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Geert Mesters

Personal Details

First Name:Geert
Middle Name:
Last Name:Mesters
Suffix:
RePEc Short-ID:pme642
http://www.geertmesters.nl
Terminal Degree:2015 Afdeling Econometrie and Operations Research; School of Business and Economics; Vrije Universiteit Amsterdam (from RePEc Genealogy)

Affiliation

Departament d'Economia i Empresa
Universitat Pompeu Fabra
Barcelona Graduate School of Economics (Barcelona GSE)

Barcelona, Spain
http://www.econ.upf.edu/

: (34) 935 42 1766
(34)935 42 17 46
Ramon Trias Fargas 25-27, 08005 Barcelona
RePEc:edi:deupfes (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Régis Barnichon & Geert Mesters, 2019. "Identifying modern macro equations with old shocks," Economics Working Papers 1659, Department of Economics and Business, Universitat Pompeu Fabra.
  2. Régis Barnichon & Geert Mesters, 2019. "The Phillips multiplier," Economics Working Papers 1632, Department of Economics and Business, Universitat Pompeu Fabra.
  3. Christian Brownlees & Geert Mesters, 2017. "Detecting Granular Time Series in Large Panels," Working Papers 991, Barcelona Graduate School of Economics.
  4. Geert Mesters & Bernd Schwaab & Siem Jan Koopman, 2014. "A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area," Tinbergen Institute Discussion Papers 14-071/III, Tinbergen Institute.
  5. Geert Mesters & Victor van der Geest & Catrien Bijleveld, 2014. "Crime, Employment and Social Welfare: an Individual-level Study on Disadvantaged Males," Tinbergen Institute Discussion Papers 14-091/III, Tinbergen Institute.
  6. Siem Jan Koopman & Geert Mesters, 2014. "Empirical Bayes Methods for Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-061/III, Tinbergen Institute.
  7. Geert Mesters & Siem Jan Koopman, 2012. "Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time," Tinbergen Institute Discussion Papers 12-009/4, Tinbergen Institute, revised 18 Mar 2014.
  8. Geert Mesters & Siem Jan Koopman, 2012. "A Forty Year Assessment of Forecasting the Boat Race," Tinbergen Institute Discussion Papers 12-110/III, Tinbergen Institute.
  9. Geert Mesters & Siem Jan Koopman & Marius Ooms, 2011. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Tinbergen Institute Discussion Papers 11-090/4, Tinbergen Institute.

Articles

  1. Regis Barnichon & Geert Mesters, 2018. "On the Demographic Adjustment of Unemployment," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 219-231, May.
  2. Geert Mesters & Regis Barnichon, 2017. "How Tight Is the U.S. Labor Market?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
  3. S. J. Koopman & G. Mesters, 2017. "Empirical Bayes Methods for Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 486-498, July.
  4. G. Mesters & S. J. Koopman & M. Ooms, 2016. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 659-687, April.
  5. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.

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. Régis Barnichon & Geert Mesters, 2019. "Identifying modern macro equations with old shocks," Economics Working Papers 1659, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. David Finck & Peter Tillmann, 2019. "The Role of Global and Domestic Shocks for Inflation Dynamics: Evidence from Asia," GRU Working Paper Series GRU_2019_022, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    2. Michael McLeay & Silvana Tenreyro, 2018. "Optimal Inflation and the Identification of the Phillips Curve," Discussion Papers 1815, Centre for Macroeconomics (CFM).
    3. Matthew Rognlie, 2019. "Comment on "Optimal Inflation and the Identification of the Phillips Curve"," NBER Chapters, in: NBER Macroeconomics Annual 2019, volume 34, National Bureau of Economic Research, Inc.

  2. Régis Barnichon & Geert Mesters, 2019. "The Phillips multiplier," Economics Working Papers 1632, Department of Economics and Business, Universitat Pompeu Fabra.

    Cited by:

    1. Aguiar-Conraria, Luís & Martins, Manuel M. F. & Soares, Maria Joana, 2019. "The Phillips Curve at 60: time for time and frequency," Research Discussion Papers 12/2019, Bank of Finland.
    2. Maurice Obstfeld, 2019. "Global Dimensions of U.S. Monetary Policy," NBER Working Papers 26039, National Bureau of Economic Research, Inc.

  3. Christian Brownlees & Geert Mesters, 2017. "Detecting Granular Time Series in Large Panels," Working Papers 991, Barcelona Graduate School of Economics.

    Cited by:

    1. George Kapetanios & M. Hashem Pesaran & Simon Reese, 2018. "A Residual-based Threshold Method for Detection of Units that are Too Big to Fail in Large Factor Models," CESifo Working Paper Series 7401, CESifo Group Munich.

  4. Geert Mesters & Bernd Schwaab & Siem Jan Koopman, 2014. "A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area," Tinbergen Institute Discussion Papers 14-071/III, Tinbergen Institute.

    Cited by:

    1. Trebesch, Christoph & Zettelmeyer, Jeromin, 2018. "ECB interventions in distressed sovereign debt markets: The case of Greek bonds," Kiel Working Papers 2101, Kiel Institute for the World Economy (IfW).
    2. Chamon, Marcos & Schumacher, Julian & Trebesch, Christoph, 2018. "Foreign-law bonds: can they reduce sovereign borrowing costs?," Working Paper Series 2162, European Central Bank.
    3. Recchioni, Maria Cristina & Tedeschi, Gabriele, 2017. "From bond yield to macroeconomic instability: A parsimonious affine model," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1116-1135.
    4. Pelizzon, Loriana & Subrahmanyam, Marti G. & Tomio, Davide & Uno, Jun, 2016. "Sovereign credit risk, liquidity, and European Central Bank intervention: Deus ex machina?," Journal of Financial Economics, Elsevier, vol. 122(1), pages 86-115.
    5. Marcos Chamon & Julian Schumacher & Christoph Trebesch, 2018. "Foreign-Law Bonds: Can They Reduce Sovereign Borrowing Costs?," CESifo Working Paper Series 7137, CESifo Group Munich.
    6. Eser, Fabian & Schwaab, Bernd, 2016. "Evaluating the impact of unconventional monetary policy measures: Empirical evidence from the ECB׳s Securities Markets Programme," Journal of Financial Economics, Elsevier, vol. 119(1), pages 147-167.
    7. Maria Cristina Recchioni & Gabriele Tedeschi, 2016. "From bond yield to macroeconomic instability: The effect of negative interest rates," Working Papers 2016/06, Economics Department, Universitat Jaume I, Castellón (Spain).

  5. Siem Jan Koopman & Geert Mesters, 2014. "Empirical Bayes Methods for Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-061/III, Tinbergen Institute.

    Cited by:

    1. James Sampi, 2016. "High Dimensional Factor Models: An Empirical Bayes Approach," Working Papers 2016-75, Peruvian Economic Association.
    2. Falk Bräuning & Siem Jan Koopman, 2016. "The Dynamic Factor Network Model with an Application to Global Credit-Risk," Tinbergen Institute Discussion Papers 16-105/III, Tinbergen Institute.

  6. Geert Mesters & Siem Jan Koopman, 2012. "Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time," Tinbergen Institute Discussion Papers 12-009/4, Tinbergen Institute, revised 18 Mar 2014.

    Cited by:

    1. Siem Jan Koopman & Geert Mesters, 2014. "Empirical Bayes Methods for Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-061/III, Tinbergen Institute.
    2. Geert Mesters & Siem Jan Koopman, 2012. "Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time," Tinbergen Institute Discussion Papers 12-009/4, Tinbergen Institute, revised 18 Mar 2014.
    3. Geert Mesters & Victor van der Geest & Catrien Bijleveld, 2014. "Crime, Employment and Social Welfare: an Individual-level Study on Disadvantaged Males," Tinbergen Institute Discussion Papers 14-091/III, Tinbergen Institute.
    4. Geert Mesters & Bernd Schwaab & Siem Jan Koopman, 2014. "A Dynamic Yield Curve Model with Stochastic Volatility and Non-Gaussian Interactions: An Empirical Study of Non-standard Monetary Policy in the Euro Area," Tinbergen Institute Discussion Papers 14-071/III, Tinbergen Institute.
    5. Falk Bräuning & Siem Jan Koopman, 2016. "The Dynamic Factor Network Model with an Application to Global Credit-Risk," Tinbergen Institute Discussion Papers 16-105/III, Tinbergen Institute.
    6. Timothy Neal, 2018. "Multidimensional Parameter Heterogeneity in Panel Data Models," Discussion Papers 2016-15A, School of Economics, The University of New South Wales.
    7. Eser, Fabian & Schwaab, Bernd, 2016. "Evaluating the impact of unconventional monetary policy measures: Empirical evidence from the ECB׳s Securities Markets Programme," Journal of Financial Economics, Elsevier, vol. 119(1), pages 147-167.
    8. Borus Jungbacker & Siem Jan Koopman, 2015. "Likelihood‐based dynamic factor analysis for measurement and forecasting," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 1-21, June.
    9. Borus Jungbacker & Siem Jan Koopman, 2008. "Likelihood-based Analysis for Dynamic Factor Models," Tinbergen Institute Discussion Papers 08-007/4, Tinbergen Institute, revised 20 Mar 2014.

  7. Geert Mesters & Siem Jan Koopman & Marius Ooms, 2011. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Tinbergen Institute Discussion Papers 11-090/4, Tinbergen Institute.

    Cited by:

    1. Hartl, Tobias & Weigand, Roland, 2019. "Multivariate Fractional Components Analysis," University of Regensburg Working Papers in Business, Economics and Management Information Systems 38283, University of Regensburg, Department of Economics.
    2. Antonello D'Agostino & Domenico Giannone & Michele Lenza & Michele Modugno, 2015. "Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models," Finance and Economics Discussion Series 2015-66, Board of Governors of the Federal Reserve System (U.S.).
    3. Siem Jan Koopman & Marcel Scharth, 2011. "The Analysis of Stochastic Volatility in the Presence of Daily Realised Measures," Tinbergen Institute Discussion Papers 11-132/4, Tinbergen Institute.
    4. Tobias Hartl & Roland Weigand, 2018. "Approximate State Space Modelling of Unobserved Fractional Components," Papers 1812.09142, arXiv.org, revised Mar 2019.

Articles

  1. Regis Barnichon & Geert Mesters, 2018. "On the Demographic Adjustment of Unemployment," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 219-231, May.

    Cited by:

    1. Andreas Hornstein & Marianna Kudlyak, 2019. "Aggregate Labor Force Participation and Unemployment and Demographic Trends," Working Paper 19-8, Federal Reserve Bank of Richmond, revised 27 Mar 2019.
    2. Geert Mesters & Regis Barnichon, 2017. "How Tight Is the U.S. Labor Market?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    3. Marc Giannoni & Richard K. Crump & Stefano Eusepi & Aysegul Sahin, 2019. "A unified approach to measuring u," Staff Reports 889, Federal Reserve Bank of New York.
    4. Barnichon, Régis, 2019. "The Ins and Outs of Labor Force Participation," CEPR Discussion Papers 13481, C.E.P.R. Discussion Papers.
    5. Christian Matthes & Regis Barnichon, 2017. "The Natural Rate of Unemployment over the Past 100 Years," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    6. Wolters, Maik Hendrik, 2018. "How the baby boomers' retirement wave distorts model-based output gap estimates," IMFS Working Paper Series 121, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    7. Barnichon, Régis & Matthes, Christian, 2016. "Understanding the Size of the Government Spending Multiplier: It's in the Sign," CEPR Discussion Papers 11373, C.E.P.R. Discussion Papers.
    8. Barnichon, Régis & Matthes, Christian, 2016. "Gaussian Mixture Approximations of Impulse Responses and The Non-Linear Effects of Monetary Shocks," CEPR Discussion Papers 11374, C.E.P.R. Discussion Papers.

  2. Geert Mesters & Regis Barnichon, 2017. "How Tight Is the U.S. Labor Market?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.

    Cited by:

    1. Lael Brainard, 2017. "Why Opportunity and Inclusion Matter to America’s Economic Strength : a speech at the Opportunity and Inclusive Growth Institute Conference, sponsored by the Federal Reserve Bank of Minneapolis, May 2," Speech 953, Board of Governors of the Federal Reserve System (U.S.).
    2. Janet L. Yellen, 2017. "Inflation, Uncertainty, and Monetary Policy : a speech at the \\"Prospects for Growth: Reassessing the Fundamentals\\" 59th Annual Meeting of the National Association for Business Economics,," Speech 971, Board of Governors of the Federal Reserve System (U.S.).

  3. S. J. Koopman & G. Mesters, 2017. "Empirical Bayes Methods for Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 486-498, July.
    See citations under working paper version above.
  4. G. Mesters & S. J. Koopman & M. Ooms, 2016. "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 659-687, April.
    See citations under working paper version above.
  5. Mesters, G. & Koopman, S.J., 2014. "Generalized dynamic panel data models with random effects for cross-section and time," Journal of Econometrics, Elsevier, vol. 180(2), pages 127-140.
    See citations under working paper version above.

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 11 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 (7) 2011-07-13 2012-11-03 2014-11-17 2015-04-25 2017-09-24 2019-05-20 2019-05-27. Author is listed
  2. NEP-MAC: Macroeconomics (7) 2014-07-13 2015-04-25 2019-02-11 2019-02-25 2019-05-20 2019-05-27 2019-07-22. Author is listed
  3. NEP-MON: Monetary Economics (4) 2014-07-13 2015-04-25 2019-02-11 2019-02-25. Author is listed
  4. NEP-ORE: Operations Research (4) 2011-07-13 2012-11-03 2014-07-13 2014-11-17. Author is listed
  5. NEP-CBA: Central Banking (3) 2015-04-25 2019-02-11 2019-02-25. Author is listed
  6. NEP-ETS: Econometric Time Series (3) 2011-07-13 2015-04-25 2017-09-24. Author is listed
  7. NEP-DCM: Discrete Choice Models (2) 2014-11-22 2015-04-25
  8. NEP-LAW: Law & Economics (2) 2014-11-22 2015-04-25
  9. NEP-URE: Urban & Real Estate Economics (2) 2014-11-22 2015-04-25
  10. NEP-BEC: Business Economics (1) 2019-05-20
  11. NEP-EEC: European Economics (1) 2015-04-25

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