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Fady Barsoum

Personal Details

First Name:Fady
Middle Name:
Last Name:Barsoum
Suffix:
RePEc Short-ID:pba1128
http://www.wiwi.uni-konstanz.de/ects/team/fady-barsoum/

Affiliation

Fachbereich Wirtschaftswissenschaften
Universität Konstanz

Konstanz, Germany
http://www.uni-konstanz.de/FuF/wiwi/
RePEc:edi:fwkonde (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. 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.
  2. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
  3. Fady Barsoum, 2013. "The Effects of Monetary Policy Shocks on a Panel of Stock Market Volatilities: A Factor-Augmented Bayesian VAR Approach," Working Paper Series of the Department of Economics, University of Konstanz 2013-15, Department of Economics, University of Konstanz.

Articles

  1. Barsoum, Fady & Stankiewicz, Sandra, 2015. "Forecasting GDP growth using mixed-frequency models with switching regimes," International Journal of Forecasting, Elsevier, vol. 31(1), pages 33-50.

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. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.

    Mentioned in:

    1. Last Week's Reading
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2013-06-04 00:35:00

Working papers

  1. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.

    Cited by:

    1. Afees A. Salisu & Rangan Gupta, 2019. "How do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch," Working Papers 201946, University of Pretoria, Department of Economics.
    2. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    3. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach," Working Papers 025, Centre for Econometric and Allied Research, University of Ibadan.
    4. 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.
    5. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    6. 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.
    7. Dušan Marković & Igor Mladenović & Miloš Milovančević, 2017. "RETRACTED ARTICLE: Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1133-1146, May.
    8. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
    9. 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.
    10. Jian Chai & Puju Cao & Xiaoyang Zhou & Kin Keung Lai & Xiaofeng Chen & Siping (Sue) Su, 2018. "The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data," Energies, MDPI, vol. 11(6), pages 1-14, May.
    11. Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    12. Afees A. Salisu & Rangan Gupta & Riza Demirer, 2020. "A Note on Uncertainty due to Infectious Diseases and Output Growth of the United States: A Mixed-Frequency Forecasting Experiment," Working Papers 202050, University of Pretoria, Department of Economics.
    13. Goran Maksimović & Srđan Jović & David Jovović & Marina Jovović, 2019. "RETRACTED ARTICLE: Analyses of Economic Development Based on Different Factors," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1103-1109, March.
    14. 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.
    15. Petra Karanikić & Igor Mladenović & Svetlana Sokolov-Mladenović & Meysam Alizamir, 2017. "RETRACTED ARTICLE: Prediction of economic growth by extreme learning approach based on science and technology transfer," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1395-1401, May.
    16. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    17. Lu, Fei & Ma, Feng & Hu, Shiyang, 2024. "Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting," Energy Economics, Elsevier, vol. 129(C).
    18. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Forecasting GDP with energy series: ADL-MIDAS vs. Linear Time Series Models," Working Papers 035, Centre for Econometric and Allied Research, University of Ibadan.
    19. Goodarzi, Milad & Meinerding, Christoph, 2023. "Asset allocation with recursive parameter updating and macroeconomic regime identifiers," Discussion Papers 06/2023, Deutsche Bundesbank.
    20. Marković, Dušan & Petković, Dalibor & Nikolić, Vlastimir & Milovančević, Miloš & Petković, Biljana, 2017. "Soft computing prediction of economic growth based in science and technology factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 217-220.
    21. 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.
    22. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    23. 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.
    24. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    25. 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.
    26. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    27. Đokić, Aleksandar & Jović, Srđan, 2017. "Evaluation of agriculture and industry effect on economic health by ANFIS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 396-399.
    28. Igor Mladenović & Miloš Milovančević & Svetlana Sokolov-Mladenović, 2017. "RETRACTED ARTICLE: Analyzing of innovations influence on economic growth by fuzzy system," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1297-1304, May.
    29. 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.
    30. Maksimović, Goran & Jović, Srđan & Jovanović, Radomir, 2017. "Economic growth rate management by soft computing approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 520-524.
    31. 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).
    32. 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.
    33. Holmberg, Johan, 2021. "Earnings and Employment Dynamics: Capturing Cyclicality using Mixed Frequency Data," Umeå Economic Studies 991, Umeå University, Department of Economics.

Articles

  1. Barsoum, Fady & Stankiewicz, Sandra, 2015. "Forecasting GDP growth using mixed-frequency models with switching regimes," International Journal of Forecasting, Elsevier, vol. 31(1), pages 33-50.
    See citations under working paper version above.Sorry, no citations of articles recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 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-ECM: Econometrics (2) 2013-05-19 2015-10-04
  2. NEP-ETS: Econometric Time Series (2) 2013-05-19 2015-10-04
  3. NEP-FOR: Forecasting (2) 2013-05-19 2015-10-04
  4. NEP-MAC: Macroeconomics (2) 2013-10-02 2015-10-04
  5. NEP-CBA: Central Banking (1) 2013-10-02
  6. NEP-MON: Monetary Economics (1) 2013-10-02

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