IDEAS home Printed from https://ideas.repec.org/f/pma1639.html
   My authors  Follow this author

Clément Marsilli
(Clement Marsilli)

Personal Details

First Name:Clement
Middle Name:
Last Name:Marsilli
Suffix:
RePEc Short-ID:pma1639
[This author has chosen not to make the email address public]
Banque de France International Macroeconomics Division 31 rue Croix des Petits-Champs F-75001 PARIS
33 1 42 97 77 11
Twitter: @clementmarsilli

Affiliation

Banque de France

Paris, France
http://www.banque-france.fr/
RePEc:edi:bdfgvfr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Denisa Georgiana Banulescu & Ferrara Laurent & Marsilli Clément, 2019. "Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences," Working Papers hal-03563168, HAL.
  2. Cabrillac, Bruno & Al-Haschimi, Alexander & Babecká Kucharčuková, Oxana & Borin, Alessandro & Bussière, Matthieu & Cezar, Raphael & Derviz, Alexis & Dimitropoulou, Dimitra & Ferrara, Laurent & Gächter, 2016. "Understanding the weakness in global trade - What is the new normal?," Occasional Paper Series 178, European Central Bank.
  3. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
  4. L. Ferrara & C. Marsilli, 2014. "Nowcasting global economic growth: A factor-augmented mixed-frequency approach," Working papers 515, Banque de France.
  5. Ferrara, L. & Marsilli, C. & Ortega, J-P., 2013. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Working papers 454, Banque de France.
  6. Laurent Ferrara & Clément Marsilli, 2012. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," EconomiX Working Papers 2012-19, University of Paris Nanterre, EconomiX.

Articles

  1. Rafaël Cezar & Rémy Lecat & Clément Marsilli & Floriane Van Den Hove, 2022. "Covid 19 crisis and capital outflows from emerging economies: global safety nets are effective, but need to be strengthened [Crise Covid 19 et sorties de capitaux dans les économies émergentes : de," Bulletin de la Banque de France, Banque de France, issue 239.
  2. Camille Fabre & Clément Marsilli, 2021. "Dette des pays émergents et en développement : panorama des années 1970 à la crise actuelle," Revue d'économie financière, Association d'économie financière, vol. 0(1), pages 23-44.
  3. Genre Véronique & Lecat Rémy & Marsilli Clément, 2020. "The euro in the history of the international monetary system [L’euro dans l’histoire du système monétaire international]," Bulletin de la Banque de France, Banque de France, issue 229.
  4. Bruno Cabrillac & Clément Marsilli & Sophie Rivaud, 2020. "De la libéralisation à la gestion des flux de capitaux internationaux," Revue d'économie financière, Association d'économie financière, vol. 0(1), pages 269-298.
  5. 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.
  6. Clément Marsilli, 2017. "Nowcasting US inflation using a MIDAS augmented Phillips curve," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 64-77.
  7. Ferrara , L. & Marsilli, C., 2016. "Nowcasting global economic growth," Rue de la Banque, Banque de France, issue 23, April..
  8. Ferrara, Laurent & Marsilli, Clément & Ortega, Juan-Pablo, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Economic Modelling, Elsevier, vol. 36(C), pages 44-50.
  9. Laurent Ferrara & Cl�ment Marsilli, 2013. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," Applied Economics Letters, Taylor & Francis Journals, vol. 20(3), pages 233-237, February.

Chapters

  1. Vincent Grossmann-Wirth & Clément Marsilli, 2018. "The Role of Debt Dynamics in US Household Consumption," Financial and Monetary Policy Studies, in: Laurent Ferrara & Ignacio Hernando & Daniela Marconi (ed.), International Macroeconomics in the Wake of the Global Financial Crisis, pages 115-128, Springer.

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. Ferrara, L. & Marsilli, C. & Ortega, J-P., 2013. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Working papers 454, Banque de France.

    Mentioned in:

    1. Guest Contribution: “Nowcasting Global GDP Growth”
      by Menzie Chinn in Econbrowser on 2015-03-12 09:56:18
  2. L. Ferrara & C. Marsilli, 2014. "Nowcasting global economic growth: A factor-augmented mixed-frequency approach," Working papers 515, Banque de France.

    Mentioned in:

    1. Guest Contribution: “Nowcasting Global GDP Growth”
      by Menzie Chinn in Econbrowser on 2015-03-12 09:56:18

Working papers

  1. Cabrillac, Bruno & Al-Haschimi, Alexander & Babecká Kucharčuková, Oxana & Borin, Alessandro & Bussière, Matthieu & Cezar, Raphael & Derviz, Alexis & Dimitropoulou, Dimitra & Ferrara, Laurent & Gächter, 2016. "Understanding the weakness in global trade - What is the new normal?," Occasional Paper Series 178, European Central Bank.

    Cited by:

    1. R. Cezar, 2016. "France’s pharmaceutical industry in global value chains," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 44, pages 52-63, Winter.
    2. Xuefeng Qian & Zhao Liu & Ying Pan, 2017. "China's Trade Slowdown: Cyclical or Structural?," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 25(6), pages 65-83, November.
    3. Bureau, B. & Bürker, M. & Libert, T., 2017. "La situation des entreprises en France en 2015," Bulletin de la Banque de France, Banque de France, issue 209, pages 39-55.
    4. Kilian, Lutz & Zhou, Xiaoqing, 2017. "Modeling Fluctuations in the Global Demand for Commodities," CEPR Discussion Papers 12357, C.E.P.R. Discussion Papers.
    5. Gächter, Martin & Gkrintzalis, Ioannis, 2017. "The finance–trade nexus revisited: Is the global trade slowdown also a financial story?," Economics Letters, Elsevier, vol. 158(C), pages 21-25.
    6. B. Cabrillac & L. Gauvin & J.-L. Gossé, 2016. "GDP-indexed bonds: what are the benefits for issuing countries, investors and international financial stability?," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 44, pages 6-19, Winter.
    7. Humbertclaude, S. & Monteil, F., 2017. "Le patrimoine économique national en 2015 : un modeste rebond," Bulletin de la Banque de France, Banque de France, issue 209, pages 5-14.
    8. Guillaume Gaulier & Aude Sztulman & Deniz Ünal, 2019. "Are global value chains receding? The jury is still out. Key findings from the analysis of deflated world trade in parts and components," Working Papers 2019-01, CEPII research center.
    9. Hagemejer, Jan & Hałka, Aleksandra & Kotłowski, Jacek, 2022. "Global value chains and exchange rate pass-through—The role of non-linearities," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 461-478.
    10. Campos, Rodolfo G. & Estefania-Flores, Julia & Furceri, Davide & Timini, Jacopo, 2023. "Geopolitical fragmentation and trade," Journal of Comparative Economics, Elsevier, vol. 51(4), pages 1289-1315.
    11. E. Buttin, 2016. "Green bonds: a solution for financing the energy transition or a simple buzzword?," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 44, pages 20-27, Winter.
    12. William F. Lincoln & Andrew H. McCallum & Michael Siemer, 2019. "The Great Recession and a Missing Generation of Exporters," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 67(4), pages 703-745, December.
    13. Rougès, D. & Strauss-Kahn, M.-O., 2017. "Sondage 2016 sur les Français et l’économie : comportements, préoccupations et attentes," Bulletin de la Banque de France, Banque de France, issue 209, pages 15-23.
    14. Gunnella, Vanessa & Al-Haschimi, Alexander & Benkovskis, Konstantins & Chiacchio, Francesco & de Soyres, François & Di Lupidio, Benedetta & Fidora, Michael & Franco-Bedoya, Sebastian & Frohm, Erik & G, 2019. "The impact of global value chains on the euro area economy," Occasional Paper Series 221, European Central Bank.
    15. C. Mazet-Sonilhac & J.-S. Mésonnier, 2016. "The cost of equity for large non-financial companies in the euro area: an estimation over the last decade," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 44, pages 28-39, Winter.
    16. A. Boileau & L. Carlino & A. S. Lafon, 2016. "In the first half of 2016, the main French groups increased their profitability," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 44, pages 40-51, Winter.
    17. Cezar, R., 2017. "L’industrie pharmaceutique française dans les chaînes de valeur mondiales," Bulletin de la Banque de France, Banque de France, issue 209, pages 57-69.
    18. Sondermann, David & Consolo, Agostino & Gunnella, Vanessa & Koester, Gerrit & Lambrias, Kyriacos & Lopez-Garcia, Paloma & Nerlich, Carolin & Petroulakis, Filippos & Saiz, Lorena & Serafini, Roberta, 2019. "Economic structures 20 years into the euro," Occasional Paper Series 224, European Central Bank.
    19. Boileau, A. & Chavy-Martin, A.-C., 2017. "Les délais de paiement sont stables en 2015," Bulletin de la Banque de France, Banque de France, issue 209, pages 25-38.

  2. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.

    Cited by:

    1. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    2. Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Reprints LFIN 2021010, Université catholique de Louvain, Louvain Finance (LFIN).
    3. O-Chia Chuang & Chenxu Yang, 2022. "Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model," Energies, MDPI, vol. 15(8), pages 1-14, April.
    4. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    5. Jardet Caroline & Meunier Baptiste, 2020. "Nowcasting World GDP Growth with High-Frequency Data," Working papers 788, Banque de France.
    6. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    7. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
    8. Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
    9. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
    10. Götz, T.B. & Hecq, A.W. & Smeekes, S., 2015. "Testing for Granger Causality in Large Mixed-Frequency VARs," Research Memorandum 036, Maastricht University, Graduate School of Business and Economics (GSBE).
    11. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    12. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
    13. Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022. "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, vol. 108(C).
    14. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.

  3. L. Ferrara & C. Marsilli, 2014. "Nowcasting global economic growth: A factor-augmented mixed-frequency approach," Working papers 515, Banque de France.

    Cited by:

    1. Martin Enilov, 2024. "The predictive power of commodity prices for future economic growth: Evaluating the role of economic development," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3040-3062, July.
    2. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    3. Luciano Campos & Danilo Leiva-León & Steven Zapata- Álvarez, 2022. "Latin American Falls, Rebounds and Tail Risks," Borradores de Economia 1201, Banco de la Republica de Colombia.
    4. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," NBER Working Papers 28014, National Bureau of Economic Research, Inc.
    5. M. Ayhan Kose & Naotaka Sugawara & Marco E. Terrones, 2020. "Global Recessions," Koç University-TUSIAD Economic Research Forum Working Papers 2002, Koc University-TUSIAD Economic Research Forum.
    6. Camacho, Maximo & Martinez-Martin, Jaime, 2015. "Monitoring the world business cycle," Economic Modelling, Elsevier, vol. 51(C), pages 617-625.
    7. Johanna Garnitz & Robert Lehmann & Klaus Wohlrabe, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," Applied Economics, Taylor & Francis Journals, vol. 51(54), pages 5802-5816, November.
    8. Jack Fosten & Shaoni Nandi, 2023. "Nowcasting from cross‐sectionally dependent panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 898-919, September.
    9. Heinisch, Katja & Lindner, Axel, 2018. "For how long do IMF forecasts of world economic growth stay up-to-date?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue Latest ar, pages 1-6.
    10. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print hal-03919944, HAL.
    11. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    12. Francesco Ravazzolo & Joaquin Vespignani, 2020. "World steel production: A new monthly indicator of global real economic activity," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(2), pages 743-766, May.
    13. Mahmut Gunay, 2018. "Nowcasting Annual Turkish GDP Growth with MIDAS," CBT Research Notes in Economics 1810, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    14. Michael Zhemkov, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, CEPII research center, issue 168, pages 10-24.
    15. Luke Hartigan & Tom Rosewall, 2024. "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," Working Papers 2024-15, University of Sydney, School of Economics.
    16. Nicolas Chatelais & Arthur Stalla-Bourdillon & Menzie Chinn, 2023. "Forecasting real activity using cross-sectoral stock market information," Post-Print hal-04459605, HAL.
    17. Stankevich, Ivan, 2020. "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 113-127.
    18. Baumann, Ursel & Gomez-Salvador, Ramon & Seitz, Franz, 2019. "Detecting turning points in global economic activity," Working Paper Series 2310, European Central Bank.
    19. Ferrara , L. & Marsilli, C., 2016. "Nowcasting global economic growth," Rue de la Banque, Banque de France, issue 23, April..
    20. Nicolas Chatelais & Arthur Stalla-Bourdillon & Menzie D. Chinn, 2022. "Macroeconomic Forecasting using Filtered Signals from a Stock Market Cross Section," NBER Working Papers 30305, National Bureau of Economic Research, Inc.
    21. Denisa BANULESCU-RADU & Laurent FERRARA & Clément MARSILLI, 2019. "Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences," LEO Working Papers / DR LEO 2710, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    22. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.

  4. Ferrara, L. & Marsilli, C. & Ortega, J-P., 2013. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Working papers 454, Banque de France.

    Cited by:

    1. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    2. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.
    3. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
    4. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    5. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    6. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    7. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    8. L. Ferrara & C. Marsilli, 2014. "Nowcasting global economic growth: A factor-augmented mixed-frequency approach," Working papers 515, Banque de France.
    9. Fatemeh Salimi Namin, 2020. "Exchange Rates, Stock Prices, and Stock Market Uncertainty," AMSE Working Papers 2037, Aix-Marseille School of Economics, France.
    10. Chauvet, Marcelle & Senyuz, Zeynep & Yoldas, Emre, 2010. "What does financial volatility tell us about macroeconomic fluctuations?," MPRA Paper 34104, University Library of Munich, Germany, revised Jun 2011.
    11. Laurent Ferrara & Stéphane Lhuissier & Fabien Tripier, 2017. "Uncertainty Fluctuations: Measures, Effects and Macroeconomic Policy Challenges," CEPII Policy Brief 2017-20, CEPII research center.
    12. Grégory Levieuge, 2017. "Explaining and forecasting bank loans. Good times and crisis," Post-Print hal-03529226, HAL.
    13. Cremers, Martijn & Fleckenstein, Matthias & Gandhi, Priyank, 2021. "Treasury yield implied volatility and real activity," Journal of Financial Economics, Elsevier, vol. 140(2), pages 412-435.
    14. Dorji, Karma Minjur Phuntsho, 2024. "Exploring Nowcasting Techniques for Real-Time GDP Estimation in Bhutan," MPRA Paper 121380, University Library of Munich, Germany, revised 30 Jun 2024.
    15. Laine, Olli-Matti & Lindblad, Annika, 2020. "Nowcasting Finnish GDP growth using financial variables: a MIDAS approach," BoF Economics Review 4/2020, Bank of Finland.
    16. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    17. Junttila, Juha & Vataja, Juuso, 2018. "Economic policy uncertainty effects for forecasting future real economic activity," Economic Systems, Elsevier, vol. 42(4), pages 569-583.
    18. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    19. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank.
    20. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    21. Fatemeh Salimi, 2020. "Exchange Rates, Stock Prices, and Stock Market Uncertainty," Working Papers halshs-03007904, HAL.
    22. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    23. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    24. Stefan Gebauer, 2017. "The Use of Financial Market Variables in Forecasting," DIW Roundup: Politik im Fokus 115, DIW Berlin, German Institute for Economic Research.
    25. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.

  5. Laurent Ferrara & Clément Marsilli, 2012. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," EconomiX Working Papers 2012-19, University of Paris Nanterre, EconomiX.

    Cited by:

    1. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    2. Mittal, Amit & Garg, Ajay Kumar, 2021. "Bank stocks inform higher growth—A System GMM analysis of ten emerging markets in Asia," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 210-220.
    3. Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Post-Print hal-01385941, HAL.
    4. L. Ferrara & C. Marsilli, 2014. "Nowcasting global economic growth: A factor-augmented mixed-frequency approach," Working papers 515, Banque de France.
    5. Mittal, Amit & Garg, Ajay Kumar, 2018. "Bank stocks inform higher growth – A System GMM analysis of ten emerging markets in Asia," MPRA Paper 98253, University Library of Munich, Germany.
    6. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    7. Athanassios Petralias & Sotirios Petros & Pródromos Prodromídis, 2013. "Greece in Recession: Economic predictions, mispredictions and policy implications," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 75, Hellenic Observatory, LSE.
    8. Deimante Teresiene & Greta Keliuotyte-Staniuleniene & Yiyi Liao & Rasa Kanapickiene & Ruihui Pu & Siyan Hu & Xiao-Guang Yue, 2021. "The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators," JRFM, MDPI, vol. 14(4), pages 1-23, April.
    9. Morita, Hiroshi, 2022. "Forecasting GDP growth using stock returns in Japan: A factor-augmented MIDAS approach," Discussion paper series HIAS-E-118, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    10. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    11. Özgür Ömer Ersin & Melike Bildirici, 2023. "Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19," Mathematics, MDPI, vol. 11(8), pages 1-26, April.
    12. Gong, Yuting & Chen, Qiang & Liang, Jufang, 2018. "A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets," Economic Modelling, Elsevier, vol. 68(C), pages 586-598.
    13. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.

Articles

  1. 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.
    See citations under working paper version above.
  2. Clément Marsilli, 2017. "Nowcasting US inflation using a MIDAS augmented Phillips curve," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 64-77.

    Cited by:

    1. Xindi Wang & Zeshui Xu & Xinxin Wang & Marinko Skare, 2022. "A review of inflation from 1906 to 2022: a comprehensive analysis of inflation studies from a global perspective," Oeconomia Copernicana, Institute of Economic Research, vol. 13(3), pages 595-631, September.
    2. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    3. Barış Soybilgen & M. Ege Yazgan & Hüseyin Kaya, 2023. "Nowcasting Turkish Food Inflation Using Daily Online Prices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 171-190, September.

  3. Ferrara, Laurent & Marsilli, Clément & Ortega, Juan-Pablo, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Economic Modelling, Elsevier, vol. 36(C), pages 44-50.
    See citations under working paper version above.
  4. Laurent Ferrara & Cl�ment Marsilli, 2013. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," Applied Economics Letters, Taylor & Francis Journals, vol. 20(3), pages 233-237, February.
    See citations under working paper version above.

Chapters

    Sorry, no citations of chapters recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 6 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FOR: Forecasting (4) 2012-04-17 2013-07-15 2013-10-25 2014-12-19
  2. NEP-MAC: Macroeconomics (3) 2013-10-25 2014-11-22 2014-12-19
  3. NEP-ECM: Econometrics (1) 2014-12-19
  4. NEP-ETS: Econometric Time Series (1) 2014-12-19
  5. NEP-INT: International Trade (1) 2016-10-02

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Clement Marsilli
(Clement Marsilli) should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.