Emmanouil Sofianos

Personal Details

First Name:	Emmanouil
Middle Name:
Last Name:	Sofianos
Suffix:
RePEc Short-ID:	pso695
	[This author has chosen not to make the email address public]
	http://esofianos.gr/

Affiliation

Bureau d'Économie Théorique et Appliquée (BETA)

Nancy/Strasbourg, France
https://www.beta-economics.fr/
RePEc:edi:bestrfr (more details at EDIRC)

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. Gogas, Periklis & Papadimitriou, Theophilos & Sofianos, Emmanouil, 2019. "Money Neutrality, Monetary Aggregates and Machine Learning," DUTH Research Papers in Economics 4-2016, Democritus University of Thrace, Department of Economics.

   Cited by:

   1. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
   2. Lu, Yao & Zhao, Zhihui & Tian, Yuan & Zhan, Minghua, 2024. "How does the economic structure break change the forecast effect of money and credit on output? Evidence based on machine learning algorithms," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
   3. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
   4. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.

Articles

1. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.

   Cited by:

   1. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
   2. Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
   3. Sanusi, Olajide I. & Safi, Samir K. & Adeeko, Omotara & Tabash, Mosab I., 2022. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(2), June.
   4. Kea Baret & Amelie Barbier-Gauchard & Theophilos Papadimitriou, 2022. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers 2022.11, International Network for Economic Research - INFER.
      • Kea Baret & Amélie Barbier-Gauchard & Theophilos Papadimitriou, 2023. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Post-Print hal-03121966, HAL.
      • Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
   5. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
   6. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
   7. Clément Cariou & Amélie Charles & Olivier Darné, 2024. "Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2341-2357, September.
   8. Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
   9. Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).

2. Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2022. "Mind the gap: forecasting euro-area output gaps with machine learning," Applied Economics Letters, Taylor & Francis Journals, vol. 29(19), pages 1824-1828, November.

   Cited by:

   1. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
   2. Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).

3. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.

   Cited by:

   1. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
   2. Sun-Feel Yang & So-Won Choi & Eul-Bum Lee, 2023. "A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices," Energies, MDPI, vol. 16(11), pages 1-39, May.
   3. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Mingyue Wang, 2023. "A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network," Energies, MDPI, vol. 16(5), pages 1-15, February.
   4. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
   5. Periklis Gogas & Theophilos Papadimitriou, 2022. "Emerging Trends in Energy Economics," Energies, MDPI, vol. 15(14), pages 1-2, July.

More information

Research fields, statistics, top rankings, if available.

Statistics

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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 1 paper 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-BIG: Big Data (1) 2019-11-18. Author is listed
2. NEP-CBA: Central Banking (1) 2019-11-18. Author is listed
3. NEP-CMP: Computational Economics (1) 2019-11-18. Author is listed
4. NEP-EEC: European Economics (1) 2019-11-18. Author is listed
5. NEP-FOR: Forecasting (1) 2019-11-18. Author is listed
6. NEP-MAC: Macroeconomics (1) 2019-11-18. Author is listed
7. NEP-MON: Monetary Economics (1) 2019-11-18. Author is listed

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