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Marie Bessec

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.

    Cited by:

    1. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    2. Holmberg, Johan, 2021. "Earnings and Employment Dynamics: Capturing Cyclicality using Mixed Frequency Data," Umeå Economic Studies 991, Umeå University, Department of Economics.

  2. Marie Bessec & Julien Fouquau, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," Post-Print hal-01644930, HAL.

    Cited by:

    1. Christina C. Bartenschlager & Jens O. Brunner, 2019. "Reaching for the stars: attention to multiple testing problems and method recommendations using simulation for business research," Journal of Business Economics, Springer, vol. 89(4), pages 447-479, June.
    2. Vincenzo Loia & Stefania Tomasiello & Alfredo Vaccaro & Jinwu Gao, 2020. "Using local learning with fuzzy transform: application to short term forecasting problems," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 13-32, March.
    3. Yuri S. Popkov & Alexey Yu. Popkov & Yuri A. Dubnov & Dimitri Solomatine, 2020. "Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models," Mathematics, MDPI, vol. 8(7), pages 1-20, July.
    4. Wang, Chuang & Zhao, Haishen & Liu, Yang & Fan, Guojin, 2024. "Minute-level ultra-short-term power load forecasting based on time series data features," Applied Energy, Elsevier, vol. 372(C).
    5. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    6. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    7. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
    8. Mesbaholdin Salami & Farzad Movahedi Sobhani & Mohammad Sadegh Ghazizadeh, 2018. "Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market," Data, MDPI, vol. 3(4), pages 1-26, October.
    9. Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
    10. Zhou, Cheng & Chen, Xiyang, 2019. "Predicting energy consumption: A multiple decomposition-ensemble approach," Energy, Elsevier, vol. 189(C).
    11. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    12. Yajing Gao & Xiaojie Zhou & Jiafeng Ren & Zheng Zhao & Fushen Xue, 2018. "Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game," Energies, MDPI, vol. 11(5), pages 1-19, April.
    13. Daniel v{S}tifani'c & Jelena Musulin & Adrijana Miov{c}evi'c & Sandi Baressi v{S}egota & Roman v{S}ubi'c & Zlatan Car, 2020. "Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory," Papers 2007.02673, arXiv.org.
    14. Lintao Yang & Honggeng Yang & Haitao Liu, 2018. "GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting," Sustainability, MDPI, vol. 10(1), pages 1-16, January.
    15. Kailai Ni & Jianzhou Wang & Guangyu Tang & Danxiang Wei, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia," Energies, MDPI, vol. 12(13), pages 1-30, June.
    16. Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
    17. Miguel López & Sergio Valero & Carlos Sans & Carolina Senabre, 2020. "Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy," Energies, MDPI, vol. 14(1), pages 1-14, December.
    18. Ding, Jia & Wang, Maolin & Ping, Zuowei & Fu, Dongfei & Vassiliadis, Vassilios S., 2020. "An integrated method based on relevance vector machine for short-term load forecasting," European Journal of Operational Research, Elsevier, vol. 287(2), pages 497-510.
    19. Dai, Yeming & Yang, Xinyu & Leng, Mingming, 2022. "Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    20. Richard Bean, 2023. "Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge," Energies, MDPI, vol. 16(3), pages 1-23, January.
    21. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    22. Guo-Feng Fan & Li-Ling Peng & Xiangjun Zhao & Wei-Chiang Hong, 2017. "Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model," Energies, MDPI, vol. 10(11), pages 1-22, October.
    23. Wei, Nan & Yin, Lihua & Li, Chao & Wang, Wei & Qiao, Weibiao & Li, Changjun & Zeng, Fanhua & Fu, Lingdi, 2022. "Short-term load forecasting using detrend singular spectrum fluctuation analysis," Energy, Elsevier, vol. 256(C).
    24. Koch, Christopher & Hirth, Lion, 2019. "Short-term electricity trading for system balancing: An empirical analysis of the role of intraday trading in balancing Germany's electricity system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    25. Tayab, Usman Bashir & Lu, Junwei & Yang, Fuwen & AlGarni, Tahani Saad & Kashif, Muhammad, 2021. "Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach," Renewable Energy, Elsevier, vol. 180(C), pages 467-481.

  3. Kheira Benhami & Marie Bessec & Guillaume Gilquin, 2017. "Les tensions sur le marché du crédit de trésorerie en France dans une perspective historique," Post-Print hal-01645409, HAL.

    Cited by:

    1. Catherine Refait-Alexandre & Stéphanie Serve, 2020. "Multiple banking relationships: do SMEs mistrust their banks?," Post-Print hal-03141969, HAL.

  4. Marie Bessec & Julien Fouquau & Sophie Meritet, 2016. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Post-Print hal-01276807, HAL.

    Cited by:

    1. Marie Bessec & Julien Fouquau, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," Post-Print hal-01644930, HAL.
    2. Bartosz Uniejewski & Jakub Nowotarski & Rafal Weron, 2016. "Automated variable selection and shrinkage for day-ahead electricity price forecasting," HSC Research Reports HSC/16/06, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    3. Mohammad Nure Alam, 2021. "Accessing the Effect of Renewables on the Wholesale Power Market," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 341-360.
    4. Hryshchuk, Antanina & Lessmann, Stefan, 2018. "Deregulated day-ahead electricity markets in Southeast Europe: Price forecasting and comparative structural analysis," IRTG 1792 Discussion Papers 2018-009, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Caputo, Antonio C. & Federici, Alessandro & Pelagagge, Pacifico M. & Salini, Paolo, 2023. "Offshore wind power system economic evaluation framework under aleatory and epistemic uncertainty," Applied Energy, Elsevier, vol. 350(C).

  5. Marie Bessec & Catherine Doz, 2014. "Short-term forecasting of French GDP growth using dynamic factor models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01515602, HAL.

    Cited by:

    1. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2024. "Lessons from nowcasting GDP across the world," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 8, pages 187-217, Edward Elgar Publishing.
    2. Anastasia Mogilat & Oleg Kryzhanovskiy & Zhanna Shuvalova & Yaroslav Murashov, 2024. "DYFARUS: Dynamic Factor Model to Forecast GDP by Output Using Input-Output Tables," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 3-25, June.
    3. Danilo Cascaldi-Garcia & Thiago Revil T. Ferreira & Domenico Giannone & Michele Modugno, 2021. "Back to the Present: Learning about the Euro Area through a Now-casting Model," International Finance Discussion Papers 1313, Board of Governors of the Federal Reserve System (U.S.).
    4. Christophe Piette, 2016. "Predicting Belgium’s GDP using targeted bridge models," Working Paper Research 290, National Bank of Belgium.

  6. Bessec, M., 2012. "Short-term forecasts of French GDP: a dynamic factor model with targeted predictors," Working papers 409, Banque de France.

    Cited by:

    1. 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.
    2. Ademmer, Martin & Boysen-Hogrefe, Jens & Carstensen, Kai & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Rossian, Thies & Stolzenburg, Ulrich, 2019. "Schätzung von Produktionspotenzial und -lücke: Eine Analyse des EU-Verfahrens und mögliche Verbesserungen," Kieler Beiträge zur Wirtschaftspolitik 19, Kiel Institute for the World Economy (IfW Kiel).
    3. 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.
    4. Matteo Mogliani, 2019. "Bayesian MIDAS penalized regressions: estimation, selection, and prediction," Working papers 713, Banque de France.
    5. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    6. Dimitar EFTIMOSKI, 2019. "Improving Short-Term Forecasting of Macedonian GDP: Comparing the Factor Model with the Macroeconomic Structural Equation Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 32-53, June.
    7. Enrico D’Elia & Francesca Faedda & Giacomo Giannone, 2020. "Un modello statistico per il monitoraggio delle entrate tributarie (MoME)," Working Papers wp2020-5, Ministry of Economy and Finance, Department of Finance.
    8. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    9. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
    10. Christophe Piette, 2016. "Predicting Belgium’s GDP using targeted bridge models," Working Paper Research 290, National Bank of Belgium.
    11. Aysun, Uluc & Wright, Cardel, 2024. "A two-step dynamic factor modelling approach for forecasting inflation in small open economies," Emerging Markets Review, Elsevier, vol. 62(C).

  7. Bec, F. & Bessec, M., 2012. "Inventory Investment Dynamics and Recoveries: A Comparison of Manufacturing and Retail Trade Sectors," Working papers 400, Banque de France.

    Cited by:

    1. Jean Barthélemy & Magali Marx, 2012. "Generalizing the Taylor Principle: New Comment," Working Papers hal-03461113, HAL.

  8. Marie Bessec & Catherine Doz, 2012. "Prévision de court terme de la croissance du PIB français à l’aide de modèles à facteurs dynamiques," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01515627, HAL.

    Cited by:

    1. Zouri, Stéphane, 2020. "Business cycles, bilateral trade and financial integration: Evidence from Economic Community of West African States (ECOWAS)," International Economics, Elsevier, vol. 163(C), pages 25-43.
    2. Zouri, Stéphane, 2019. "Business cycles,bilateral trade and international financial intergration : Evidence from Economic Community of West African States (ECOWAS)," MPRA Paper 98748, University Library of Munich, Germany.
    3. Zouri, Stéphane, 2019. "Business cycles,bilateral trade and international financial intergration : Evidence from Economic Community of West African States (ECOWAS)," MPRA Paper 95275, University Library of Munich, Germany.
    4. 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).
    5. 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.

  9. Bessec, M. & Bouabdallah, O., 2012. "Forecasting GDP over the business cycle in a multi-frequency and data-rich environment," Working papers 384, Banque de France.

    Cited by:

    1. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    2. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    3. Zhang, Wei & He, Jie & Ge, Chanyuan & Xue, Rui, 2022. "Real-time macroeconomic monitoring using mixed frequency data: Evidence from China," Economic Modelling, Elsevier, vol. 117(C).
    4. Catherine Doz & Anna Petronevich, 2017. "On the consistency of the two-step estimates of the MS-DFM: a Monte Carlo study," PSE Working Papers halshs-01592863, HAL.
    5. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
    6. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    7. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    8. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    9. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    10. Catherine Doz & Anna Petronevich, 2016. "Dating Business Cycle Turning Points for the French Economy: An MS-DFM approach," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 481-538, Emerald Group Publishing Limited.
    11. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    12. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

  10. Marie Bessec & Julien Fouquau, 2008. "The non-linear link between electricity consumption and temperature in Europe: a threshold panel approach," Post-Print halshs-00222934, HAL.

    Cited by:

    1. Andres Gonzalez & Timo Terasvirta & Dick van Dijk, 2005. "Panel Smooth Transition Regression Models," Research Paper Series 165, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Peter Kudela & Tomas Havranek & Dominik Herman & Zuzana Irsova, 2019. "Does Daylight Saving Time Save Electricity? Evidence from Slovakia," Working Papers IES 2019/4, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2019.
    3. Blazquez Leticia & Nina Boogen & Massimo Filippini, 2012. "Residential electricity demand for Spain: new empirical evidence using aggregated data," CEPE Working paper series 12-82, CEPE Center for Energy Policy and Economics, ETH Zurich.
    4. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    5. Ge, Fei & Ye, Bin & Xing, Shengnan & Wang, Bao & Sun, Shuang, 2017. "The analysis of the underlying reasons of the inconsistent relationship between economic growth and the consumption of electricity in China – A case study of Anhui province," Energy, Elsevier, vol. 128(C), pages 601-608.
    6. Li, Muyuan & Yao, Jinfeng & Shen, Yanbo & Yuan, Bin & Simmonds, Ian & Liu, Yunyun, 2023. "Impact of synoptic circulation patterns on renewable energy-related variables over China," Renewable Energy, Elsevier, vol. 215(C).
    7. Chao Bi & Minna Jia & Jingjing Zeng, 2019. "Nonlinear Effect of Public Infrastructure on Energy Intensity in China: A Panel Smooth Transition Regression Approach," Sustainability, MDPI, vol. 11(3), pages 1-21, January.
    8. van der Wiel, K. & Stoop, L.P. & van Zuijlen, B.R.H. & Blackport, R. & van den Broek, M.A. & Selten, F.M., 2019. "Meteorological conditions leading to extreme low variable renewable energy production and extreme high energy shortfall," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 261-275.
    9. Petrick, Sebastian & Rehdanz, Katrin & Tol, Richard S. J., 2010. "The impact of temperature changes on residential energy consumption," Kiel Working Papers 1618, Kiel Institute for the World Economy (IfW Kiel).
    10. De Felice, Matteo & Alessandri, Andrea & Catalano, Franco, 2015. "Seasonal climate forecasts for medium-term electricity demand forecasting," Applied Energy, Elsevier, vol. 137(C), pages 435-444.
    11. Andreas Hefti & Peiyao Shen & King King Li, 2021. "Igniting deliberation in high stake decisions: a field study," ECON - Working Papers 378, Department of Economics - University of Zurich.
    12. Figueiredo, Nuno Carvalho & Silva, Patrícia Pereira da & Bunn, Derek, 2016. "Weather and market specificities in the regional transmission of renewable energy price effects," Energy, Elsevier, vol. 114(C), pages 188-200.
    13. Saehong Park & Seunghyoung Ryu & Yohwan Choi & Jihyo Kim & Hongseok Kim, 2015. "Data-Driven Baseline Estimation of Residential Buildings for Demand Response," Energies, MDPI, vol. 8(9), pages 1-21, September.
    14. Anne-Laure Delatte & Julien Fouquau, 2012. "What Drove the Massive Hoarding of International Reserves in Emerging Economies? A Time-Varying Approach," Post-Print hal-01410598, HAL.
    15. Do, Linh Phuong Catherine & Lin, Kuan-Heng & Molnár, Peter, 2016. "Electricity consumption modelling: A case of Germany," Economic Modelling, Elsevier, vol. 55(C), pages 92-101.
    16. Po-Chin Wu & Chung-Chih Lee, 2018. "The non-linear impact of monetary policy on international reserves: macroeconomic variables nexus," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(1), pages 165-185, February.
    17. Kahia, Montassar & Moulahi, Tarek & Mahfoudhi, Sami & Boubaker, Sabri & Omri, Anis, 2022. "A machine learning process for examining the linkage among disaggregated energy consumption, economic growth, and environmental degradation," Resources Policy, Elsevier, vol. 79(C).
    18. Doğan, Buhari & Ghosh, Sudeshna & Hoang, Dung Phuong & Chu, Lan Khanh, 2022. "Are economic complexity and eco-innovation mutually exclusive to control energy demand and environmental quality in E7 and G7 countries?," Technology in Society, Elsevier, vol. 68(C).
    19. Knaut, Andreas & Paulus, Simon, 2016. "When are consumers responding to electricity prices? An hourly pattern of demand elasticity," EWI Working Papers 2016-7, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), revised 16 Mar 2017.
    20. Duarte, Rosa & Pinilla, Vicente & Serrano, Ana, 2013. "Is there an environmental Kuznets curve for water use? A panel smooth transition regression approach," Economic Modelling, Elsevier, vol. 31(C), pages 518-527.
    21. Po-Chin Wu & Shiao-Yen Liu & Kou-Bau Wang, 2017. "Does Unemployment Matter for Lottery Sales and their Persistence? A New Estimation Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(2), pages 581-592, January.
    22. Jieyi Kang & David Reiner, 2021. "What is the effect of weather on household electricity consumption? Empirical evidence from Ireland," Working Papers EPRG2112, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    23. Franz Harke & Philipp Otto, 2023. "Solar Self-Sufficient Households as a Driving Factor for Sustainability Transformation," Sustainability, MDPI, vol. 15(3), pages 1-20, February.
    24. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
    25. Davinson Stev Abril Salcedo & Luis Fernando Melo-Velandia & Daniel Parra-Amado, 2019. "Nonlinear relationship between the weather phenomenon El Niño and Colombian food prices," Borradores de Economia 1085, Banco de la Republica de Colombia.
    26. Yabin Da & Bin Zeng & Jing-Li Fan & Jiawei Hu & Lanlan Li, 2023. "Heterogeneous responses to climate: evidence from residential electricity consumption," Climatic Change, Springer, vol. 176(8), pages 1-19, August.
    27. Fan, Jing-Li & Hu, Jia-Wei & Zhang, Xian, 2019. "Impacts of climate change on electricity demand in China: An empirical estimation based on panel data," Energy, Elsevier, vol. 170(C), pages 880-888.
    28. Aneta Wlodarczyk & Marcin Zawada, 2009. "The Use of Weather Variables in the Modeling of Demand for Electricity in One of the Regions in the Southern Poland," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 99-110.
    29. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    30. Rainald Borck, 2014. "Will skyscrapers save the planet?," ERSA conference papers ersa14p1342, European Regional Science Association.
    31. David Anthoff & Richard Tol, 2013. "The uncertainty about the social cost of carbon: A decomposition analysis using fund," Climatic Change, Springer, vol. 117(3), pages 515-530, April.
    32. Kani, Alireza H. & Abbasspour, Madjid & Abedi, Zahra, 2014. "Estimation of demand function for natural gas in Iran: Evidences based on smooth transition regression models," Economic Modelling, Elsevier, vol. 36(C), pages 341-347.
    33. Gupta, Eshita, 2012. "Global warming and electricity demand in the rapidly growing city of Delhi: A semi-parametric variable coefficient approach," Energy Economics, Elsevier, vol. 34(5), pages 1407-1421.
    34. Mulder, Machiel & Scholtens, Bert, 2013. "The impact of renewable energy on electricity prices in the Netherlands," Renewable Energy, Elsevier, vol. 57(C), pages 94-100.
    35. Falk, Martin & Lin, Xiang, 2018. "Sensitivity of winter tourism to temperature increases over the last decades," Economic Modelling, Elsevier, vol. 71(C), pages 174-183.
    36. Avraam, Charalampos & Ceferino, Luis & Dvorkin, Yury, 2023. "Operational and economy-wide impacts of compound cyber-attacks and extreme weather events on electric power networks," Applied Energy, Elsevier, vol. 349(C).
    37. Bašta, Milan & Helman, Karel, 2013. "Scale-specific importance of weather variables for explanation of variations of electricity consumption: The case of Prague, Czech Republic," Energy Economics, Elsevier, vol. 40(C), pages 503-514.
    38. Ibrahim Ahamada & Dramane Coulibaly, 2011. "How does financial development influence the impact of remittances on growth volatility?," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00629898, HAL.
    39. Po-Chin Wu & Shiao-Yen Liu & Ming-Fang Yang, 2017. "Nonlinear Exchange Rate Pass-Through: The Role of National Debt," Global Economic Review, Taylor & Francis Journals, vol. 46(1), pages 1-17, January.
    40. Meixuan Teng & Hua Liao & Paul J. Burke & Tianqi Chen & Chen Zhang, 2022. "Adaptive responses: the effects of temperature levels on residential electricity use in China," Climatic Change, Springer, vol. 172(3), pages 1-20, June.
    41. Chen, Haitao & Zhang, Bin & Liu, Hua & Cao, Jiguo, 2024. "The inequality in household electricity consumption due to temperature change: Data driven analysis with a function-on-function linear model," Energy, Elsevier, vol. 288(C).
    42. Huang, Junbing & Li, Xinghao & Wang, Yajun & Lei, Hongyan, 2021. "The effect of energy patents on China's carbon emissions: Evidence from the STIRPAT model," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    43. Marie Bessec & Julien Fouquau, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," Post-Print hal-01644930, HAL.
    44. Haben, Stephen & Giasemidis, Georgios & Ziel, Florian & Arora, Siddharth, 2019. "Short term load forecasting and the effect of temperature at the low voltage level," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1469-1484.
    45. Xiaosheng Li & Xia Yan & Qingxian An & Ke Chen & Zhen Shen, 2016. "The coordination between China’s economic growth and environmental emission from the Environmental Kuznets Curve viewpoint," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 233-252, August.
    46. Li, Jianglong & Yang, Lisha & Long, Houyin, 2018. "Climatic impacts on energy consumption: Intensive and extensive margins," Energy Economics, Elsevier, vol. 71(C), pages 332-343.
    47. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    48. Deng, Nana & Wang, Bo & Qiu, Yueming & Liu, Jie & Shi, Han & Zhang, Bin & Wang, Zhaohua, 2022. "The discrepancies in the impacts of COVID-19 lockdowns on electricity consumption in China: Is the short-term pain worth it?," Energy Economics, Elsevier, vol. 114(C).
    49. Yi, Fujin & Ye, Haijian & Wu, Ximing & Zhang, Y. Yvette & Jiang, Fei, 2020. "Self-aggravation effect of air pollution: Evidence from residential electricity consumption in China," Energy Economics, Elsevier, vol. 86(C).
    50. Anne-Laure Delatte & Julien Fouquau, 2011. "The determinants of International Reserves in the Emerging countries: a non linear approach," Post-Print hal-00822326, HAL.
    51. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org.
    52. Sulemana Mahawiya, 2015. "Financial sector development and threshold effect of inflation in ECOWAS and SADC: A Panel smooth transition regression approach," Working Papers 539, Economic Research Southern Africa.
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    91. Du, Kerui & Yu, Ying & Wei, Chu, 2020. "Climatic impact on China's residential electricity consumption: Does the income level matter?," China Economic Review, Elsevier, vol. 63(C).
    92. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
    93. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    94. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    95. Maren Diane Schmeck, 2016. "Pricing Options On Forwards In Energy Markets: The Role Of Mean Reversion'S Speed," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(08), pages 1-26, December.
    96. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
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  11. Marie Bessec & Othman Bouabdallah, 2005. "What causes the forecasting failure of Markov-Switching models? A Monte Carlo study," Econometrics 0503018, University Library of Munich, Germany.

    Cited by:

    1. Mahua Barari & Nityananda Sarkar & Srikanta Kundu & Kushal Banik Chowdhury, 2014. "Forecasting House Prices in the United States with Multiple Structural Breaks," International Econometric Review (IER), Econometric Research Association, vol. 6(1), pages 1-23, April.
    2. W. Miles, 2008. "Boom–Bust Cycles and the Forecasting Performance of Linear and Non-Linear Models of House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 36(3), pages 249-264, April.
    3. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    4. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 239-270, July.
    5. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    6. Katarzyna Maciejowska & Rafal Weron, 2015. "Short- and mid-term forecasting of baseload electricity prices in the UK: The impact of intra-day price relationships and market fundamentals," HSC Research Reports HSC/15/04, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    7. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," MPRA Paper 10428, University Library of Munich, Germany.
    8. Giordani, Paolo & Villani, Mattias, 2009. "Forecasting Macroeconomic Time Series With Locally Adaptive Signal Extraction," Working Paper Series 234, Sveriges Riksbank (Central Bank of Sweden).
    9. Huber, Florian & Pfarrhofer, Michael & Zörner, Thomas O., 2018. "Stochastic model specification in Markov switching vector error correction models," Working Papers in Economics 2018-3, University of Salzburg.
    10. Serinaldi, Francesco, 2011. "Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape," Energy Economics, Elsevier, vol. 33(6), pages 1216-1226.
    11. Adam Misiorek & Rafal Weron, 2006. "Interval forecasting of spot electricity prices," HSC Research Reports HSC/06/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    12. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number hsbook0601, December.
    13. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
    14. Hujer, Reinhard & Vuletic, Sandra, 2007. "Econometric analysis of financial trade processes by discrete mixture duration models," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 635-667, February.
    15. Brent Bundick, 2007. "Do federal funds futures need adjustment for excess returns? a state-dependent approach," Research Working Paper RWP 07-08, Federal Reserve Bank of Kansas City.

Articles

  1. Marie Bessec & Julien Fouquau, 2022. "Green Attention in Financial Markets: A Global Warning," Annals of Economics and Statistics, GENES, issue 148, pages 29-64.

    Cited by:

    1. Ahmed, Walid M.A., 2024. "Attention to climate change and eco-friendly financial-asset prices: A quantile ARDL approach," Energy Economics, Elsevier, vol. 136(C).
    2. Cavallo, Eduardo & Cepeda, Ana & Panizza, Ugo, 2024. "Environmental Damage News and Stock Returns: Evidence from Latin America," CEPR Discussion Papers 19154, C.E.P.R. Discussion Papers.

  2. Marie Bessec, 2019. "Revisiting the transitional dynamics of business cycle phases with mixed-frequency data," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 711-732, August.
    See citations under working paper version above.
  3. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
    See citations under working paper version above.
  4. Marie Bessec & Julien Fouquau & Sophie Meritet, 2016. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Applied Economics, Taylor & Francis Journals, vol. 48(5), pages 361-378, January.
    See citations under working paper version above.
  5. Marie Bessec & Othman Bouabdallah, 2015. "Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 360-384, June.
    See citations under working paper version above.
  6. Marie Bessec & Catherine Doz, 2014. "Short-term forecasting of French GDP growth using dynamic factor models," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 11-50.
    See citations under working paper version above.
  7. Frederique Bec & Marie Bessec, 2013. "Inventory Investment Dynamics and Recoveries: A Comparison of Manufacturing and Retail Trade Sectors," Economics Bulletin, AccessEcon, vol. 33(3), pages 2209-2222.
    See citations under working paper version above.
  8. Marie Bessec, 2013. "Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 500-511, September.
    See citations under working paper version above.
  9. Marie Bessec & Catherine Doz, 2012. "Prévision à court terme de la croissance du PIB français à l’aide de modèles à facteurs dynamiques," Économie et Prévision, Programme National Persée, vol. 199(1), pages 1-30.
    See citations under working paper version above.
  10. Marie Bessec, 2010. "Etalonnages du taux de croissance du PIB français sur la base des enquêtes de conjoncture," Economie & Prévision, La Documentation Française, vol. 0(2), pages 77-99.

    Cited by:

    1. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    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. Luboš Marek & Stanislava Hronová & Richard Hindls, 2019. "Možnosti odhadů krátkodobých makroekonomických agregátů na základě výsledků konjunkturních průzkumů [Possibilities of Estimations of Short-term Macroeconomic Aggregates Based on Business Survey Res," Politická ekonomie, Prague University of Economics and Business, vol. 2019(4), pages 347-370.

  11. Bessec, Marie & Fouquau, Julien, 2008. "The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach," Energy Economics, Elsevier, vol. 30(5), pages 2705-2721, September.
    See citations under working paper version above.
  12. Bessec Marie & Bouabdallah Othman, 2005. "What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-24, June.
    See citations under working paper version above.
  13. Marie Bessec & François-Mathieu Robineau, 2003. "Comportements chartistes et fondamentalistes. Coexistence ou domination alternative sur le marché des changes?," Revue économique, Presses de Sciences-Po, vol. 54(6), pages 1213-1238.

    Cited by:

    1. Georges Prat & Remzi Uctum, 2014. "Expectation formation in the foreign exchange market: a time-varying heterogeneity approach using survey data," Working Papers 2014-235, Department of Research, Ipag Business School.
    2. Olivier Damette & Stéphane Goutte, 2015. "Tobin tax and trading volume tightening: a reassessment," Post-Print hal-01203841, HAL.
    3. Georges Prat & Remzi Uctum, 2014. "Expectation formation in the foreign exchange market: a time-varying heterogeneity approach using survey data," Working Papers hal-04141348, HAL.

  14. Bessec, Marie, 2003. "Mean-reversion vs. adjustment to PPP: the two regimes of exchange rate dynamics under the EMS, 1979-1998," Economic Modelling, Elsevier, vol. 20(1), pages 141-164, January.

    Cited by:

    1. Jean-Louis Arcand & Max-Olivier Hongler & Shekhar Hari Kumar & Daniele Rinaldo, 2020. "Can one hear the shape of a target zone?," Papers 2002.00948, arXiv.org, revised Jun 2022.
    2. Mr. Bassem M Kamar & Jean-Etienne Carlotti & Mr. Russell C Krueger, 2009. "Establishing Conversion Values for New Currency Unions: Method and Application to the planned Gulf Cooperation Council (GCC) Currency Union," IMF Working Papers 2009/184, International Monetary Fund.
    3. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2013. "Is exchange rate – Customer order flow relationship linear? Evidence from the Hungarian FX market," Journal of International Money and Finance, Elsevier, vol. 35(C), pages 20-35.
    4. Frommel, Michael & MacDonald, Ronald & Menkhoff, Lukas, 2005. "Markov switching regimes in a monetary exchange rate model," Economic Modelling, Elsevier, vol. 22(3), pages 485-502, May.
    5. Frömmel, Michael & Schmidt, Torsten, 2006. "Bank Lending and Asset Prices in the Euro Area," Hannover Economic Papers (HEP) dp-342, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. J. Isaac Miller, 2008. "Testing the Bounds: Empirical Behavior of Target Zone Fundamentals," Working Papers 0803, Department of Economics, University of Missouri, revised 15 Apr 2009.

Chapters

    Sorry, no citations of chapters recorded.
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