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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. 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.
    2. 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," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02315466, HAL.
    3. Lutz Kilian & Xiaoqing Zhou, 2017. "Modeling Fluctuations in the Global Demand for Commodities," CESifo Working Paper Series 6749, CESifo.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Jan Hagemejer & Aleksandra Hałka & Jacek Kotłowski, 2020. "Global value chains and exchange rate pass-through: the role of non-linearities," NBP Working Papers 324, Narodowy Bank Polski.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    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. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    2. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Post-Print hal-03647097, HAL.
    3. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 605-635.
    4. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    5. 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.
    6. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    7. 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.
    8. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    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. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    2. Kose, M. Ayhan & Sugawara, Naotaka & E. Terrones, Marco, 2020. "Global Recessions," CEPR Discussion Papers 14397, C.E.P.R. Discussion Papers.
    3. 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.
    4. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    5. Francesco Ravazzolo & Joaquin Vespignani, 2017. "World steel production: A new monthly indicator of global real economic activity," CAMA Working Papers 2017-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. 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.
    7. 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.
    8. Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers 2019-04, Center for Research in Economics and Statistics.
    9. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," Munich Reprints in Economics 78264, University of Munich, Department of Economics.
    10. Camacho, Maximo & Martinez-Martin, Jaime, 2015. "Monitoring the world business cycle," Economic Modelling, Elsevier, vol. 51(C), pages 617-625.
    11. 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.
    12. 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.
    13. Luke Hartigan & Tom Rosewall, 2024. "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," RBA Research Discussion Papers rdp2024-04, Reserve Bank of Australia.
    14. 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.
    15. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    16. Baumann, Ursel & Gomez-Salvador, Ramon & Seitz, Franz, 2019. "Detecting turning points in global economic activity," Working Paper Series 2310, European Central Bank.
    17. Ferrara , L. & Marsilli, C., 2016. "Nowcasting global economic growth," Rue de la Banque, Banque de France, issue 23, April..
    18. Chatelais, Nicolas & Stalla-Bourdillon, Arthur & Chinn, Menzie D., 2023. "Forecasting real activity using cross-sectoral stock market information," Journal of International Money and Finance, Elsevier, vol. 131(C).
    19. 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.
    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. 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).
    2. 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.
    3. 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.
    4. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
    5. 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.
    6. 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.
    7. Junttila, Juha & Vataja, Juuso, 2018. "Economic policy uncertainty effects for forecasting future real economic activity," Economic Systems, Elsevier, vol. 42(4), pages 569-583.
    8. 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.
    9. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    10. 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.
    11. Fatemeh Salimi Namin, 2020. "Exchange Rates, Stock Prices, and Stock Market Uncertainty," AMSE Working Papers 2037, Aix-Marseille School of Economics, France.
    12. 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.
    13. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank.
    14. 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.
    15. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    16. Laurent Ferrara & Stéphane Lhuissier & Fabien Tripier, 2018. "Uncertainty Fluctuations: Measures, Effects and Macroeconomic Policy Challenges," Financial and Monetary Policy Studies, in: Laurent Ferrara & Ignacio Hernando & Daniela Marconi (ed.), International Macroeconomics in the Wake of the Global Financial Crisis, pages 159-181, Springer.
    17. Grégory Levieuge, 2017. "Explaining and forecasting bank loans. Good times and crisis," Post-Print hal-03529226, HAL.
    18. 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.
    19. Fatemeh Salimi, 2020. "Exchange Rates, Stock Prices, and Stock Market Uncertainty," Working Papers halshs-03007904, HAL.
    20. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    21. 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.
    22. 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.
    23. Laine, Olli-Matti & Lindblad, Annika, 2020. "Nowcasting Finnish GDP growth using financial variables: a MIDAS approach," BoF Economics Review 4/2020, Bank of Finland.
    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. 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.
    2. 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.
    3. 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.
    4. Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2013. "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?," Working Papers hal-04141198, HAL.
    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. 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.
    7. Petralias, Athanassios & Petros, Sotirios & Prodromídis, Pródromos, 2013. "Greece in recession: economic predictions, mispredictions and policy implications," LSE Research Online Documents on Economics 52626, London School of Economics and Political Science, LSE Library.
    8. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    9. 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.
    10. 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.
    11. 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.
    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

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 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

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