through multivariate information transfer estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 376-394.
    33. Marcelo C. Medeiros & Eduardo F. Mendes, 2015. "l1-Regularization of High-Dimensional Time-Series Models with Flexible Innovations," Textos para discussão 636, Department of Economics PUC-Rio (Brazil).
    34. Chuliá, Helena & Muñoz-Mendoza, Jorge A. & Uribe, Jorge M., 2023. "Energy firms in emerging markets: Systemic risk and diversification opportunities," Emerging Markets Review, Elsevier, vol. 56(C).
    35. I�aki Aldasoro & Ignazio Angeloni, 2015. "Input-output-based measures of systemic importance," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 589-606, April.
    36. Ahelegbey, Daniel Felix & Giudici, Paolo, 2019. "Tree Networks to Assess Financial Contagion," MPRA Paper 92632, University Library of Munich, Germany.
    37. Krampe, J. & Paparoditis, E. & Trenkler, C., 2023. "Structural inference in sparse high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 234(1), pages 276-300.
    38. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2020. "Time-varying general dynamic factor models and the measurement of financial connectedness," LIDAM Reprints ISBA 2020015, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    39. Ivan Alves & Stijn Ferrari & Pietro Franchini & Jean-Cyprien Heam & Pavol Jurca & Sam Langfield & Sebastiano Laviola & Franka Liedorp & Antonio Sánchez & Santiago Tavolaro & Guillaume Vuillemey, 2013. "The structure and resilience of the European interbank market," ESRB Occasional Paper Series 03, European Systemic Risk Board.
    40. Daniel Felix Ahelegbey & Paolo Giudici, 2020. "Market Risk, Connectedness and Turbulence: A Comparison of 21st Century Financial Crises," DEM Working Papers Series 188, University of Pavia, Department of Economics and Management.
    41. Foglia, Matteo & Addi, Abdelhamid & Wang, Gang-Jin & Angelini, Eliana, 2022. "Bearish Vs Bullish risk network: A Eurozone financial system analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    42. Bettendorf, Timo & Heinlein, Reinhold, 2019. "Connectedness between G10 currencies: Searching for the causal structure," Discussion Papers 06/2019, Deutsche Bundesbank.
    43. Brownlees, Christian & Hans, Christina & Nualart, Eulalia, 2021. "Bank credit risk networks: Evidence from the Eurozone," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 585-599.
    44. Daniela Scidá, 2023. "Structural VAR and financial networks: A minimum distance approach to spatial modeling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 49-68, January.
    45. Ahelegbey, Daniel Felix & Giudici, Paolo, 2022. "NetVIX — A network volatility index of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    46. Anufriev, Mikhail & Panchenko, Valentyn, 2015. "Connecting the dots: Econometric methods for uncovering networks with an application to the Australian financial institutions," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 241-255.
    47. Jonas Krampe & Luca Margaritella, 2024. "Global bank network connectedness revisited: What is common, idiosyncratic and when?," Papers 2402.02482, arXiv.org.
    48. Christian Brownlees & Eulàlia Nualart & Yucheng Sun, 2018. "Realized networks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 986-1006, November.
    49. M. Hakan Eratalay & Evgenii Vladimirov, 2017. "Mapping the Stocks in MICEX: Who Is Central in Moscow Stock Exchange?," EUSP Department of Economics Working Paper Series 2017/01, European University at St. Petersburg, Department of Economics.
    50. Chabé-Ferret, Sylvain & Reynaud, Arnaud & Tène, Eva, 2021. "Water Quality, Policy Diffusion Effects and Farmers’ Behavior," TSE Working Papers 21-1229, Toulouse School of Economics (TSE).
    51. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
    52. Niels Gillmann & Ostap Okhrin, 2023. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Papers 2302.02808, arXiv.org.
    53. Yu Chen & Jie Hu & Weiping Zhang, 2020. "Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(6), pages 78-100, November.
    54. Kamil Yilmaz, 2014. "Volatility Connectedness of Bank Stocks Across the Atlantic," Koç University-TUSIAD Economic Research Forum Working Papers 1402, Koc University-TUSIAD Economic Research Forum.
    55. Ge, Shuyi & Li, Shaoran & Linton, Oliver, 2023. "News-implied linkages and local dependency in the equity market," Journal of Econometrics, Elsevier, vol. 235(2), pages 779-815.
    56. Medeiros, Marcelo C. & Mendes, Eduardo F., 2016. "ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 191(1), pages 255-271.
    57. Sessi Tokpavi, 2013. "Testing for the Systemically Important Financial Institutions: a Conditional Approach," EconomiX Working Papers 2013-27, University of Paris Nanterre, EconomiX.
    58. Billio, Monica & Caporin, Massimiliano & Panzica, Roberto Calogero & Pelizzon, Loriana, 2017. "The impact of network connectivity on factor exposures, asset pricing and portfolio diversification," SAFE Working Paper Series 166, Leibniz Institute for Financial Research SAFE.
    59. Emanuele De Meo & Giacomo Tizzanini, 2021. "GDP‐network CoVaR: A tool for assessing growth‐at‐risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 50(2), July.
    60. Paola Cerchiello & Paolo Giudici, 2014. "Conditional graphical models for systemic risk measurement," DEM Working Papers Series 087, University of Pavia, Department of Economics and Management.
    61. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    62. Chuliá, Helena & Estévez, Marc & Uribe, Jorge M., 2023. "Systemic political risk," Economic Modelling, Elsevier, vol. 125(C).
    63. Gomez-Gonzalez, Jose E. & Uribe, Jorge M. & Valencia, Oscar, 2022. "Risk Spillovers between Global Corporations and Latin American Sovereigns: Global Factors Matter," IDB Publications (Working Papers) 12236, Inter-American Development Bank.
    64. Paolo Giudici & Laura Parisi, 2015. "Modeling Systemic Risk with Correlated Stochastic Processes," DEM Working Papers Series 110, University of Pavia, Department of Economics and Management.
    65. Rodolfo C. Moura & Márcio P. Laurini, 2021. "Spillovers and jumps in global markets: A comparative analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5997-6013, October.
    66. Foglia, Matteo & Angelini, Eliana, 2020. "From me to you: Measuring connectedness between Eurozone financial institutions," Research in International Business and Finance, Elsevier, vol. 54(C).
    67. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    68. Boyao Wu & Difang Huang & Muzi Chen, 2023. "Estimating contagion mechanism in global equity market with time‐zone effect," Financial Management, Financial Management Association International, vol. 52(3), pages 543-572, September.
    69. Siva R Venna & Satya Katragadda & Vijay Raghavan & Raju Gottumukkala, 2021. "River Stage Forecasting using Enhanced Partial Correlation Graph," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4111-4126, September.
    70. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    71. Mengting Li & Qifa Xu & Cuixia Jiang & Qinna Zhao, 2023. "The role of tail network topological characteristic in portfolio selection: A TNA‐PMC model," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 37-57, March.
    72. Guðmundsson, Guðmundur Stefán & Brownlees, Christian, 2021. "Detecting groups in large vector autoregressions," Journal of Econometrics, Elsevier, vol. 225(1), pages 2-26.
    73. Daniel Felix Ahelegbey & Luis Carvalho & Eric D. Kolaczyk, 2020. "A Bayesian Covariance Graph And Latent Position Model For Multivariate Financial Time Series," DEM Working Papers Series 181, University of Pavia, Department of Economics and Management.
    74. Dallakyan, Aramayis & Kim, Rakheon & Pourahmadi, Mohsen, 2022. "Time series graphical lasso and sparse VAR estimation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    75. Paolo Giudici & Peter Sarlin & Alessandro Spelta, 2016. "The multivariate nature of systemic risk: direct and common exposures," DEM Working Papers Series 118, University of Pavia, Department of Economics and Management.

  26. Barigozzi, Matteo & Conti, Antonio & Luciani, Matteo, 2012. "Do Euro area countries respond asymmetrically to the common monetary policy?," LSE Research Online Documents on Economics 43344, London School of Economics and Political Science, LSE Library.

    Cited by:

    1. Kerssenfischer, Mark, 2017. "The effects of US monetary policy shocks: Applying external instrument identification to a dynamic factor model," Discussion Papers 08/2017, Deutsche Bundesbank.
    2. Jackson, Laura E. & Owyang, Michael T. & Zubairy, Sarah, 2018. "Debt and stabilization policy: Evidence from a Euro Area FAVAR," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 67-91.
    3. Christophe Blot & Jérôme Creel & Bruno Ducoudre & Xavier Timbeau, 2015. "Back to fiscal consolidation in Europe and its dual tradeoff : now or later, through spending cuts or tax hikes ?," Working Papers hal-01143545, HAL.
    4. Mattia Guerini & Duc Thi Luu & Mauro Napoletano, 2019. "Synchronization Patterns in the European Union," GREDEG Working Papers 2019-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    5. Akbari Dehbaghi, Simin & Arman, Seyed Aziz & Ahangari, Majid, 2020. "The Impact of Domestic and Foreign Monetary Policy on Iran\'s economy: Global Modeling," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 15(2), pages 151-180, April.
    6. Jung, Alexander & Carcel Villanova, Hector, 2020. "The empirical properties of euro area M3, 1980-2017," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 37-49.
    7. Andrea Colabella, 2019. "Do the ECB’s monetary policies benefit emerging market economies? A GVAR analysis on the crisis and post-crisis period," Temi di discussione (Economic working papers) 1207, Bank of Italy, Economic Research and International Relations Area.
    8. Marco Flaccadoro, 2022. "Exchange rate pass-through in small, open, commodity-exporting economies: lessons from Canada," Temi di discussione (Economic working papers) 1368, Bank of Italy, Economic Research and International Relations Area.
    9. Christophe Blot & Marion Cochard & Jérôme Creel & Bruno Ducoudré & Danielle Schweisguth & Xavier Timbeau, 2014. "Fiscal Consolidation, Public Debt and Output Dynamics in the Euro Area: lessons from a simple model with time-varying fiscal multipliers," Revue d'économie politique, Dalloz, vol. 124(6), pages 953-989.
    10. Potjagailo, Galina & Wolters, Maik H., 2019. "Global financial cycles since 1880," IMFS Working Paper Series 132, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    11. von Borstel, Julia & Eickmeier, Sandra & Krippner, Leo, 2016. "The interest rate pass-through in the euro area during the sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 386-402.
    12. Potjagailo, Galina, 2017. "Spillover effects from Euro area monetary policy across Europe: A factor-augmented VAR approach," Journal of International Money and Finance, Elsevier, vol. 72(C), pages 127-147.
    13. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    14. Christophe Blot & Jérôme Creel & Paul Hubert & Fabien Labondance, 2016. "The impact of ECB policies on Euro area investment," Sciences Po publications info:hdl:2441/6m0bv06f219, Sciences Po.
    15. Matteo Farnè & Angelos T. Vouldis, 2021. "Banks’ business models in the euro area: a cluster analysis in high dimensions," Annals of Operations Research, Springer, vol. 305(1), pages 23-57, October.
    16. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).
    17. Bagnai, Alberto & Granville, Brigitte & Mongeau Ospina, Christian A., 2017. "Withdrawal of Italy from the euro area: Stochastic simulations of a structural macroeconometric model," Economic Modelling, Elsevier, vol. 64(C), pages 524-538.
    18. Badinger, Harald & Schiman, Stefan, 2020. "Measuring Monetary Policy with Residual Sign Restrictions at Known Shock Dates," Department of Economics Working Paper Series 300, WU Vienna University of Economics and Business.
    19. Giancarlo Corsetti & Joao B. Duarte & Samuel Mann, 2018. "One Money, Many Markets," Discussion Papers 1805, Centre for Macroeconomics (CFM).
    20. Mario Forni & Luca Gambetti & marco Lippi & Luca Sala, 2020. "Common Components Structural VARs," Center for Economic Research (RECent) 147, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    21. Katerina Arnostova & Oxana Babecka Kucharcukova & Jan Babecky & Vojtech Belling & Sona Benecka & Jan Bruha & Martin Gurtler & Tomas Holub & Eva Hromadkova & Lubos Komarek & Zlatuse Komarkova & Petr Kr, 2016. "Analyses of the Czech Republic's Current Economic Alignment with the Euro Area 2016," Occasional Publications - Edited Volumes, Czech National Bank, number as16 edited by Katerina Arnostova & Lucie Matejkova, January.
    22. Georgios Georgiadis & Martina Jancokova, 2017. "Financial Globalisation, Monetary Policy Spillovers and Macro-modelling: Tales from 1001 Shocks," Globalization Institute Working Papers 314, Federal Reserve Bank of Dallas.
    23. Mandler, Martin & Scharnagl, Michael & Volz, Ute, 2016. "Heterogeneity in euro-area monetary policy transmission: Results from a large multi-country BVAR model," Discussion Papers 03/2016, Deutsche Bundesbank.
    24. Norhana Endut & James Morley & Pao-Lin Tien, 2015. "The Changing Transmission Mechanism of U.S. Monetary Policy," Discussion Papers 2015-03, School of Economics, The University of New South Wales.
    25. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    26. Cavallo, Antonella & Ribba, Antonio, 2015. "Common macroeconomic shocks and business cycle fluctuations in Euro area countries," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 377-392.
    27. Thomas Lux & Duc Thi Luu & Boyan Yanovski, 2020. "An analysis of systemic risk in worldwide economic sentiment indices," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 909-928, November.
    28. Xing, Xiaoyun & Wang, Mingsong & Wang, Yougui & Stanley, H. Eugene, 2020. "Credit creation under multiple banking regulations: The impact of balance sheet diversity on money supply," Economic Modelling, Elsevier, vol. 91(C), pages 720-735.
    29. Simona Hašková & Marek Vochozka, 2018. "Duality in Cyclical Trends in European Union Confirmed," SAGE Open, , vol. 8(1), pages 21582440177, January.
    30. Hanisch, Max & Kempa, Bernd, 2017. "The international transmission channels of US supply and demand shocks: Evidence from a non-stationary dynamic factor model for the G7 countries," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 70-88.
    31. Tomas Adam & Oxana Babecka Kucharcukova & Jan Babecky & Vojtech Belling & Sona Benecka & Jan Bruha & Kamil Galuscak & Tomas Holub & Eva Hromadkova & Lubos Komarek & Zlatuse Komarkova & Kamila Kulhava , 2015. "Analyses of the Czech Republic's Current Economic Alignment with the Euro Area 2015," Occasional Publications - Edited Volumes, Czech National Bank, number as15 edited by Kamila Kulhava & Lucie Matejkova, January.
    32. Petrovska Magdalena & Tonovska Jasna & Nikolov Miso & Sulejmani Artan, 2022. "Evaluating Monetary Policy Effectiveness in North Macedonia: Evidence from a Bayesian Favar Framework," South East European Journal of Economics and Business, Sciendo, vol. 17(2), pages 67-82, December.
    33. Corsetti, Giancarlo & Duarte, Joao B. & Mann, Samuel, 2018. "One money, many markets: a factor model approach to monetary policy in the Euro Area with high-frequency identification," LSE Research Online Documents on Economics 87182, London School of Economics and Political Science, LSE Library.
    34. Geiger, Martin & Gründler, Daniel & Scharler, Johann, 2023. "Monetary policy shocks and consumer expectations in the euro area," Journal of International Economics, Elsevier, vol. 140(C).
    35. Gabe de Bondt, 2017. "Confidence and monetary policy transmission," EcoMod2017 10197, EcoMod.
    36. Stefano Neri & Tiziano Ropele, 2015. "The macroeconomic effects of the sovereign debt crisis in the euro area," Temi di discussione (Economic working papers) 1007, Bank of Italy, Economic Research and International Relations Area.
    37. Roy, Ripon & Bashar, Omar H.N.M. & Bhattacharya, Prasad Sankar, 2023. "The cross-industry effects of monetary policy: New evidence from Bangladesh," Economic Modelling, Elsevier, vol. 127(C).
    38. Florian Huber & Michael Pfarrhofer & Philipp Piribauer, 2020. "A multi‐country dynamic factor model with stochastic volatility for euro area business cycle analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 911-926, September.
    39. Destefanis, Sergio & Fragetta, Matteo & Gasteiger, Emanuel, 2021. "Does one size fit all in the Euro Area? Some counterfactual evidence," ECON WPS - Working Papers in Economic Theory and Policy 05/2019, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit, revised 2021.
    40. Pestova, Anna, 2020. "“Credit view” on monetary policy in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 72-88.
    41. Michael Pfarrhofer & Anna Stelzer, 2019. "The international effects of central bank information shocks," Papers 1912.03158, arXiv.org.
    42. Potjagailo, Galina, 2016. "Spillover effects from euro area monetary policy across the EU: A factor-augmented VAR approach," Kiel Working Papers 2033, Kiel Institute for the World Economy (IfW Kiel).
    43. Max Hanisch, 2017. "US Monetary Policy and the Euro Area," Discussion Papers of DIW Berlin 1701, DIW Berlin, German Institute for Economic Research.
    44. Hristov, Nikolay & Huelsewig, Oliver & Siemsen, Thomas & Wollmershaeuser, Timo, 2019. "Restoring euro area monetary transmission: Which role for government bond rates?," Munich Reprints in Economics 78269, University of Munich, Department of Economics.
    45. Zhu, Bing & Betzinger, Michael & Sebastian, Steffen, 2017. "Housing market stability, mortgage market structure, and monetary policy: Evidence from the euro area," Journal of Housing Economics, Elsevier, vol. 37(C), pages 1-21.
    46. Corsetti, Giancarlo & Duarte, Joao B. & Mann, Samuel, 2020. "One Money, Many Markets: Monetary Transmission and Housing Financing in the Euro Area," CEPR Discussion Papers 14968, C.E.P.R. Discussion Papers.
    47. Katerina Arnostova & Tomas Adam & Oxana Babecka Kucharcukova & Jan Babecky & Vojtech Belling & Sona Benecka & Jan Bruha & Martin Gurtler & Tibor Hledik & Tomas Holub & Eva Hromadkova & Lubos Komarek &, 2017. "Analyses of the Czech Republic's Current Economic Alignment with the Euro Area 2017," Occasional Publications - Edited Volumes, Czech National Bank, number as17 edited by Katerina Arnostova & Lucie Matejkova, January.
    48. Ayla OguÅŸ Binatli & Niloufer Sohrabji, 2019. "Monetary Policy Transmission in the Euro Zone," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 5(1), pages 79-92, January.
    49. Conti, Antonio M., 2021. "Resurrecting the Phillips Curve in Low-Inflation Times," Economic Modelling, Elsevier, vol. 96(C), pages 172-195.
    50. Hanisch, Max, 2019. "US monetary policy and the euro area," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 77-96.
    51. Neri, Stefano & Nobili, Andrea & Conti, Antonio M., 2017. "Low inflation and monetary policy in the euro area," Working Paper Series 2005, European Central Bank.
    52. Krokida, Styliani-Iris & Makrychoriti, Panagiota & Spyrou, Spyros, 2020. "Monetary policy and herd behavior: International evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 386-417.
    53. Andrea Colabella, 2021. "Do ECB's Monetary Policies Benefit EMEs? A GVAR Analysis on the Global Financial and Sovereign Debt Crises and Postcrises Period," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 472-494, April.
    54. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Dynamic Factor Models, Cointegration, and Error Correction Mechanisms," Finance and Economics Discussion Series 2016-018, Board of Governors of the Federal Reserve System (U.S.).
    55. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2020. "Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors," Econometrics, MDPI, vol. 8(1), pages 1-23, February.
    56. Karim Triki, 2016. "Expenditure-based Consolidation: Experiences and Outcomes – Workshop proceedings," European Economy - Discussion Papers 026, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    57. Alberto Caruso, 2016. "The Impact of Macroeconomic News on the Euro-Dollar Exchange Rate," Working Papers ECARES ECARES 2016-32, ULB -- Universite Libre de Bruxelles.
    58. Theron Shumba & Sophia Mukorera, 2023. "Monetary Policy Implications on Macroeconomic Performance in the Common Monetary Area: A Panel-SVAR Framework," Economies, MDPI, vol. 11(5), pages 1-18, May.
    59. Hanisch, Max, 2017. "The effectiveness of conventional and unconventional monetary policy: Evidence from a structural dynamic factor model for Japan," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 110-134.
    60. Ute Volz & Martin Mandler & Michael Scharnagl, 2016. "Heterogeneity in Euro Area Monetary Policy Transmission: Results from a large Multi-Country BVAR," EcoMod2016 9609, EcoMod.

  27. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2011. "Measuring Euro Area Monetary Policy Transmission in a Structural Dynamic Factor Model," European Economy - Economic Papers 2008 - 2015 441, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.

    Cited by:

    1. Konstantins Benkovskis & Andrejs Bessonovs & Martin Feldkircher & Julia Wörz, 2011. "The Transmission of Euro Area Monetary Shocks to the Czech Republic, Poland and Hungary: Evidence from a FAVAR Model," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 8-36.
    2. Prettner, Catherine & Prettner, Klaus, 2012. "After Two Decades of Integration: How Interdependent are Eastern European Economies and the Euro Area?," Department of Economics Working Paper Series 138, WU Vienna University of Economics and Business.
    3. Prettner, Catherine & Prettner, Klaus, 2014. "How interdependent are Eastern European economies and the Euro area?," University of Göttingen Working Papers in Economics 187, University of Goettingen, Department of Economics.
    4. Efrem Castelnuovo, 2016. "Monetary policy shocks and Cholesky VARs: an assessment for the Euro area," Empirical Economics, Springer, vol. 50(2), pages 383-414, March.
    5. Bhattacharya, Rudrani & Tripathi, Shruti & Chowdhury, Sahana Roy, 2019. "Financial structure, institutional quality and monetary policy transmission: A Meta-Analysis," Working Papers 19/274, National Institute of Public Finance and Policy.

  28. Matteo Barigozzi & Alessio Moneta, 2011. "The Rank of a System of Engel Curves. How Many Common Factors?," Papers on Economics and Evolution 2011-01, Philipps University Marburg, Department of Geography.

    Cited by:

    1. Andreas Chai, 2017. "Tackling Keynes’ question: a look back on 15 years of Learning To Consume," Journal of Evolutionary Economics, Springer, vol. 27(2), pages 251-271, April.

  29. Matteo Barigozzi & Christian T. Brownlees & Giampiero M. Gallo & David Veredas, 2010. "Disentangling Systematic and Idiosyncratic Risk for Large Panels of Assets," Econometrics Working Papers Archive wp2010_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".

    Cited by:

    1. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.
    2. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    3. Bernard Herskovic & Bryan Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2020. "Firm Volatility in Granular Networks," Journal of Political Economy, University of Chicago Press, vol. 128(11), pages 4097-4162.
    4. Bollerslev, Tim & Todorov, Viktor & Li, Sophia Zhengzi, 2013. "Jump tails, extreme dependencies, and the distribution of stock returns," Journal of Econometrics, Elsevier, vol. 172(2), pages 307-324.
    5. Herskovic, Bernard & Kelly, Bryan & Lustig, Hanno & Van Nieuwerburgh, Stijn, 2016. "The common factor in idiosyncratic volatility: Quantitative asset pricing implications," Journal of Financial Economics, Elsevier, vol. 119(2), pages 249-283.

  30. Matteo Barigozzi & Antonio Conti, 2010. "On the Sources of Euro Area Money Demand Stability. A Time-Varying Cointegration Analysis," Working Papers ECARES ECARES 2010-022, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. De Santis, Roberto A. & Favero, Carlo A. & Roffia, Barbara, 2008. "Euro area money demand and international portfolio allocation: a contribution to assessing risks to price stability," Working Paper Series 926, European Central Bank.
    2. Setzer, Ralph & Wolff, Guntram B., 2009. "Money demand in the euro area: new insights from disaggregated data," MPRA Paper 17483, University Library of Munich, Germany.
    3. Chen-Huan Shieh & Shou-Hsiang Liu & Chung-Ching Lee, 2017. "How Stable is the Money Demand in Taiwan?," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 3(5), pages 54-64, 05-2017.

  31. Matteo Barigozzi & Giorgio Fagiolo & Giuseppe Mangioni, 2010. "Identifying the Community Structure of the International-Trade Multi Network," LEM Papers Series 2010/15, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

    Cited by:

    1. Michela Chessa & Arnaud Persenda & Dominique Torre, 2023. "Brexit and Canadadvent: An application of graphs and hypergraphs to recent international trade agreements," Post-Print hal-04194464, HAL.
    2. Rodrigo Mesa-Arango & Badri Narayanan & Satish V. Ukkusuri, 2019. "The Impact of International Crises on Maritime Transportation Based Global Value Chains," Networks and Spatial Economics, Springer, vol. 19(2), pages 381-408, June.
    3. Nobi, Ashadun & Lee, Tae Ho & Lee, Jae Woo, 2020. "Structure of trade flow networks for world commodities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    4. IKEDA Yuichi & AOYAMA Hideaki & IYETOMI Hiroshi & MIZUNO Takayuki & OHNISHI Takaaki & SAKAMOTO Yohei & WATANABE Tsutomu, 2016. "Econophysics Point of View of Trade Liberalization: Community dynamics, synchronization, and controllability as example of collective motions," Discussion papers 16026, Research Institute of Economy, Trade and Industry (RIETI).
    5. Hao, Xiaoqing & An, Haizhong, 2022. "Comparative study on transmission mechanism of supply shortage risk in the international trade of iron ore, pig iron and crude steel," Resources Policy, Elsevier, vol. 79(C).
    6. Juan de Lucio & Raúl Mínguez & Asier Minondo & Francisco Requena, 2014. "Networks and the Dynamics of Firms' Export Portfolio: Evidence for Mexico," Working Papers 2014003, The University of Sheffield, Department of Economics.
    7. A. Baronchelli & T.E. Uberti, 2018. "Exports and FDI: comparing networks in the new millennium," Working Paper CRENoS 201813, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    8. Yujing Wang & Fu Ren & Ruoxin Zhu & Qingyun Du, 2020. "An Exploratory Analysis of Networked and Spatial Characteristics of International Natural Resource Trades (2000–2016)," Sustainability, MDPI, vol. 12(18), pages 1-34, September.
    9. Marco Grassia & Giuseppe Mangioni & Stefano Schiavo & Silvio Traverso, 2020. "(Unintended) Consequences of export restrictions on medical goods during the Covid-19 pandemic," Papers 2007.11941, arXiv.org.
    10. Ali Kharrazi & Brian D. Fath & Harald Katzmair, 2016. "Advancing Empirical Approaches to the Concept of Resilience: A Critical Examination of Panarchy, Ecological Information, and Statistical Evidence," Sustainability, MDPI, vol. 8(9), pages 1-17, September.
    11. Qing Guan & Haizhong An & Xiaoqing Hao & Xiaoliang Jia, 2016. "The Impact of Countries’ Roles on the International Photovoltaic Trade Pattern: The Complex Networks Analysis," Sustainability, MDPI, vol. 8(4), pages 1-16, March.
    12. Marco Duenas & Giorgio Fagiolo, 2011. "Modeling the International-Trade Network: A Gravity Approach," LEM Papers Series 2011/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. LI, Yang & Luo, Jingqiu & Jiang, Yongmu, 2021. "Policy uncertainty spillovers and financial risk contagion in the Asia-Pacific network," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    14. Liu, Linqing & Shen, Mengyun & Sun, Da & Yan, Xiaofei & Hu, Shi, 2022. "Preferential attachment, R&D expenditure and the evolution of international trade networks from the perspective of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    15. Adelaide Baronchelli & Teodora Erika Uberti, 2021. "International Economic Integration: Comparing Exports and FDI Networks in the New Millennium," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(11), pages 1-30, November.
    16. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    17. Lovrić, Marko & Da Re, Riccardo & Vidale, Enrico & Pettenella, Davide & Mavsar, Robert, 2018. "Social network analysis as a tool for the analysis of international trade of wood and non-wood forest products," Forest Policy and Economics, Elsevier, vol. 86(C), pages 45-66.
    18. Zhu, Bo & Liu, Jiahao & Lin, Renda & Chevallier, Julien, 2021. "Cross-border systemic risk spillovers in the global oil system: Does the oil trade pattern matter?," Energy Economics, Elsevier, vol. 101(C).
    19. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2011. "Null Models of Economic Networks: The Case of the World Trade Web," Papers 1112.2895, arXiv.org, revised Sep 2012.
    20. Wu, Jianshe & Li, Xiaoxiao & Jiao, Licheng & Wang, Xiaohua & Sun, Bo, 2013. "Minimum spanning trees for community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2265-2277.
    21. John J Bartholdi & Pisit Jarumaneeroj & Amar Ramudhin, 2016. "A new connectivity index for container ports," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 18(3), pages 231-249, September.
    22. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Structure of global buyer-supplier networks and its implications for conflict minerals regulations," Papers 1505.02274, arXiv.org.
    23. Zhong, Weiqiong & An, Haizhong & Shen, Lei & Dai, Tao & Fang, Wei & Gao, Xiangyun & Dong, Di, 2017. "Global pattern of the international fossil fuel trade: The evolution of communities," Energy, Elsevier, vol. 123(C), pages 260-270.
    24. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    25. Alessandro Chessa & Irene Crimaldi & Massimo Riccaboni & Luca Trapin, 2014. "Cluster Analysis of Weighted Bipartite Networks: A New Copula-Based Approach," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-12, October.
    26. Rosanna Grassi & Paolo Bartesaghi & Stefano Benati & Gian Paolo Clemente, 2021. "Multi-Attribute Community Detection in International Trade Network," Networks and Spatial Economics, Springer, vol. 21(3), pages 707-733, September.
    27. Yuichi Ikeda & Hiroshi Iyetomi, 2018. "Trade Network Reconstruction and Simulation with Changes in Trade Policy," Papers 1806.00605, arXiv.org.
    28. Sun, Mei & Li, Juan & Gao, Cuixia & Han, Dun, 2017. "Identifying regime shifts in the US electricity market based on price fluctuations," Applied Energy, Elsevier, vol. 194(C), pages 658-666.
    29. Zhong, Weiqiong & An, Haizhong & Shen, Lei & Fang, Wei & Gao, Xiangyun & Dong, Di, 2017. "The roles of countries in the international fossil fuel trade: An emergy and network analysis," Energy Policy, Elsevier, vol. 100(C), pages 365-376.
    30. Fu, Xin & Yang, Yu & Dong, Wen & Wang, Changjian & Liu, Yi, 2017. "Spatial structure, inequality and trading community of renewable energy networks: A comparative study of solar and hydro energy product trades," Energy Policy, Elsevier, vol. 106(C), pages 22-31.
    31. Stefania Vitali & Stefano Battiston, 2013. "The Community Structure of the Global Corporate Network," Papers 1301.2363, arXiv.org.
    32. Jalili, Mahdi, 2017. "Spike phase synchronization in multiplex cortical neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 325-333.
    33. Fraňková, Eva & Fousek, Jan & Kala, Lukáš & Labohý, Jan, 2014. "Transaction network analysis for studying Local Exchange Trading Systems (LETS): Research potentials and limitations," Ecological Economics, Elsevier, vol. 107(C), pages 266-275.
    34. Yang, Yu & Poon, Jessie P.H. & Liu, Yi & Bagchi-Sen, Sharmistha, 2015. "Small and flat worlds: A complex network analysis of international trade in crude oil," Energy, Elsevier, vol. 93(P1), pages 534-543.
    35. Nicolò Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2017. "The Multiplex Dependency Structure of Financial Markets," Complexity, Hindawi, vol. 2017, pages 1-13, September.
    36. Julian Maluck & Reik V Donner, 2015. "A Network of Networks Perspective on Global Trade," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-24, July.
    37. Zhong, Weiqiong & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2014. "The evolution of communities in the international oil trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 42-52.
    38. Yuichi Ikeda, 2020. "An Interacting Agent Model of Economic Crisis," Papers 2001.11843, arXiv.org.
    39. Carlo Piccardi, 2011. "Finding and Testing Network Communities by Lumped Markov Chains," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-13, November.
    40. Sun, Qingru & Gao, Xiangyun & Zhong, Weiqiong & Liu, Nairong, 2017. "The stability of the international oil trade network from short-term and long-term perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 345-356.
    41. Ehsan Ardjmand & William A. Young II & Najat E. Almasarwah, 2021. "Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 12(2), pages 15-32, April.
    42. Nicole Palan & Nadia Simoes & Nuno Crespo, 2021. "Measuring fifty years of trade globalisation," The World Economy, Wiley Blackwell, vol. 44(6), pages 1859-1884, June.
    43. Yuichi Ikeda & Hiroshi Iyetomi, 2018. "Trade network reconstruction and simulation with changes in trade policy," Evolutionary and Institutional Economics Review, Springer, vol. 15(2), pages 495-513, December.
    44. Hoppe, K. & Rodgers, G.J., 2015. "A microscopic study of the fitness-dependent topology of the world trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 64-74.
    45. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2020. "Community structure in the World Trade Network based on communicability distances," Papers 2001.06356, arXiv.org, revised Jul 2020.
    46. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Structure of global buyer-supplier networks and its implications for conflict minerals regulations," UTokyo Price Project Working Paper Series 053, University of Tokyo, Graduate School of Economics.
    47. Kyle F Davis & Paolo D'Odorico & Francesco Laio & Luca Ridolfi, 2013. "Global Spatio-Temporal Patterns in Human Migration: A Complex Network Perspective," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.
    48. Xia, Qifan & Du, Debin & Cao, Wanpeng & Li, Xiya, 2023. "Who is the core? Reveal the heterogeneity of global rare earth trade structure from the perspective of industrial chain," Resources Policy, Elsevier, vol. 82(C).
    49. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Structure of global buyer-supplier networks and its implications for conflict minerals regulations," CARF F-Series CARF-F-362, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    50. Jayles, Bertrand & Cheong, Siew Ann & Herrmann, Hans J., 2022. "Modeling the resilience of social networks to lockdowns regarding the dynamics of meetings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 602(C).
    51. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2022. "Community structure in the World Trade Network based on communicability distances," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 405-441, April.
    52. Xu, Helian & Cheng, Long, 2019. "The study of the influence of common humanistic relations on international services trade-from the perspective of multi-networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 642-651.
    53. Li, Yuke & Wu, Tianhao & Marshall, Nicholas & Steinerberger, Stefan, 2017. "Extracting geography from trade data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 205-212.
    54. Carlo Piccardi & Lucia Tajoli, 2015. "Are Preferential Agreements Significant for the World Trade Structure? A Network Community Analysis," Kyklos, Wiley Blackwell, vol. 68(2), pages 220-239, May.

  32. Barigozzi, Matteo & Alessi, Lucia & Capasso, Marco & Fagiolo, Giorgio, 2009. "The distribution of households consumption-expenditure budget shares," Working Paper Series 1061, European Central Bank.

    Cited by:

    1. Joachim Kaldasch, 2012. "Evolutionary Model of the Personal Income Distribution," Papers 1203.6507, arXiv.org.
    2. Mien, Toh Siaw & Said, Rusmawati, 2018. "A Cross-sectional Household Analysis of Household Consumption Patterns: An Indirect Approach to Identify the Possible Factors of Personal Bankruptcy," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 52(3), pages 231-246.
    3. Yu, Jian & Shi, Xunpeng & Cheong, Tsun Se, 2021. "Distribution dynamics of China's household consumption upgrading," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 193-203.
    4. Stefano Marchetti & Caterina Giusti & Monica Pratesi, 2016. "The use of Twitter data to improve small area estimates of households’ share of food consumption expenditure in Italy [Die Nutzung von Twitter Daten um die Small Area Schätzungen vom Ausgabenanteil," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(2), pages 79-93, October.
    5. Ioannis Kostakis & Dimitrios Paparas & Anna Saiti & Stamatina Papadaki, 2020. "Food Consumption within Greek Households: Further Evidence from a National Representative Sample," Economies, MDPI, vol. 8(1), pages 1-18, February.
    6. Voytenkov, Valentin & Demidova, Olga, 2023. "Impact of COVID-19 on household consumption in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 72, pages 73-99.
    7. Iwona Bak & Beata Szczecinska, 2021. "Economic Aspects of Population Aging. Modeling Senior Household Ependiture," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 50-67.

  33. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2009. "A Robust Criterion for Determining the Number of Factors in Approximate Factor Models," Working Papers ECARES 2009_023, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Juan José Echavarría & Andrés gonzález & Enrique López & Norberto Rodríguez, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," Borradores de Economia 9884, Banco de la Republica.
    2. Xisong Jin, 2018. "How much does book value data tell us about systemic risk and its interactions with the macroeconomy? A Luxembourg empirical evaluation," BCL working papers 118, Central Bank of Luxembourg.
    3. Xisong Jin & Francisco Nadal De Simone, 2013. "Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach," BCL working papers 82, Central Bank of Luxembourg.
    4. Xisong Jin & Francisco Nadal De Simone, 2017. "Systemic Financial Sector and Sovereign Risks," BCL working papers 109, Central Bank of Luxembourg.
    5. Travaglini, Guido, 2011. "Principal Components and Factor Analysis. A Comparative Study," MPRA Paper 35486, University Library of Munich, Germany.
    6. Jiang, Yu & Guo, Yongji & Zhang, Yihao, 2017. "Forecasting China's GDP growth using dynamic factors and mixed-frequency data," Economic Modelling, Elsevier, vol. 66(C), pages 132-138.
    7. Fernando tenjo Galarza & Enrique López E. & Diego H. Rodríguez H., 2011. "El canal de préstamos de la política monetaria en Colombia. Un enfoque FAVAR," Borradores de Economia 684, Banco de la Republica de Colombia.
    8. Fernando Tenjo Galarza & Enrique López E. & Diego H. Rodríguez H., 2012. "El canal de préstamos de la política monetaria en Colombia. Un enfoque FAVAR," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 195-256, December.
    9. Xisong Jin & Francisco Nadal De Simone, 2015. "Investment funds? vulnerabilities: A tail-risk dynamic CIMDO approach," BCL working papers 95, Central Bank of Luxembourg.
    10. Jin, Xisong & Nadal De Simone, Francisco, 2014. "A framework for tracking changes in the intensity of investment funds' systemic risk," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 343-368.
    11. Xisong Jin & Francisco Nadal De Simone, 2016. "Tracking Changes in the Intensity of Financial Sector's Systemic Risk," BCL working papers 102, Central Bank of Luxembourg.

  34. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2009. "Estimation and forecasting in large datasets with conditionally heteroskedastic dynamic common factors," Working Paper Series 1115, European Central Bank.

    Cited by:

    1. Hallin, Marc & Trucíos, Carlos, 2023. "Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach," Econometrics and Statistics, Elsevier, vol. 27(C), pages 1-15.
    2. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    3. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    4. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    5. Driton Kuçi, 2015. "Contemporary Models of Organization of Power and the Macedonian Model of Organization of Power," European Journal of Interdisciplinary Studies Articles, Revistia Research and Publishing, vol. 1, September.
    6. Chevallier, Julien, 2011. "Macroeconomics, finance, commodities: Interactions with carbon markets in a data-rich model," Economic Modelling, Elsevier, vol. 28(1-2), pages 557-567, January.
    7. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    8. Marc Hallin & Charles Mathias & Hugues Pirotte & David Veredas, 2011. "Market liquidity as dynamic factors," Working Papers ECARES 163, 42-50, ULB -- Universite Libre de Bruxelles.
    9. Aramonte, Sirio & Giudice Rodriguez, Marius del & Wu, Jason, 2013. "Dynamic factor Value-at-Risk for large heteroskedastic portfolios," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4299-4309.
    10. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    11. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    12. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    13. Marc Hallin & Carlos Trucíos, 2020. "Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach," Working Papers ECARES 2020-50, ULB -- Universite Libre de Bruxelles.

  35. Matteo Barigozzi & Giorgio Fagiolo & Diego Garlaschelli, 2009. "Multinetwork of international trade: A commodity-specific analysis," Papers 0908.1879, arXiv.org, revised Jun 2010.

    Cited by:

    1. Aldasoro, Iñaki & Alves, Iván, 2016. "Multiplex interbank networks and systemic importance – An application to European data," ESRB Working Paper Series 20, European Systemic Risk Board.
    2. Franco Ruzzenenti & Andreas Joseph & Elisa Ticci & Pietro Vozzella & Giampaolo Gabbi, 2015. "Interactions between financial and environmental networks in OECD countries," Papers 1501.04992, arXiv.org, revised Apr 2015.
    3. Roland Lantner & Didier Lebert, 2013. "Dominance, dependence and interdependence in linear structures. A theoretical model and an application to the international trade flows," Post-Print halshs-00825477, HAL.
    4. Rodrigo Mesa-Arango & Badri Narayanan & Satish V. Ukkusuri, 2019. "The Impact of International Crises on Maritime Transportation Based Global Value Chains," Networks and Spatial Economics, Springer, vol. 19(2), pages 381-408, June.
    5. Gianfranco Giulioni & Edmondo Di Giuseppe & Massimiliano Pasqui & Piero Toscano & Francesco Miglietta, 2018. "Investigating Wheat Price with a Multi-Agent Model," Papers 1807.10537, arXiv.org.
    6. Nobi, Ashadun & Lee, Tae Ho & Lee, Jae Woo, 2020. "Structure of trade flow networks for world commodities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    7. C'elestin Coquid'e & Jos'e Lages & Dima L. Shepelyansky, 2020. "Crisis contagion in the world trade network," Papers 2002.07100, arXiv.org.
    8. Hu, Xiaoqian & Wang, Chao & Lim, Ming K. & Chen, Wei-Qiang, 2020. "Characteristics of the global copper raw materials and scrap trade systems and the policy impacts of China's import ban," Ecological Economics, Elsevier, vol. 172(C).
    9. De Benedictis, L. & Nenci, S. & Santoni, G. & Tajoli, L. & Vicarelli, C., 2013. "Network Analysis of World Trade using the BACI-CEPII dataset," Working papers 471, Banque de France.
    10. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    11. Hong, Liu & Yan, Yongze & Ouyang, Min & Tian, Hui & He, Xiaozheng, 2017. "Vulnerability effects of passengers' intermodal transfer distance preference and subway expansion on complementary urban public transportation systems," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 58-72.
    12. V. Kandiah & H. Escaith & D. L. Shepelyansky, 2015. "Contagion effects in the world network of economic activities," Papers 1507.03278, arXiv.org.
    13. V. Kandiah & H. Escaith & D. L. Shepelyansky, 2015. "Google matrix of the world network of economic activities," Papers 1504.06773, arXiv.org.
    14. A. Baronchelli & T.E. Uberti, 2018. "Exports and FDI: comparing networks in the new millennium," Working Paper CRENoS 201813, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    15. Martinčić-Ipšić, Sanda & Margan, Domagoj & Meštrović, Ana, 2016. "Multilayer network of language: A unified framework for structural analysis of linguistic subsystems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 117-128.
    16. Giudici, Paolo & Huang, Bihong & Spelta, Alessandro, 2018. "Trade Networks and Economic Fluctuations in Asia," ADBI Working Papers 832, Asian Development Bank Institute.
    17. Marco Duenas & Giorgio Fagiolo, 2011. "Modeling the International-Trade Network: A Gravity Approach," LEM Papers Series 2011/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    18. Luu, Duc Thi & Lux, Thomas, 2018. "Multilayer overlaps and correlations in the bank-firm credit network of Spain," Economics Working Papers 2018-04, Christian-Albrechts-University of Kiel, Department of Economics.
    19. Michael Lebacher & Paul W. Thurner & Göran Kauermann, 2021. "A dynamic separable network model with actor heterogeneity: An application to global weapons transfers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 201-226, January.
    20. Roland Lantner & Didier Lebert, 2013. "Dominance, dependence and interdependence in linear structures. A theoretical model and an application to the international trade flows," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00825477, HAL.
    21. Célestin Coquidé & José Lages & Dima Shepelyansky, 2020. "Interdependence of sectors of economic activities for world countries from the reduced Google matrix analysis of WTO data," Post-Print hal-02132487, HAL.
    22. Jeroen van Lidth de Jeude & Riccardo Di Clemente & Guido Caldarelli & Fabio Saracco & Tiziano Squartini, 2019. "Reconstructing Mesoscale Network Structures," Complexity, Hindawi, vol. 2019, pages 1-13, January.
    23. Marco Duenas & Giorgio Fagiolo, 2013. "Global Trade Imbalances: A Network Approach," LEM Papers Series 2013/12, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    24. Michael Lebacher & Paul W. Thurner & Göran Kauermann, 2021. "Censored regression for modelling small arms trade volumes and its ‘Forensic’ use for exploring unreported trades," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 909-933, August.
    25. Fourel, V. & Héam, J-C. & Salakhova, D. & Tavolaro, S., 2013. "Domino Effects when Banks Hoard Liquidity: The French network," Working papers 432, Banque de France.
    26. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2022. "Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network," Papers 2206.06309, arXiv.org, revised Dec 2022.
    27. Angela Abbate & Luca De Benedictis & Giorgio Fagiolo & Lucia Tajoli, 2012. "The International Trade Network in Space and Time," LEM Papers Series 2012/17, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    28. Luca Salvatici & Silvia Nenci, 2017. "New features, forgotten costs and counterfactual gains of the international trading system," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 44(4), pages 592-633.
    29. Adelaide Baronchelli & Teodora Erika Uberti, 2021. "International Economic Integration: Comparing Exports and FDI Networks in the New Millennium," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 13(11), pages 1-30, November.
    30. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    31. Kyu-Min Lee & Kwang-Il Goh, 2016. "Strength of weak layers in cascading failures on multiplex networks: case of the international trade network," Papers 1603.05181, arXiv.org, revised May 2016.
    32. Lovrić, Marko & Da Re, Riccardo & Vidale, Enrico & Pettenella, Davide & Mavsar, Robert, 2018. "Social network analysis as a tool for the analysis of international trade of wood and non-wood forest products," Forest Policy and Economics, Elsevier, vol. 86(C), pages 45-66.
    33. Zhang, Xiaohang & Cui, Huiyuan & Zhu, Ji & Du, Yu & Wang, Qi & Shi, Wenhua, 2017. "Measuring the dissimilarity of multiplex networks: An empirical study of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 380-394.
    34. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2011. "Null Models of Economic Networks: The Case of the World Trade Web," Papers 1112.2895, arXiv.org, revised Sep 2012.
    35. Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
    36. Marlies Hanna Schütz & Nicole Palan, 2016. "Restructuring of the international clothing and textile trade network: the role of Italy and Portugal," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 5(1), pages 1-29, December.
    37. Gianfranco Giulioni & Edmondo Di Giuseppe & Piero Toscano & Francesco Miglietta & Massimiliano Pasqui, 2019. "A Novel Computational Model of the Wheat Global Market with an Application to the 2010 Russian Federation Case," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(3), pages 1-4.
    38. Mario Maggioni & Teodora Uberti, 2011. "Networks and geography in the economics of knowledge flows," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(5), pages 1031-1051, August.
    39. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    40. Marco Bardoscia & Paolo Barucca & Stefano Battiston & Fabio Caccioli & Giulio Cimini & Diego Garlaschelli & Fabio Saracco & Tiziano Squartini & Guido Caldarelli, 2021. "The Physics of Financial Networks," Papers 2103.05623, arXiv.org.
    41. Yuichi Ikeda & Hiroshi Iyetomi, 2018. "Trade Network Reconstruction and Simulation with Changes in Trade Policy," Papers 1806.00605, arXiv.org.
    42. Leonardo Ermann & Dima L. Shepelyansky, 2015. "Google matrix analysis of the multiproduct world trade network," Papers 1501.03371, arXiv.org.
    43. Marlies Schütz & Nicole Palan, 2015. "Restructuring the International Textile Production and Trade Network. The Role of Italy and Portugal," FIW Working Paper series 156, FIW.
    44. Peiteng Shi & Jiang Zhang & Bo Yang & Jingfei Luo, 2014. "Hierarchicality of Trade Flow Networks Reveals Complexity of Products," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-10, June.
    45. Di Vece, Marzio & Garlaschelli, Diego & Squartini, Tiziano, 2023. "Reconciling econometrics with continuous maximum-entropy network models," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    46. Yang, Yu & Poon, Jessie P.H. & Liu, Yi & Bagchi-Sen, Sharmistha, 2015. "Small and flat worlds: A complex network analysis of international trade in crude oil," Energy, Elsevier, vol. 93(P1), pages 534-543.
    47. Li, Liqiang & Liu, Jing, 2020. "The aggregation of multiplex networks based on the similarity of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    48. Julian Maluck & Reik V Donner, 2015. "A Network of Networks Perspective on Global Trade," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-24, July.
    49. Zhong, Weiqiong & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2014. "The evolution of communities in the international oil trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 42-52.
    50. Yuichi Ikeda, 2020. "An Interacting Agent Model of Economic Crisis," Papers 2001.11843, arXiv.org.
    51. Grazzini, Jakob & Spelta, Alessandro, 2022. "An empirical analysis of the global input–output network and its evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    52. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna, 2023. "Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    53. Peiteng Shi & Jiang Zhang & Bo Yang & Jingfei Luo, 2014. "Hierarchicality of Trade Flow Networks Reveals Complexity of Products," Papers 1401.3103, arXiv.org.
    54. Barigozzi, Matteo & Fagiolo, Giorgio & Mangioni, Giuseppe, 2011. "Identifying the community structure of the international-trade multi-network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2051-2066.
    55. Kim, Min Jae & Kim, Sehyun & Jo, Yong Hwan & Kim, Soo Yong, 2011. "Dependence structure of the commodity and stock markets, and relevant multi-spread strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3842-3854.
    56. Roland Lantner & Didier Lebert, 2013. "Dominance, dependence and interdependence in linear structures. A theoretical model and an application to the international trade flows," Documents de travail du Centre d'Economie de la Sorbonne 13043, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    57. Mika J. Straka & Guido Caldarelli & Tiziano Squartini & Fabio Saracco, 2017. "From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networks," Papers 1710.10143, arXiv.org.
    58. Wang, Bi & Su, Qin & Chin, Kwai Sang, 2021. "Vulnerability assessment of China–Europe Railway Express multimodal transport network under cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    59. Shi, Qing & Sun, Xiaoqi & Xu, Man & Wang, Mengjiao, 2022. "The multiplex network structure of global cobalt industry chain," Resources Policy, Elsevier, vol. 76(C).
    60. Pietro Vozzella & Franco Ruzzenenti & Giampaolo Gabbi, 2019. "Energy and Environmental Flows: Do Most Financialised Countries within the Mediterranean Area Export Unsustainability?," Sustainability, MDPI, vol. 11(13), pages 1-15, July.
    61. Zhuo-Ming Ren & An Zeng & Yi-Cheng Zhang, 2020. "Bridging nestedness and economic complexity in multilayer world trade networks," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.
    62. Yuichi Ikeda & Hiroshi Iyetomi, 2018. "Trade network reconstruction and simulation with changes in trade policy," Evolutionary and Institutional Economics Review, Springer, vol. 15(2), pages 495-513, December.
    63. Deepika Srivastava & M. Rahul, 2024. "Network analysis of trade and FDI," SN Business & Economics, Springer, vol. 4(1), pages 1-27, January.
    64. Hoppe, K. & Rodgers, G.J., 2015. "A microscopic study of the fitness-dependent topology of the world trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 64-74.
    65. C'elestin Coquid'e & Jos'e Lages & Dima L. Shepelyansky, 2023. "Prospects of BRICS currency dominance in international trade," Papers 2305.00585, arXiv.org.
    66. Vittorio Carlei & Francesca Affortunato & Alessandro Marra & Marco Brogi, 2019. "Does centrality of importing countries affect export prices in the global trade?," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 529-551, January.
    67. Arvis, Jean-Francois, 2013. "How many dimensions do we trade in ? product space geometry and latent comparative advantage," Policy Research Working Paper Series 6478, The World Bank.
    68. Shen, Xinran & Lovrić, Marko, 2022. "Structural determinants of global trade in graphic paper and pulp products," Forest Policy and Economics, Elsevier, vol. 134(C).
    69. C'elestin Coquid'e & Leonardo Ermann & Jos'e Lages & D. L. Shepelyansky, 2019. "Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE data," Papers 1903.01820, arXiv.org.
    70. N. Foti & S. Pauls & Daniel N. Rockmore, 2011. "Stability of the World Trade Web over Time - An Extinction Analysis," Papers 1104.4380, arXiv.org, revised May 2011.
    71. Knoll, Susanne & Padula, Antonio Domingos & Crespolini dos Santos, Mariane & Pumi, Guilherme & Zhou, Shudong & Zhong, Funing & Jardim Barcellos, Julio Otavio, 2018. "Information flow in the Sino-Brazilian beef trade," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 21(1).
    72. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna & Luu, Duc Thi, 2022. "The multilayer architecture of the global input-output network and its properties," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 304-341.
    73. Ma, Yu & Wang, Minxi & Li, Xin, 2022. "Analysis of the characteristics and stability of the global complex nickel ore trade network," Resources Policy, Elsevier, vol. 79(C).
    74. Yanni Huang & Taha Hossein Rashidi & Lauren Gardner, 2018. "Modelling the global maritime container network," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 20(3), pages 400-420, September.

  36. Matteo Barigozzi & Biagio Speciale, 2009. "Immigrant’s legal status, permanence in the destination country and the distribution of consumption expenditure," Working Papers ECARES 2009_019, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. Adamopoulou, Effrosyni & Kaya, Ezgi, 2019. "Not Just a Work Permit: EU Citizenship and the Consumption Behavior of Documented and Undocumented Immigrants," Cardiff Economics Working Papers E2019/16, Cardiff University, Cardiff Business School, Economics Section.

  37. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2008. "A robust criterion for determining the number of static factors in approximate factor models," Working Paper Series 903, European Central Bank.

    Cited by:

    1. Juan José Echavarría & Andrés gonzález & Enrique López & Norberto Rodríguez, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," Borradores de Economia 9884, Banco de la Republica.
    2. Jin, Xisong & Nadal De Simone, Francisco, 2020. "Monetary policy and systemic risk-taking in the Euro area investment fund industry: A structural factor-augmented vector autoregression analysis," Journal of Financial Stability, Elsevier, vol. 49(C).
    3. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    4. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    5. Le, Vu & Wang, Qing, 2014. "Robust thresholding for Diffusion Index forecast," Economics Letters, Elsevier, vol. 125(1), pages 52-56.
    6. Alain Kabundi & Francisco Nadal De Simone, 2011. "France in the global economy: a structural approximate dynamic factor model analysis," Empirical Economics, Springer, vol. 41(2), pages 311-342, October.
    7. Vedolin, Andrea, 2012. "Uncertainty and leveraged Lucas Trees: the cross section of equilibrium volatility risk premia," LSE Research Online Documents on Economics 43091, London School of Economics and Political Science, LSE Library.
    8. Alain Kabundi & Tumisang Loate & Nicola Viegi, 2020. "Spillovers of the Conventional and Unconventional Monetary Policy from the US to South Africa," Working Papers 202033, University of Pretoria, Department of Economics.
    9. Ali Babikir & Henry Mwambi, 2016. "Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods," Empirical Economics, Springer, vol. 51(4), pages 1541-1556, December.
    10. Christian Menden & Christian R. Proaño, 2017. "Dissecting the financial cycle with dynamic factor models," IMK Working Paper 183-2017, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    11. Matteo Barigozzi & Marco Capasso, 2007. "A Multivariate Perspective for Modeling and Forecasting Inflation's Conditional Mean and Variance," LEM Papers Series 2007/21, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    12. Schwaab, Bernd & Koopman, Siem Jan & Lucas, André, 2012. "Dynamic factor models with macro, frailty and industry effects for US default counts: the credit crisis of 2008," Working Paper Series 1459, European Central Bank.
    13. Gao, Quansheng & Hu, Chengjun, 2009. "Dynamic mortality factor model with conditional heteroskedasticity," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 410-423, December.
    14. Fernando tenjo Galarza & Enrique López E. & Diego H. Rodríguez H., 2011. "El canal de préstamos de la política monetaria en Colombia. Un enfoque FAVAR," Borradores de Economia 684, Banco de la Republica de Colombia.
    15. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
    16. Dzhamilya Abuzyarova & Veronika Belousova & Zhaklin Krayushkina & Yulia Lonshcikova & Ekaterina Nikiforova & Nikolay Chichkanov, 2019. "The Role of Human Capital in Science, Technology and Innovation," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 13(2), pages 107-119.
    17. Pallara, Kevin, 2016. "The dynamic effects of government spending: a FAVAR approach," MPRA Paper 92283, University Library of Munich, Germany.
    18. Fernando Tenjo Galarza & Enrique López E. & Diego H. Rodríguez H., 2012. "El canal de préstamos de la política monetaria en Colombia. Un enfoque FAVAR," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 195-256, December.
    19. Mohan Subbiah & Frank J Fabozzi, 2016. "Equity style allocation: A nonparametric approach," Journal of Asset Management, Palgrave Macmillan, vol. 17(3), pages 141-164, May.

  38. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2008. "A review of nonfundamentalness and identification in structural VAR models," Working Paper Series 922, European Central Bank.

    Cited by:

    1. Paul Beaudry & Franck Portier, 2014. "News Driven Business Cycles: Insights and Challenges," 2014 Meeting Papers 289, Society for Economic Dynamics.
    2. Giorgio Fagiolo & Andrea Roventini, 2012. "Macroeconomic Policy in DSGE and Agent-Based Models," EconomiX Working Papers 2012-17, University of Paris Nanterre, EconomiX.
    3. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2010. "Dynamic Macroeconomic Effects of Public Capital: Evidence from Regional Italian Data," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 69(1), pages 29-66, April.
    4. G. Fagiolo & A. Roventini, 2009. "On the Scientific Status of Economic Policy: A Tale of Alternative Paradigms," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 6.
    5. Helmut Lütkepohl, 2012. "Fundamental Problems with Nonfundamental Shocks," Discussion Papers of DIW Berlin 1230, DIW Berlin, German Institute for Economic Research.
    6. Lanne, Markku & Saikkonen, Pentti, 2010. "Noncausal Vector Autoregression," MPRA Paper 23717, University Library of Munich, Germany.
    7. Fève, Patrick & Jidoud, Ahmat, 2012. "Identifying News Shocks from SVARs," TSE Working Papers 12-287, Toulouse School of Economics (TSE).
    8. Johannes Hermanus Kemp, 2020. "Empirical estimates of fiscal multipliers for South Africa," WIDER Working Paper Series wp-2020-91, World Institute for Development Economic Research (UNU-WIDER).
    9. Gianluca Cubadda & Francesco Giancaterini & Alain Hecq & Joann Jasiak, 2023. "Optimization of the Generalized Covariance Estimator in Noncausal Processes," Papers 2306.14653, arXiv.org, revised Jan 2024.

  39. Marco Capasso & Lucia Alessi & Matteo Barigozzi & Giorgio Fagiolo, 2007. "On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters," LEM Papers Series 2007/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

    Cited by:

    1. Matthias Duschl & Thomas Brenner, 2013. "Characteristics of regional industry-specific employment growth rates' distributions," Papers in Regional Science, Wiley Blackwell, vol. 92(2), pages 249-270, June.
    2. Giuricich, Mario Nicoló & Burnecki, Krzysztof, 2019. "Modelling of left-truncated heavy-tailed data with application to catastrophe bond pricing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 498-513.
    3. Giorgio Fagiolo & Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2010. "On the distributional properties of household consumption expenditures: the case of Italy," Empirical Economics, Springer, vol. 38(3), pages 717-741, June.
    4. Iván Blanco, Juan Ignacio Peña, and Rosa Rodriguez, 2018. "Modelling Electricity Swaps with Stochastic Forward Premium Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    5. Lunardi, José T. & Miccichè, Salvatore & Lillo, Fabrizio & Mantegna, Rosario N. & Gallegati, Mauro, 2014. "Do firms share the same functional form of their growth rate distribution? A statistical test," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 140-164.

  40. Giorgio Fagiolo & Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2007. "On the distributional properties of household consumption expenditures. The case of Italy," LEM Papers Series 2007/24, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

    Cited by:

    1. Schulz, Jan & Mayerhoffer, Daniel M., 2021. "A network approach to consumption," BERG Working Paper Series 173, Bamberg University, Bamberg Economic Research Group.
    2. Barigozzi, Matteo & Moneta, Alessio, 2016. "Identifying the independent sources of consumption variation," LSE Research Online Documents on Economics 60979, London School of Economics and Political Science, LSE Library.
    3. Ellis Scharfenaker, 2019. "Implications of Quantal Response Statistical Equilibrium," Working Paper Series, Department of Economics, University of Utah 2019_07, University of Utah, Department of Economics.
    4. Barigozzi, Matteo & Alessi, Lucia & Capasso, Marco & Fagiolo, Giorgio, 2012. "The distribution of household consumption-expenditure budget shares," Structural Change and Economic Dynamics, Elsevier, vol. 23(1), pages 69-91.
    5. Mundt, Philipp & Oh, Ilfan, 2019. "Asymmetric competition, risk, and return distribution," Economics Letters, Elsevier, vol. 179(C), pages 29-32.
    6. Alfarano, Simone & Milaković, Mishael & Irle, Albrecht & Kauschke, Jonas, 2012. "A statistical equilibrium model of competitive firms," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 136-149.
    7. Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2020. "Exploiting ergodicity in forecasts of corporate profitability," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    8. Chatterjee, Arnab & Chakrabarti, Anindya S. & Ghosh, Asim & Chakraborti, Anirban & Nandi, Tushar K., 2016. "Invariant features of spatial inequality in consumption: The case of India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 169-181.
    9. Matteo Barigozzi & Biagio Speciale, 2009. "Immigrant’s legal status, permanence in the destination country and the distribution of consumption expenditure," Working Papers ECARES 2009_019, ULB -- Universite Libre de Bruxelles.
    10. Matteo Barigozzi & Lucia Alessi & Marco Capasso & Giorgio Fagiolo, 2008. "The Distribution of Consumption-Expenditure Budget Shares. Evidence from Italian Households," LEM Papers Series 2008/18, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    11. David S. Bieri & Casey J. Dawkins, 2019. "Amenities, affordability, and housing vouchers," Journal of Regional Science, Wiley Blackwell, vol. 59(1), pages 56-82, January.

Articles

  1. Matteo Barigozzi & Lorenzo Trapani, 2022. "Testing for Common Trends in Nonstationary Large Datasets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1107-1122, June.

    Cited by:

    1. Morten {O}rregaard Nielsen & Won-Ki Seo & Dakyung Seong, 2023. "Inference on common trends in functional time series," Papers 2312.00590, arXiv.org, revised Dec 2023.
    2. Gianluca Cubadda & Marco Mazzali, 2023. "The Vector Error Correction Index Model: Representation, Estimation and Identification," CEIS Research Paper 556, Tor Vergata University, CEIS, revised 04 Apr 2023.

  2. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2021. "Time-varying general dynamic factor models and the measurement of financial connectedness," Journal of Econometrics, Elsevier, vol. 222(1), pages 324-343.
    See citations under working paper version above.
  3. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.

    Cited by:

    1. Chiara Casoli & Riccardo (Jack) Lucchetti, 2022. "Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices [Commodity-price comovement and global economic activity]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 494-514.
    2. Donato Ceci & Andrea Silvestrini, 2023. "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.
    3. Davide Brignone & Alessandro Franconi & Marco Mazzali, 2023. "Robust Impulse Responses using External Instruments: the Role of Information," Papers 2307.06145, arXiv.org.
    4. Hie Joo Ahn & Matteo Luciani, 2021. "Relative prices and pure inflation since the mid-1990s," Finance and Economics Discussion Series 2021-069, Board of Governors of the Federal Reserve System (U.S.).
    5. Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023. "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, vol. 26(C), pages 3-16.
    6. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    7. Haroon Mumtaz & Roman Sustek, 2023. "Global house prices since 1950," Discussion Papers 2307, Centre for Macroeconomics (CFM).
    8. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    9. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    10. Shahriyar Aliyev & Evžen Kočenda, 2023. "ECB monetary policy and commodity prices," Review of International Economics, Wiley Blackwell, vol. 31(1), pages 274-304, February.
    11. Mirela Sorina Miescu & Giorgio Motta & Dario Pontiggia & Raffaele Rossi, 2023. "The Expansionary Effects Of Housing Credit Supply Shocks," Working Papers 399832231, Lancaster University Management School, Economics Department.
    12. Cantore, Cristiano & Ferroni, Filippo & Mumtaz, Hroon & Theophilopoulou, Angeliki, 2022. "A tail of labour supply and a tale of monetary policy," Bank of England working papers 989, Bank of England.
    13. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    14. Gianluca Cubadda & Marco Mazzali, 2023. "The Vector Error Correction Index Model: Representation, Estimation and Identification," CEIS Research Paper 556, Tor Vergata University, CEIS, revised 04 Apr 2023.
    15. Takumah, Wisdom, 2023. "Fiscal Policy and Asset Prices in a Dynamic Factor Model with Cointegrated Factors," MPRA Paper 117897, University Library of Munich, Germany, revised 10 Jul 2023.

  4. Barigozzi, Matteo & Hallin, Marc, 2020. "Generalized dynamic factor models and volatilities: Consistency, rates, and prediction intervals," Journal of Econometrics, Elsevier, vol. 216(1), pages 4-34.
    See citations under working paper version above.
  5. Barigozzi, Matteo & Trapani, Lorenzo, 2020. "Sequential testing for structural stability in approximate factor models," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 5149-5187.
    See citations under working paper version above.
  6. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2020. "Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors," Econometrics, MDPI, vol. 8(1), pages 1-23, February.

    Cited by:

    1. Chiara Casoli & Riccardo (Jack) Lucchetti, 2022. "Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices [Commodity-price comovement and global economic activity]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 494-514.
    2. Sergej Gričar & Štefan Bojnec, 2021. "Technical Analysis of Tourism Price Process in the Eurozone," JRFM, MDPI, vol. 14(11), pages 1-25, October.
    3. Donato Ceci & Andrea Silvestrini, 2023. "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.
    4. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    5. Feng, Zongbao & Chen, Weiya & Liu, Yang & Chen, Hongyu & Skibniewski, Mirosław J., 2023. "Long-term equilibrium relationship analysis and energy-saving measures of metro energy consumption and its influencing factors based on cointegration theory and an ARDL model," Energy, Elsevier, vol. 263(PD).
    6. Lippi, Marco & Deistler, Manfred & Anderson, Brian, 2023. "High-Dimensional Dynamic Factor Models: A Selective Survey and Lines of Future Research," Econometrics and Statistics, Elsevier, vol. 26(C), pages 3-16.
    7. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    8. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    9. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    10. Tibor Szendrei & Katalin Varga, 2020. "FISS – A Factor-based Index of Systemic Stress in the Financial System," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 3-34, March.

  7. Mercedes Campi & Marco Dueñas & Matteo Barigozzi & Giorgio Fagiolo, 2019. "Intellectual property rights, imitation, and development. The effect on cross-border mergers and acquisitions," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 28(2), pages 230-256, February.
    See citations under working paper version above.
  8. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2019. "Identification of Global and Local Shocks in International Financial Markets via General Dynamic Factor Models," Journal of Financial Econometrics, Oxford University Press, vol. 17(3), pages 462-494.
    See citations under working paper version above.
  9. Matteo Barigozzi & Christian Brownlees, 2019. "NETS: Network estimation for time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 347-364, April.
    See citations under working paper version above.
  10. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 206(1), pages 187-225.
    See citations under working paper version above.
  11. Matteo Barigozzi & Antonio M. Conti, 2018. "On the Stability of Euro Area Money Demand and Its Implications for Monetary Policy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(4), pages 755-787, August.
    See citations under working paper version above.
  12. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    See citations under working paper version above.
  13. Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
    See citations under working paper version above.
  14. Matteo Barigozzi & Marc Hallin, 2016. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-60, February.
    See citations under working paper version above.
  15. Matteo Barigozzi & Alessio Moneta, 2016. "Identifying the Independent Sources of Consumption Variation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 420-449, March.
    See citations under working paper version above.
  16. Barigozzi, Matteo & Brownlees, Christian & Gallo, Giampiero M. & Veredas, David, 2014. "Disentangling systematic and idiosyncratic dynamics in panels of volatility measures," Journal of Econometrics, Elsevier, vol. 182(2), pages 364-384.
    See citations under working paper version above.
  17. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2014. "Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 693-714, October.
    See citations under working paper version above.
  18. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2013. "The common component of firm growth," Structural Change and Economic Dynamics, Elsevier, vol. 26(C), pages 73-82.

    Cited by:

    1. Stella, Andrea, 2015. "Firm dynamics and the origins of aggregate fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 71-88.
    2. Juan Federico & Joan-Lluis Capelleras, 2015. "The heterogeneous dynamics between growth and profits: the case of young firms," Small Business Economics, Springer, vol. 44(2), pages 231-253, February.
    3. Fornaro, Paolo, 2016. "Predicting Finnish economic activity using firm-level data," International Journal of Forecasting, Elsevier, vol. 32(1), pages 10-19.

  19. Barigozzi, Matteo & Alessi, Lucia & Capasso, Marco & Fagiolo, Giorgio, 2012. "The distribution of household consumption-expenditure budget shares," Structural Change and Economic Dynamics, Elsevier, vol. 23(1), pages 69-91.
    See citations under working paper version above.
  20. Matteo Barigozzi & Biagio Speciale, 2011. "Immigrants' legal status, permanence in the destination country and the distribution of consumption expenditure," Applied Economics Letters, Taylor & Francis Journals, vol. 18(14), pages 1341-1347.
    See citations under working paper version above.
  21. Barigozzi, Matteo & Fagiolo, Giorgio & Mangioni, Giuseppe, 2011. "Identifying the community structure of the international-trade multi-network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2051-2066.
    See citations under working paper version above.
  22. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2011. "Non‐Fundamentalness in Structural Econometric Models: A Review," International Statistical Review, International Statistical Institute, vol. 79(1), pages 16-47, April.

    Cited by:

    1. Berner, Anne & Bruns, Stephan B. & Moneta, Alessio & Stern, David I., 2021. "Do energy efficiency improvements reduce energy use? Empirical evidence on the economy-wide rebound effect in Europe and the United States," University of Göttingen Working Papers in Economics 422, University of Goettingen, Department of Economics.
    2. Alessi, Lucia & Kerssenfischer, Mark, 2016. "The response of asset prices to monetary policy shocks: stronger than thought," Working Paper Series 1967, European Central Bank.
    3. Weifeng Jin, 2023. "Quantile Autoregression-based Non-causality Testing," Papers 2301.02937, arXiv.org.
    4. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    5. Marco M. Sorge, 2013. "On the Fundamentalness of Nonfundamentalness in DSGE Models," CSEF Working Papers 340, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    6. Soccorsi, Stefano, 2016. "Measuring nonfundamentalness for structural VARs," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 86-101.
    7. Hecq, A.W. & Lieb, L.M. & Telg, J.M.A., 2015. "Identification of Mixed Causal-Noncausal Models : How Fat Should We Go?," Research Memorandum 035, Maastricht University, Graduate School of Business and Economics (GSBE).
    8. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    9. Mario Forni & Luca Gambetti & marco Lippi & Luca Sala, 2020. "Common Components Structural VARs," Center for Economic Research (RECent) 147, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    10. Pellényi, Gábor, 2012. "A monetáris politika hatása a magyar gazdaságra. Elemzés strukturális, dinamikus faktormodellel [The sectoral effects of monetary policy in Hungary: a structural factor]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 263-284.
    11. Angelini, Giovanni & Sorge, Marco M., 2021. "Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    12. Luca Fanelli & Marco M. Sorge, 2015. "Indeterminacy, Misspecification and Forecastability: Good Luck in Bad Policy?," CSEF Working Papers 402, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    13. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Francesco Giancaterini & Alain Hecq, 2020. "Inference in mixed causal and noncausal models with generalized Student's t-distributions," Papers 2012.01888, arXiv.org, revised Nov 2022.
    15. J. Baron & J. Schmidt, 2014. "Technological Standardization, Endogenous Productivity and Transitory Dynamics," Working papers 503, Banque de France.
    16. Lanne, Markku & Saikkonen, Pentti, 2010. "Noncausal Vector Autoregression," MPRA Paper 23717, University Library of Munich, Germany.
    17. Fève, Patrick & Jidoud, Ahmat, 2012. "Identifying News Shocks from SVARs," TSE Working Papers 12-287, Toulouse School of Economics (TSE).
    18. Lobato, Ignacio N. & Velasco, Carlos, 2018. "Efficiency improvements for minimum distance estimation of causal and invertible ARMA models," Economics Letters, Elsevier, vol. 162(C), pages 150-152.
    19. Ellahie, Atif & Ricco, Giovanni, 2017. "Government Purchases Reloaded : Informational Insufficiency and Heterogeneity in Fiscal VARs," Economic Research Papers 269308, University of Warwick - Department of Economics.
    20. Gourieroux, Christian & Jasiak, Joann, 2018. "Misspecification of noncausal order in autoregressive processes," Journal of Econometrics, Elsevier, vol. 205(1), pages 226-248.
    21. Nektarios A. Michail & Christos S. Savva & Demetris Koursaros, 2017. "Size Effects of Fiscal Policy and Business Confidence in the Euro Area," IJFS, MDPI, vol. 5(4), pages 1-15, November.
    22. Lof, Matthijs, 2013. "Essays on Expectations and the Econometrics of Asset Pricing," MPRA Paper 59064, University Library of Munich, Germany.
    23. Ricco, Giovanni & Ellahie, Atif, 2012. "Government Spending Reloaded: Fundamentalness and Heterogeneity in Fiscal SVARs," MPRA Paper 42105, University Library of Munich, Germany.
    24. Lof, Matthijs, 2011. "Noncausality and Asset Pricing," MPRA Paper 30519, University Library of Munich, Germany.
    25. Hecq, Alain & Issler, João Victor & Telg, Sean, 2017. "Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors," MPRA Paper 80767, University Library of Munich, Germany.
    26. Kang, Jihye & Kim, Soyoung, 2022. "Government spending news and surprise shocks: It’s the timing and persistence," Journal of Macroeconomics, Elsevier, vol. 73(C).
    27. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2013. "The common component of firm growth," Structural Change and Economic Dynamics, Elsevier, vol. 26(C), pages 73-82.
    28. Alessio Moneta & Doris Entner & Patrik O. Hoyer & Alex Coad, 2013. "Causal Inference by Independent Component Analysis: Theory and Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 705-730, October.
    29. Alain Hecq & Li Sun, 2019. "Identification of Noncausal Models by Quantile Autoregressions," Papers 1904.05952, arXiv.org.
    30. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.
    31. Hamidi Sahneh, Mehdi, 2015. "Are the shocks obtained from SVAR fundamental?," MPRA Paper 65126, University Library of Munich, Germany.
    32. Nikolay Iskrev, 2021. "Spectral decomposition of the information about latent variables in dynamic macroeconomic models," Working Papers w202105, Banco de Portugal, Economics and Research Department.
    33. Alain Hecq & Sean Telg & Lenard Lieb, 2017. "Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?," Econometrics, MDPI, vol. 5(4), pages 1-22, October.
    34. Paul Levine & Joseph Pearlman & Stephen Wright & Bo Yang, 2023. "Imperfect Information and Hidden Dynamics," School of Economics Discussion Papers 1223, School of Economics, University of Surrey.
    35. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers 21/13, Institute for Fiscal Studies.
    36. Martin Hodula & Lukas Pfeifer, 2018. "The Impact of Credit Booms and Economic Policy on Labour Productivity: A Sectoral Analysis," ACTA VSFS, University of Finance and Administration, vol. 12(1), pages 10-42.
    37. Nyholm, Juho, 2017. "Residual-based diagnostic tests for noninvertible ARMA models," MPRA Paper 81033, University Library of Munich, Germany.
    38. Fanelli, Luca & Sorge, Marco M., 2017. "Indeterminate forecast accuracy under indeterminacy," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 57-70.

  23. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.

    Cited by:

    1. Leu, Shawn C.-Y. & Robertson, Mari L., 2021. "Mortgage credit volumes and monetary policy after the Great Recession," Economic Modelling, Elsevier, vol. 94(C), pages 483-500.
    2. Alessi, Lucia & Kerssenfischer, Mark, 2016. "The response of asset prices to monetary policy shocks: stronger than thought," Working Paper Series 1967, European Central Bank.
    3. Matteo Barigozzi & Lorenzo Trapani, 2018. "Determining the dimension of factor structures in non-stationary large datasets," Papers 1806.03647, arXiv.org.
    4. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2013. "Forecasting the US Real Private Residential Fixed Investment Using Large Number of Predictors," Working Papers 201348, University of Pretoria, Department of Economics.
    5. Miranda Gualdrón, Karen Alejandra & Poncela, Pilar & Ruiz Ortega, Esther, 2021. "Dynamic factor models: does the specification matter?," DES - Working Papers. Statistics and Econometrics. WS 32210, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hallin, Marc & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Zevallos, Mauricio, 2019. "Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach," Textos para discussão 505, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    7. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    8. Igan, Deniz & Kabundi, Alain & De Simone, Francisco Nadal & Tamirisa, Natalia, 2017. "Monetary policy and balance sheets," Journal of Policy Modeling, Elsevier, vol. 39(1), pages 169-184.
    9. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    10. Onatski, Alexei, 2015. "Asymptotic analysis of the squared estimation error in misspecified factor models," Journal of Econometrics, Elsevier, vol. 186(2), pages 388-406.
    11. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    12. Mattia Guerini & Duc Thi Luu & Mauro Napoletano, 2019. "Synchronization Patterns in the European Union," GREDEG Working Papers 2019-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    13. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    14. Matteo Barigozzi & Daniele Massacci, 2022. "Modelling Large Dimensional Datasets with Markov Switching Factor Models," Papers 2210.09828, arXiv.org, revised Dec 2023.
    15. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
    16. Matteo Luciani & Libero Monteforte, 2012. "Uncertainty and Heterogeneity in factor models forecasting," Working Papers 5, Department of the Treasury, Ministry of the Economy and of Finance.
    17. Afanasyeva, Elena & Güntner, Jochen, 2014. "Lending standards, credit booms and monetary policy," IMFS Working Paper Series 85, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    18. Pietro Dallari & Antonio Ribba, 2015. "Dynamic Factor Models with In nite-Dimensional Factor Space: Asymptotic Analysis," Center for Economic Research (RECent) 115, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    19. Paccagnini, Alessia, 2019. "Did financial factors matter during the Great Recession?," Economics Letters, Elsevier, vol. 174(C), pages 26-30.
    20. Daniel Czarnowske & Amrei Stammann, 2020. "Inference in Unbalanced Panel Data Models with Interactive Fixed Effects," Papers 2004.03414, arXiv.org.
    21. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    22. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    23. Forni, Mario & Di Bonaventura, Luca & Pattarin, Francesco, 2018. "The Forcasting Performance of Dynamic Factor Models with Vintage Data," CEPR Discussion Papers 13034, C.E.P.R. Discussion Papers.
    24. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    25. Barigozzi, Matteo & Conti, Antonio & Luciani, Matteo, 2012. "Do Euro area countries respond asymmetrically to the common monetary policy?," LSE Research Online Documents on Economics 43344, London School of Economics and Political Science, LSE Library.
    26. Olivier Fortin-Gagnon & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2018. "A Large Canadian Database for Macroeconomic Analysis," CIRANO Working Papers 2018s-25, CIRANO.
    27. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
    28. Li, Hongjun & Li, Qi & Shi, Yutang, 2017. "Determining the number of factors when the number of factors can increase with sample size," Journal of Econometrics, Elsevier, vol. 197(1), pages 76-86.
    29. Fiorelli, Cristiana & Meliciani, Valentina, 2019. "Economic growth in the era of unconventional monetary instruments: A FAVAR approach," Journal of Macroeconomics, Elsevier, vol. 62(C).
    30. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    31. Mustafa Çakir & Alain Kabundi, 2017. "Transmission of China's Shocks to the BRIS Countries," South African Journal of Economics, Economic Society of South Africa, vol. 85(3), pages 430-454, September.
    32. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2019. "Expectations Anchoring Indexes for Brazil using Kalman Filter: exploring signals of inflation anchoring in the long term," Working Papers Series 497, Central Bank of Brazil, Research Department.
    33. Barigozzi, Matteo & Moneta, Alessio, 2016. "Identifying the independent sources of consumption variation," LSE Research Online Documents on Economics 60979, London School of Economics and Political Science, LSE Library.
    34. Jiti Gao & Guangming Pan & Yanrong Yang & Bo Zhang, 2019. "Estimation of Cross-Sectional Dependence in Large Panels," Papers 1904.06843, arXiv.org.
    35. Chou, Ray Yeutien & Yen, Tso-Jung & Yen, Yu-Min, 2020. "Macroeconomic forecasting using approximate factor models with outliers," International Journal of Forecasting, Elsevier, vol. 36(2), pages 267-291.
    36. Seung C. Ahn & Alex R. Horenstein, 2017. "Asset Pricing and Excess Returns over the Market Return," Working Papers 2017-12, University of Miami, Department of Economics.
    37. Claudia Godbout & Marco J. Lombardi, 2012. "Short-Term Forecasting of the Japanese Economy Using Factor Models," Staff Working Papers 12-7, Bank of Canada.
    38. Ling-Ni Boon & Florian Ielpo, 2016. "An anatomy of global risk premiums," Journal of Asset Management, Palgrave Macmillan, vol. 17(4), pages 229-243, July.
    39. Bai, Jushan & Liao, Yuan, 2012. "Efficient Estimation of Approximate Factor Models," MPRA Paper 41558, University Library of Munich, Germany.
    40. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    41. Xu Cheng & Zhipeng Liao & Frank Schorfheide, 2016. "Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(4), pages 1511-1543.
    42. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
    43. Adél Bosch & Franz Ruch, 2012. "An alternative business cycle dating procedure for South Africa," Working Papers 267, Economic Research Southern Africa.
    44. Karen Miranda & Pilar Poncela & Esther Ruiz, 2022. "Dynamic factor models: Does the specification matter?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 397-428, May.
    45. Sofiane Aboura & Julien Chevallier, 2015. "Cross-market volatility index with Factor-DCC," Post-Print halshs-01348723, HAL.
    46. Jorge Fornero & Markus Kirchner & Carlos Molina, 2021. "Estimating Shadow Policy Rates in a Small Open Economy and the Role of Foreign Factors," Working Papers Central Bank of Chile 915, Central Bank of Chile.
    47. Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Papers 2103.01201, arXiv.org.
    48. Bastian Gribisch, 2018. "A latent dynamic factor approach to forecasting multivariate stock market volatility," Empirical Economics, Springer, vol. 55(2), pages 621-651, September.
    49. Forni, Mario & Giovannelli, Alessandro & Lippi, Marco & Soccorsi, Stefano, 2016. "Dynamic Factor model with infinite dimensional factor space: forecasting," CEPR Discussion Papers 11161, C.E.P.R. Discussion Papers.
    50. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    51. Freyaldenhoven, Simon, 2022. "Factor models with local factors — Determining the number of relevant factors," Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
    52. Julien Chevallier & Florian Ielpo & Ling-Ni Boon, 2013. "Common risk factors in commodities," Economics Bulletin, AccessEcon, vol. 33(4), pages 2801-2816.
    53. Gloria Gonzalez-Rivera & Esther Ruiz & Javier Vicente, 2018. "Growth in Stress," Working Papers 201805, University of California at Riverside, Department of Economics.
    54. Nathan Bedock & Dalibor Stevanovic, 2012. "An Empirical Study of Credit Shock Transmission in a Small Open Economy," CIRANO Working Papers 2012s-16, CIRANO.
    55. Panagiotidis, Theodore & Printzis, Panagiotis, 2020. "What is the investment loss due to uncertainty?," Global Finance Journal, Elsevier, vol. 45(C).
    56. Nagayasu, Jun, 2015. "Global and country-specific factors in real effective exchange rates," MPRA Paper 64217, University Library of Munich, Germany.
    57. Richard D. F. Harris & Anh T. H. Nguyen, 2017. "Dynamic factor long memory volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1205-1221, August.
    58. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    59. Aboura, Sofiane & Chevallier, Julien, 2014. "Cross-market index with Factor-DCC," Economic Modelling, Elsevier, vol. 40(C), pages 158-166.
    60. Joakim Westerlund & Sagarika Mishra, 2017. "On the determination of the number of factors using information criteria with data-driven penalty," Statistical Papers, Springer, vol. 58(1), pages 161-184, March.
    61. Alessia Paccagnini, 2017. "Forecasting with FAVAR: macroeconomic versus financial factors," NBP Working Papers 256, Narodowy Bank Polski.
    62. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).
    63. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
    64. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    65. Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach," The World Economy, Wiley Blackwell, vol. 42(3), pages 846-875, March.
    66. Massacci, Daniele, 2017. "Least squares estimation of large dimensional threshold factor models," Journal of Econometrics, Elsevier, vol. 197(1), pages 101-129.
    67. Aramonte, Sirio & Giudice Rodriguez, Marius del & Wu, Jason, 2013. "Dynamic factor Value-at-Risk for large heteroskedastic portfolios," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4299-4309.
    68. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    69. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised Dec 2023.
    70. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    71. Mario Forni & Luca Gambetti & marco Lippi & Luca Sala, 2020. "Common Components Structural VARs," Center for Economic Research (RECent) 147, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    72. Oualid Bada & Alois Kneip & Dominik Liebl & Tim Mensinger & James Gualtieri & Robin C. Sickles, 2021. "A Wavelet Method for Panel Models with Jump Discontinuities in the Parameters," Papers 2109.10950, arXiv.org.
    73. Rangan Gupta & Faaiqa Hartley, 2011. "The Role of Asset Prices in Forecasting Inflation and Output in South Africa," Working Papers 201115, University of Pretoria, Department of Economics.
    74. Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "A diagnostic criterion for approximate factor structure," Papers 1612.04990, arXiv.org, revised Aug 2017.
    75. Simon Freyaldenhoven, 2017. "A Generalized Factor Model with Local Factors," 2017 Papers pfr361, Job Market Papers.
    76. Matteo Barigozzi & Lorenzo Trapani, 2018. "Sequential testing for structural stability in approximate factor models," Discussion Papers 18/04, University of Nottingham, Granger Centre for Time Series Econometrics.
    77. Gupta, Rangan & Modise, Mampho P., 2013. "Macroeconomic Variables and South African Stock Return Predictability," Economic Modelling, Elsevier, vol. 30(C), pages 612-622.
    78. Aboura, Sofiane & Chevallier, Julien, 2015. "A cross-volatility index for hedging the country risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 25-41.
    79. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    80. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2021. "Expecting the unexpected: economic growth under stress," Working Papers 202106, University of California at Riverside, Department of Economics.
    81. Luke Hartigan, 2015. "Changes in the Factor Structure of the U.S. Economy: Permanent Breaks or Business Cycle Regimes?," Discussion Papers 2015-17, School of Economics, The University of New South Wales.
    82. Choi, In & Lin, Rui & Shin, Yongcheol, 2023. "Canonical correlation-based model selection for the multilevel factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 22-44.
    83. Cheng Hsiao & Yimeng Xie & Qiankun Zhou, 2021. "Factor dimension determination for panel interactive effects models: an orthogonal projection approach," Computational Statistics, Springer, vol. 36(2), pages 1481-1497, June.
    84. Fan, Jianqing & Xue, Lingzhou & Yao, Jiawei, 2017. "Sufficient forecasting using factor models," Journal of Econometrics, Elsevier, vol. 201(2), pages 292-306.
    85. Juho Koistinen & Bernd Funovits, 2022. "Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization," Papers 2202.00310, arXiv.org, revised Feb 2022.
    86. Nagayasu, Jun, 2016. "Commonality and Heterogeneity in Real Effective Exchange Rates: Evidence from Advanced and Developing Countries," MPRA Paper 70078, University Library of Munich, Germany.
    87. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    88. Matteo Barigozzi & Marc Hallin, 2016. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-60, February.
    89. Andrew S. Duncan & Alain Kabundi, 2014. "Global Financial Crises and Time-Varying Volatility Comovement in World Equity Markets," South African Journal of Economics, Economic Society of South Africa, vol. 82(4), pages 531-550, December.
    90. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    91. Marc Hallin & Marco Lippi, 2013. "Factor Models in High-Dimensional Time Series: A Time-Domain Approach," Working Papers ECARES ECARES 2013-15, ULB -- Universite Libre de Bruxelles.
    92. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    93. Barigozzi, Matteo & Cho, Haeran & Fryzlewicz, Piotr, 2018. "Simultaneous multiple change-point and factor analysis for high-dimensional time series," LSE Research Online Documents on Economics 88110, London School of Economics and Political Science, LSE Library.
    94. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    95. Karen Poghosyan & Ruben Poghosyan, 2021. "On the Applicability of Dynamic Factor Models for Forecasting Real GDP Growth in Armenia," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 71(1), pages 52-79, June.
    96. Mingli Chen & Ivan Fernandez-Val & Martin Weidner, 2018. "Nonlinear factor models for network and panel data," CeMMAP working papers CWP38/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    97. Sofiane Aboura & Julien Chevallier, 2014. "The cross-market index for volatility surprise," Journal of Asset Management, Palgrave Macmillan, vol. 15(1), pages 7-23, February.
    98. Ouerk, Salima & Boucher, Christophe & Lubochinsky, Catherine, 2020. "Unconventional monetary policy in the Euro Area: Shadow rate and light effets," Journal of Macroeconomics, Elsevier, vol. 65(C).
    99. Marc Hallin & Marcelo Moreira J. & Alexei Onatski, 2013. "Group Invariance, Likelihood Ratio Tests, and the Incidental Parameter Problem in a High-Dimensional Linear Model," Working Papers ECARES ECARES 2013-04, ULB -- Universite Libre de Bruxelles.
    100. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    101. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    102. Tan, Ying & Sha, Wenbiao & Paudel, Krishna, 2017. "The Impact of Monetary Policy on Agricultural Price Index in China: A FAVAR Approach," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252676, Southern Agricultural Economics Association.
    103. Mehmet Caner & Xu Han, 2014. "Selecting the Correct Number of Factors in Approximate Factor Models: The Large Panel Case With Group Bridge Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 359-374, July.
    104. Franz Ruch & Mehmet Balcilar Author-Name-First Mehmet & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 15-08, Eastern Mediterranean University, Department of Economics.
    105. Lorenzo Boldrini & Eric Hillebrand, 2015. "Supervision in Factor Models Using a Large Number of Predictors," CREATES Research Papers 2015-38, Department of Economics and Business Economics, Aarhus University.
    106. Yu, Long & He, Yong & Zhang, Xinsheng, 2019. "Robust factor number specification for large-dimensional elliptical factor model," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    107. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    108. Javier Emmanuel Anguiano Pita & Antonio Ruiz Porras, 2020. "Market dynamics and integration of the financial markets of the NAFTA countries," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 92, pages 67-100, Enero-Jun.
    109. Forni, Mario & Cavicchioli, Maddalena & Lippi, Marco & Zaffaroni, Paolo, 2016. "Eigenvalue Ratio Estimators for the Number of Common Factors," CEPR Discussion Papers 11440, C.E.P.R. Discussion Papers.
    110. De Vos, Ignace & Everaert, Gerdie & Sarafidis, Vasilis, 2021. "A method for evaluating the rank condition for CCE estimators," MPRA Paper 112305, University Library of Munich, Germany, revised 09 Mar 2022.
    111. Carlos Vladimir Rodríguez-Caballero & Massimiliano Caporin, 2018. "A multilevel factor approach for the analysis of CDS commonality and risk contribution," CREATES Research Papers 2018-33, Department of Economics and Business Economics, Aarhus University.
    112. Jeroen V.K. Rombouts & Jakob Guldbæk Mikkelsen, 2017. "Testing for time-varying loadings in dynamic factor models," CREATES Research Papers 2017-22, Department of Economics and Business Economics, Aarhus University.
    113. Evžen Kočenda & Karen Poghosyan, 2020. "Nowcasting Real GDP Growth: Comparison between Old and New EU Countries," Eastern European Economics, Taylor & Francis Journals, vol. 58(3), pages 197-220, May.
    114. Alain Kabundi & Elmarie Nel & Franz Ruch, 2016. "Nowcasting Real GDP growth in South Africa," Working Papers 581, Economic Research Southern Africa.
    115. Dalibor Stevanovic & Charles Olivier Mao Takongmo, 2014. "Selection of the number of factors in presence of structural instability: a Monte Carlo study," CIRANO Working Papers 2014s-44, CIRANO.
    116. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," Working Papers ECARES ECARES 2011-019, ULB -- Universite Libre de Bruxelles.
    117. Aboura, Sofiane & Chevallier, Julien, 2015. "Geographical diversification with a World Volatility Index," Journal of Multinational Financial Management, Elsevier, vol. 30(C), pages 62-82.
    118. Yuefeng Han & Rong Chen & Cun-Hui Zhang, 2020. "Rank Determination in Tensor Factor Model," Papers 2011.07131, arXiv.org, revised May 2022.
    119. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    120. Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2013. "The common component of firm growth," Structural Change and Economic Dynamics, Elsevier, vol. 26(C), pages 73-82.
    121. Roman Matkovskyy, 2016. "A comparison of pre- and post-crisis efficiency of OECD countries: evidence from a model with temporal heterogeneity in time and unobservable individual effect," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 13(2), pages 135-167, December.
    122. Corona, Francisco & Orraca, Pedro, 2016. "Remittances in Mexico and their unobserved components," DES - Working Papers. Statistics and Econometrics. WS 22674, Universidad Carlos III de Madrid. Departamento de Estadística.
    123. Kyle E. Binder & Mohsen Pourahmadi & James W. Mjelde, 2020. "The role of temporal dependence in factor selection and forecasting oil prices," Empirical Economics, Springer, vol. 58(3), pages 1185-1223, March.
    124. Xia, Qiang & Liang, Rubing & Wu, Jianhong, 2017. "Transformed contribution ratio test for the number of factors in static approximate factor models," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 235-241.
    125. Nombulelo Gumata & Alain Kabundi & Eliphas Ndou, 2013. "Important channels of transmission of monetary policy shock in South Africa," Working Papers 6021, South African Reserve Bank.
    126. Yunus Emre Ergemen & Niels Haldrup & Carlos Vladimir Rodríguez-Caballero, 2015. "Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads," CREATES Research Papers 2015-58, Department of Economics and Business Economics, Aarhus University.
    127. Zakaria Moussa, 2016. "How big is the comeback? Japanese exchange rate pass-through assessed by Time-Varying FAVAR," Working Papers hal-01282811, HAL.
    128. Xu Lin & Lizi Wu, 2021. "Interdependence among mental health care providers: evidence from a spatial dynamic panel data model with interactive fixed effects," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(1), pages 131-165, August.
    129. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    130. Smets, Frank & Beyer, Robert C. M., 2015. "Labour market adjustments in Europe and the US: How different?," Working Paper Series 1767, European Central Bank.
    131. Alain Kabundi & Andrew S. Duncan, 2011. "Global Financial Crises and Time-varying Volatility Comovement in World Equity Markets," Working Papers 253, Economic Research Southern Africa.
    132. Chou, Ray Yeutien & Yen, Tso-Jung & Yen, Yu-Min, 2017. "Risk evaluations with robust approximate factor models," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 244-264.
    133. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    134. Kappler, Marcus & Schleer, Frauke, 2017. "A financially stressed euro area," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-37.
    135. Zakaria Moussa, 2016. "How big is the comeback? Japanese exchange rate pass-through assessed by time-varying FAVAR," Post-Print hal-03714934, HAL.
    136. Rangan Gupta, 2012. "Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment," Working Papers 201214, University of Pretoria, Department of Economics.
    137. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2011. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Working Papers 201122, University of Pretoria, Department of Economics.
    138. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Dynamic Factor Models, Cointegration, and Error Correction Mechanisms," Finance and Economics Discussion Series 2016-018, Board of Governors of the Federal Reserve System (U.S.).
    139. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
    140. Paolo Andreini & Donato Ceci, 2019. "A Horse Race in High Dimensional Space," CEIS Research Paper 452, Tor Vergata University, CEIS, revised 14 Feb 2019.
    141. Kevin Sheppard & Wen Xu, 2019. "Factor High-Frequency-Based Volatility (HEAVY) Models," Journal of Financial Econometrics, Oxford University Press, vol. 17(1), pages 33-65.
    142. Naik, Prasad A., 2015. "Marketing Dynamics: A Primer on Estimation and Control," Foundations and Trends(R) in Marketing, now publishers, vol. 9(3), pages 175-266, December.
    143. Duván Humberto Cataño & Carlos Vladimir Rodríguez-Caballero & Daniel Peña, 2019. "Wavelet Estimation for Dynamic Factor Models with Time-Varying Loadings," CREATES Research Papers 2019-23, Department of Economics and Business Economics, Aarhus University.
    144. Xingyu Li & Yan Shen & Qiankun Zhou, 2022. "Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects," Papers 2202.12078, arXiv.org.
    145. Yunus Emre Ergemen & Carlos Vladimir Rodríguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
    146. Aboura, Sofiane & Chevallier, Julien, 2017. "A new weighting-scheme for equity indexes," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 159-175.
    147. Sebastian Linde, 2023. "Hospital cost efficiency: an examination of US acute care inpatient hospitals," International Journal of Health Economics and Management, Springer, vol. 23(3), pages 325-344, September.
    148. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    149. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    150. Anthony N. Rezitis, 2015. "Empirical Analysis of Agricultural Commodity Prices, Crude Oil Prices and US Dollar Exchange Rates using Panel Data Econometric Methods," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 851-868.
    151. Alessandro Barbarino & Efstathia Bura, 2017. "A Unified Framework for Dimension Reduction in Forecasting," Finance and Economics Discussion Series 2017-004, Board of Governors of the Federal Reserve System (U.S.).
    152. Zhao Zhao & Guowei Cui & Shaoping Wang, 2017. "A Monte Carlo comparison of estimating the number of dynamic factors," Empirical Economics, Springer, vol. 53(3), pages 1217-1241, November.
    153. Marc Anderes, 2021. "Housing Demand Shocks and Households Balance Sheets," KOF Working papers 21-492, KOF Swiss Economic Institute, ETH Zurich.
    154. Jiti Gao & Guangming Pan & Yanrong Yang & Bo Zhang, 2019. "An Integrated Panel Data Approach to Modelling Economic Growth," Monash Econometrics and Business Statistics Working Papers 9/19, Monash University, Department of Econometrics and Business Statistics.
    155. Mouloud El Hafidi & Marouane Daoui, 2019. "Chocs de la politique monétaire et croissance économique au Maroc : une approche en terme de modèles FAVAR," Post-Print hal-03311354, HAL.
    156. Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.

  24. Giorgio Fagiolo & Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2010. "On the distributional properties of household consumption expenditures: the case of Italy," Empirical Economics, Springer, vol. 38(3), pages 717-741, June.
    See citations under working paper version above.
  25. Marco Capasso & Lucia Alessi & Matteo Barigozzi & Giorgio Fagiolo, 2009. "On Approximating The Distributions Of Goodness-Of-Fit Test Statistics Based On The Empirical Distribution Function: The Case Of Unknown Parameters," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 12(02), pages 157-167.
    See citations under working paper version above.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.