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Gergely Ganics

Personal Details

First Name:Gergely
Middle Name:
Last Name:Ganics
Suffix:
RePEc Short-ID:pga946
[This author has chosen not to make the email address public]
https://sites.google.com/view/gergelyganics/home

Affiliation

(75%) Magyar Nemzeti Bank (MNB)

Budapest, Hungary
http://www.mnb.hu/
RePEc:edi:mnbgvhu (more details at EDIRC)

(25%) Közgazdaságtudományi Kar
Budapesti Corvinus Egyetem

Budapest, Hungary
http://economics.uni-corvinus.hu/
RePEc:edi:bkeeehu (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "Constructing Fan Charts from the Ragged Edge of SPF Forecasts," Working Papers 22-36, Federal Reserve Bank of Cleveland.
  2. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "What is the Predictive Value of SPF Point and Density Forecasts?," Working Papers 22-37, Federal Reserve Bank of Cleveland.
  3. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
  4. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
  5. Gergely Ganics & Florens Odendahl, 2019. "Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area," Working papers 733, Banque de France.
  6. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence intervals for bias and size distortion in IV and local projections — IV models," Working Papers 1841, Banco de España.
  7. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.

Articles

  1. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
  2. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2021. "Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 307-324, January.
  3. Gergely Ganics & Eva Ortega, 2019. "Banco de España macroeconomic projections: comparison with an econometric model," Economic Bulletin, Banco de España, issue SEP.
  4. Gergely Ganics & Eva Ortega, 2019. "Las previsiones macroeconómicas del Banco de España a la luz de un modelo econométrico," Boletín Económico, Banco de España, issue SEP.

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. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.

    Cited by:

    1. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    2. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series 2436, European Central Bank.
    3. Clements, Michael P., 2021. "Rounding behaviour of professional macro-forecasters," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1614-1631.
    4. Manzan, Sebastiano, 2021. "Are professional forecasters Bayesian?," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    5. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.

  2. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. Dalhaus, Tatjana & Schaumburg, Julia & Sekhposyan, Tatevik, 2021. "Networking the yield curve: implications for monetary policy," Working Paper Series 2532, European Central Bank.
    2. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    3. Adrian, Tobias & Adams, Patrick & Boyarchenko, Nina & Giannone, Domenico, 2020. "Forecasting Macroeconomic Risks," CEPR Discussion Papers 14436, C.E.P.R. Discussion Papers.
    4. De Santis, Roberto A. & Van der Veken, Wouter, 2020. "Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions," Working Paper Series 2436, European Central Bank.
    5. Clements, Michael P., 2021. "Rounding behaviour of professional macro-forecasters," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1614-1631.
    6. Manzan, Sebastiano, 2021. "Are professional forecasters Bayesian?," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    7. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.

  3. Gergely Ganics & Florens Odendahl, 2019. "Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area," Working papers 733, Banque de France.

    Cited by:

    1. Bańbura, Marta & Leiva-León, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Discussion Papers 48/2021, Deutsche Bundesbank.
    2. Ashish Shrestha & Bishal Ghimire & Francisco Gonzalez-Longatt, 2021. "A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System," Energies, MDPI, vol. 14(11), pages 1-15, June.
    3. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    4. Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    5. Hauber, Philipp, 2021. "How useful is external information from professional forecasters? Conditional forecasts in large factor models," EconStor Preprints 251469, ZBW - Leibniz Information Centre for Economics.
    6. Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021. "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series 2543, European Central Bank.

  4. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence intervals for bias and size distortion in IV and local projections — IV models," Working Papers 1841, Banco de España.

    Cited by:

    1. Germano Ruisi, 2019. "Time-Varying Local Projections," Working Papers 891, Queen Mary University of London, School of Economics and Finance.
    2. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    3. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2018. "Confidence intervals for bias and size distortion in IV and local projections–IV models," Economics Working Papers 1640, Department of Economics and Business, Universitat Pompeu Fabra.
    4. Daniel J. Lewis & Karel Mertens, 2022. "A Robust Test for Weak Instruments with Multiple Endogenous Regressors," Staff Reports 1020, Federal Reserve Bank of New York.
    5. Barbara Rossi, 2019. "Identifying and Estimating the Effects of Unconventional Monetary Policy in the Data: How to Do It and What Have We Learned?," Working Papers 1081, Barcelona School of Economics.
    6. Rossi, Barbara, 2019. "Identifying and Estimating the Effects of Unconventional Monetary Policy: How to Do It And What Have We Learned?," CEPR Discussion Papers 14064, C.E.P.R. Discussion Papers.
    7. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "Assessing the strength of many instruments with the first-stage F and Cragg-Donald statistics," Papers 2302.14423, arXiv.org.

  5. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.

    Cited by:

    1. 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.
    2. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: obtaining measures of multi-horizon uncertainty from survey density forecasts," Working Papers 1947, Banco de España.
    3. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    4. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
    5. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    6. Graziano Moramarco, 2021. "Regime-Switching Density Forecasts Using Economists' Scenarios," Papers 2110.13761, arXiv.org, revised Feb 2024.
    7. Ruben Loaiza-Maya & Gael M Martin & David T. Frazier, 2020. "Focused Bayesian Prediction," Monash Econometrics and Business Statistics Working Papers 1/20, Monash University, Department of Econometrics and Business Statistics.

Articles

  1. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    See citations under working paper version above.
  2. Gergely Ganics & Atsushi Inoue & Barbara Rossi, 2021. "Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 307-324, January.
    See citations under working paper version above.Sorry, no citations of articles 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 10 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 (7) 2018-01-29 2019-11-04 2020-01-13 2020-01-13 2020-01-27 2020-02-03 2020-07-27. Author is listed
  2. NEP-ECM: Econometrics (6) 2018-01-29 2019-01-14 2020-01-13 2020-01-13 2022-12-19 2023-01-02. Author is listed
  3. NEP-ETS: Econometric Time Series (3) 2018-01-29 2019-01-14 2019-11-04. Author is listed
  4. NEP-MAC: Macroeconomics (3) 2019-11-04 2020-01-13 2020-01-27. Author is listed
  5. NEP-EEC: European Economics (2) 2019-11-04 2020-01-13
  6. NEP-ORE: Operations Research (2) 2020-01-13 2020-02-03

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