IDEAS home Printed from https://ideas.repec.org/f/pgu370.html
   My authors  Follow this author

Pierre Guérin
(Pierre Guerin)

Personal Details

First Name:Pierre
Middle Name:
Last Name:Guérin
Suffix:
RePEc Short-ID:pgu370
https://sites.google.com/view/pierreguerineconomics/home
Twitter: @pierreconomics

Affiliation

Economics Department
Organisation de Coopération et de Développement Économiques (OCDE)

Paris, France
http://www.oecd.org/eco/

: 33-(0)-1-45 24 82 00
33-(0)-1-45 24 85 00
2 rue Andre Pascal, 75775 Paris Cedex 16
RePEc:edi:edoecfr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Pierre Guérin, 2019. "Améliorer l’efficience de l’investissement public en France," OECD Economics Department Working Papers 1560, OECD Publishing.
  2. Antoine Goujard & Pierre Guérin, 2018. "Financing innovative business investment in Poland," OECD Economics Department Working Papers 1480, OECD Publishing.
  3. Narayan Bulusu & Pierre Guérin, 2018. "What Drives Interbank Loans? Evidence from Canada," Staff Working Papers 18-5, Bank of Canada.
  4. Claudia Foroni & Pierre Guérin & Massimiliano Marcellino, 2017. "Explaining the Time-varying Effects Of Oil Market Shocks On U.S. Stock Returns," Working Papers 597, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  5. Pierre Guérin & Danilo Leiva-Leon, 2017. "Monetary policy, stock market and sectoral comovement," Working Papers 1731, Banco de España;Working Papers Homepage.
  6. Christian Friedrich & Pierre Guérin, 2016. "The Dynamics of Capital Flow Episodes," Staff Working Papers 16-9, Bank of Canada.
  7. Pierre Guerin & Danilo Leiva-Leon & Massimiliano Marcellino, 2016. "Markov-Switching Three-Pass Regression Filter," Working Papers 591, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  8. Kimberly Berg & Pierre Guérin & Yuko Imura, 2016. "Predictive Ability of Commodity Prices for the Canadian Dollar," Staff Analytical Notes 16-2, Bank of Canada.
  9. Claudia Foroni & Pierre Guérin & Massimiliano Marcellino, 2015. "Using low frequency information for predicting high frequency variables," Working Paper 2015/13, Norges Bank.
  10. Laurent Ferrara & Pierre Guérin, 2015. "What Are The Macroeconomic Effects of High-Frequency Uncertainty Shocks?," EconomiX Working Papers 2015-12, University of Paris Nanterre, EconomiX.
  11. Guérin, Pierre & Leiva-Leon, Danilo, 2014. "Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data," MPRA Paper 59361, University Library of Munich, Germany.
  12. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
  13. Bijsterbosch, Martin & Guérin, Pierre, 2014. "Characterizing very high uncertainty episodes," Working Paper Series 1637, European Central Bank.
  14. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2013. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," CFS Working Paper Series 2013/22, Center for Financial Studies (CFS).
  15. Eric Ghysels & Pierre Guérin & Massimiliano Marcellino, 2013. "Regime Switches in the Risk-Return Trade-Off," Staff Working Papers 13-51, Bank of Canada.
  16. Mohr, Matthias & Maurin, Laurent & Guérin, Pierre, 2011. "Trend-cycle decomposition of output and euro area inflation forecasts: a real-time approach based on model combination," Working Paper Series 1384, European Central Bank.
  17. Guérin, Pierre & Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.

Articles

  1. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high‐frequency uncertainty shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 662-679, August.
  2. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
  3. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
  4. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2017. "Explaining the time-varying effects of oil market shocks on US stock returns," Economics Letters, Elsevier, vol. 155(C), pages 84-88.
  5. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(02), pages 363-393, March.
  6. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
  7. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
  8. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
  9. Russell Barnett & Pierre Guérin, 2013. "Monitoring Short-Term Economic Developments in Foreign Economies," Bank of Canada Review, Bank of Canada, vol. 2013(Summer), pages 22-31.
  10. Pierre Guérin & Massimiliano Marcellino, 2013. "Markov-Switching MIDAS Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 45-56, January.
  11. Bijsterbosch, Martin & Guérin, Pierre, 2013. "Characterizing very high uncertainty episodes," Economics Letters, Elsevier, vol. 121(2), pages 239-243.

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. Laurent Ferrara & Pierre Guérin, 2015. "What Are The Macroeconomic Effects of High-Frequency Uncertainty Shocks?," EconomiX Working Papers 2015-12, University of Paris Nanterre, EconomiX.

    Mentioned in:

    1. Guest Contribution: “Macroeconomic Effects of High-Frequency Uncertainty Shocks”
      by Menzie Chinn in Econbrowser on 2015-06-30 12:30:51
  2. Author Profile
    1. Guest Contribution: “Macroeconomic Effects of High-Frequency Uncertainty Shocks”
      by Menzie Chinn in Econbrowser on 2015-06-30 12:30:51

Working papers

  1. Claudia Foroni & Pierre Guérin & Massimiliano Marcellino, 2017. "Explaining the Time-varying Effects Of Oil Market Shocks On U.S. Stock Returns," Working Papers 597, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Eraslan, Sercan & Menla Ali, Faek, 2018. "Oil price shocks and stock return volatility: New evidence based on volatility impulse response analysis," Economics Letters, Elsevier, vol. 172(C), pages 59-62.
    2. Zhenhua Liu & Zhihua Ding & Tao Lv & Jy S. Wu & Wei Qiang, 2019. "Financial factors affecting oil price change and oil-stock interactions: a review and future perspectives," 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. 95(1), pages 207-225, January.
    3. Brice V. Dupoyet & Corey A. Shank, 2018. "Oil prices implied volatility or direction: Which matters more to financial markets?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(3), pages 275-295, August.
    4. Eraslan, Sercan & Ali, Faek Menla, 2018. "Oil price shocks and stock return volatility: New evidence based on volatility impulse response analysis," Discussion Papers 38/2018, Deutsche Bundesbank.

  2. Christian Friedrich & Pierre Guérin, 2016. "The Dynamics of Capital Flow Episodes," Staff Working Papers 16-9, Bank of Canada.

    Cited by:

    1. Avdjiev, Stefan & Hale, Galina, 2018. "U.S. Monetary Policy and Fluctuations of International Bank Lending," Working Paper Series 2018-2, Federal Reserve Bank of San Francisco.
    2. Soohyon Kim, 2018. "Determinants of Capital Flows in the Korean Bond Market," Working Papers 2018-44, Economic Research Institute, Bank of Korea.

  3. Pierre Guerin & Danilo Leiva-Leon & Massimiliano Marcellino, 2016. "Markov-Switching Three-Pass Regression Filter," Working Papers 591, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Maxime Leroux & Rachidi Kotchoni & Dalibor Stevanovic, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.
    2. Pierre Guérin & Danilo Leiva-Leon, 2017. "Monetary policy, stock market and sectoral comovement," Working Papers 1731, Banco de España;Working Papers Homepage.
    3. Edouard Djeutem & Geoffrey R. Dunbar, 2018. "Uncovered Return Parity: Equity Returns and Currency Returns," Staff Working Papers 18-22, Bank of Canada.

  4. Kimberly Berg & Pierre Guérin & Yuko Imura, 2016. "Predictive Ability of Commodity Prices for the Canadian Dollar," Staff Analytical Notes 16-2, Bank of Canada.

    Cited by:

    1. Michael B. Devereux & Gregor W. Smith, 2018. "Commodity Currencies and Monetary Policy," NBER Working Papers 25076, National Bureau of Economic Research, Inc.

  5. Claudia Foroni & Pierre Guérin & Massimiliano Marcellino, 2015. "Using low frequency information for predicting high frequency variables," Working Paper 2015/13, Norges Bank.

    Cited by:

    1. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    2. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    3. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.
    4. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    5. 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.
    6. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.

  6. Laurent Ferrara & Pierre Guérin, 2015. "What Are The Macroeconomic Effects of High-Frequency Uncertainty Shocks?," EconomiX Working Papers 2015-12, University of Paris Nanterre, EconomiX.

    Cited by:

    1. Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan Working Paper Series 18-E-18, Bank of Japan.
    2. M. Bussière & L. Ferrara & J. Milovich, 2015. "Explaining the Recent Slump in Investment: the Role of Expected Demand and Uncertainty," Working papers 571, Banque de France.
    3. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2016. "Structural analysis with mixed frequencies: monetary policy, uncertainty and gross capital flows," Working Papers 2016-04, Joint Research Centre, European Commission (Ispra site).
    4. Casarin, Roberto & Foroni, Claudia & Marcellino, Massimiliano & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    5. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2017. "Uncertainty and Monetary Policy in Good and Bad Times," Melbourne Institute Working Paper Series wp2017n09, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    6. Ferrara, L. & Istrefi, K., 2016. "Impact des chocs d’incertitude sur l’économie mondiale – Synthèse de conférence," Bulletin de la Banque de France, Banque de France, issue 206, pages 61-68.
    7. Dario Bonciani & Andrea Tafuro, 2018. "The Effects of Uncertainty Shocks on Daily Prices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 89-104, April.
    8. Magnus Reif, 2018. "Macroeconomic Uncertainty and Forecasting Macroeconomic Aggregates," ifo Working Paper Series 265, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

  7. Guérin, Pierre & Leiva-Leon, Danilo, 2014. "Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data," MPRA Paper 59361, University Library of Munich, Germany.

    Cited by:

    1. María Dolores Gadea-Rivas & Ana Gómez-Loscos & Danilo Leiva-Leon, 2017. "The evolution of regional economic interlinkages in Europe," Working Papers 1705, Banco de España;Working Papers Homepage.

  8. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.

    Cited by:

    1. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    2. 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.
    3. McCracken, Michael W. & Owyang, Michael T. & Sekhposyan, Tatevik, 2015. "Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR," Working Papers 2015-30, Federal Reserve Bank of St. Louis.
    4. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    5. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.
    6. Hecq, Alain & Goetz, Thomas, 2018. "Granger causality testing in mixed-frequency Vars with possibly (co)integrated processes," MPRA Paper 87746, University Library of Munich, Germany.

  9. Bijsterbosch, Martin & Guérin, Pierre, 2014. "Characterizing very high uncertainty episodes," Working Paper Series 1637, European Central Bank.

    Cited by:

    1. Silvia Delrio, 2016. "Estimating the effects of global uncertainty in open economies," Working Papers 2016:19, Department of Economics, University of Venice "Ca' Foscari".
    2. Ko, Jun-Hyung & Lee, Chang-Min, 2015. "International economic policy uncertainty and stock prices: Wavelet approach," Economics Letters, Elsevier, vol. 134(C), pages 118-122.
    3. Sangyup Choi & Chansik Yoon, 2019. "Uncertainty, Financial Markets, and Monetary Policy over the Last Century," Working papers 2019rwp-142, Yonsei University, Yonsei Economics Research Institute.
    4. Klößner, Stefan & Sekkel, Rodrigo, 2014. "International spillovers of policy uncertainty," Economics Letters, Elsevier, vol. 124(3), pages 508-512.
    5. Magnus Reif, 2018. "Macroeconomic Uncertainty and Forecasting Macroeconomic Aggregates," ifo Working Paper Series 265, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

  10. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2013. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," CFS Working Paper Series 2013/22, Center for Financial Studies (CFS).

    Cited by:

    1. Etienne, Xiaoli, 2015. "Financialization of Agricultural Commodity Markets: Do Financial Data Help to Forecast Agricultural Prices," 2015 Conference, August 9-14, 2015, Milan, Italy 211626, International Association of Agricultural Economists.
    2. Carl Bonham & Peter Fuleky & James Jones & Ashley Hirashima, 2015. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 2015-13R, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2016.
    3. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K, 2014. "Are there Gains from Pooling Real-Time Oil Price Forecasts?," CEPR Discussion Papers 10075, C.E.P.R. Discussion Papers.
    4. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    5. Wang, Yudong & Liu, Li & Diao, Xundi & Wu, Chongfeng, 2015. "Forecasting the real prices of crude oil under economic and statistical constraints," Energy Economics, Elsevier, vol. 51(C), pages 599-608.
    6. Christiane Baumeister & Lutz Kilian & Thomas K. Lee, 2016. "Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at the Pump," CESifo Working Paper Series 5759, CESifo Group Munich.
    7. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    8. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    9. Ghassan, Hassan Belkacem & AlHajhoj, Hassan Rafdan, 2016. "Long run dynamic volatilities between OPEC and non-OPEC crude oil prices," Applied Energy, Elsevier, vol. 169(C), pages 384-394.
    10. Ron Alquist & Gregory Bauer & Antonio Diez de los Rios, 2014. "What Does the Convenience Yield Curve Tell Us about the Crude Oil Market?," Staff Working Papers 14-42, Bank of Canada.
    11. Bos, Martijn & Demirer, Riza & Gupta, Rangan & Tiwari, Aviral Kumar, 2018. "Oil returns and volatility: The role of mergers and acquisitions," Energy Economics, Elsevier, vol. 71(C), pages 62-69.
    12. Bernard, Jean-Thomas & Khalaf, Lynda & Kichian, Maral & Yelou, Clement, 2018. "Oil Price Forecasts For The Long Term: Expert Outlooks, Models, Or Both?," Macroeconomic Dynamics, Cambridge University Press, vol. 22(03), pages 581-599, April.
    13. Funk, Christoph, 2018. "Forecasting the real price of oil - Time-variation and forecast combination," Energy Economics, Elsevier, vol. 76(C), pages 288-302.
    14. Valadkhani, Abbas & Smyth, Russell, 2018. "Asymmetric responses in the timing, and magnitude, of changes in Australian monthly petrol prices to daily oil price changes," Energy Economics, Elsevier, vol. 69(C), pages 89-100.
    15. Nguyen, Duc Khuong & Walther, Thomas, 2017. "Modeling and forecasting commodity market volatility with long-term economic and financial variables," MPRA Paper 84464, University Library of Munich, Germany, revised Jan 2018.
    16. Hirashima, Ashley & Jones, James & Bonham, Carl S. & Fuleky, Peter, 2017. "Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism," Annals of Tourism Research, Elsevier, vol. 63(C), pages 191-202.
    17. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
    18. Gupta, Rangan & Yoon, Seong-Min, 2018. "OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 206-214.
    19. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    20. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    21. 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.
    22. Buncic, Daniel & Piras, Gion Donat, 2016. "Heterogeneous agents, the financial crisis and exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 313-359.
    23. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    24. Danish A. Alvi, 2018. "Application of Probabilistic Graphical Models in Forecasting Crude Oil Price," Papers 1804.10869, arXiv.org.
    25. Snudden, Stephen, 2018. "Targeted growth rates for long-horizon crude oil price forecasts," International Journal of Forecasting, Elsevier, vol. 34(1), pages 1-16.
    26. Ding Du & Ronald J Gunderson & Xiaobing Zhao, 2016. "Investor sentiment and oil prices," Journal of Asset Management, Palgrave Macmillan, vol. 17(2), pages 73-88, March.
    27. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
    28. Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    29. Dbouk, Wassim & Jamali, Ibrahim, 2018. "Predicting daily oil prices: Linear and non-linear models," Research in International Business and Finance, Elsevier, vol. 46(C), pages 149-165.
    30. 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.
    31. Ding Du & Xiaobing Zhao, 2017. "Financial investor sentiment and the boom/bust in oil prices during 2003–2008," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 331-361, February.
    32. Zhaojie Luo & Xiaojing Cai & Katsuyuki Tanaka & Tetsuya Takiguchi & Takuji Kinkyo & Shigeyuki Hamori, 2019. "Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(1), pages 1-13, January.
    33. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.

  11. Eric Ghysels & Pierre Guérin & Massimiliano Marcellino, 2013. "Regime Switches in the Risk-Return Trade-Off," Staff Working Papers 13-51, Bank of Canada.

    Cited by:

    1. Kinnunen, Jyri & Martikainen, Minna, 2015. "Expected returns and idiosyncratic risk: Industry-level evidence from Russia," BOFIT Discussion Papers 30/2015, Bank of Finland, Institute for Economies in Transition.
    2. Nektarios Aslanidis & Charlotte Christiansen, 2017. "Flight to Safety from European Stock Markets," CREATES Research Papers 2017-38, Department of Economics and Business Economics, Aarhus University.
    3. Tobias Adrian & Richard K. Crump & Erik Vogt, 2019. "Nonlinearity and Flight‐to‐Safety in the Risk‐Return Trade‐Off for Stocks and Bonds," Journal of Finance, American Finance Association, vol. 74(4), pages 1931-1973, August.
    4. Rachidi Kotchoni, 2018. "Detecting and Measuring Nonlinearity," Econometrics, MDPI, Open Access Journal, vol. 6(3), pages 1-27, August.
    5. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou & Weining Wang, 2017. "Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing," CREATES Research Papers 2017-34, Department of Economics and Business Economics, Aarhus University.
    6. Liu, Xiaochun, 2017. "Can macroeconomic dynamics explain the time variation of risk–return trade-offs in the U.S. financial market?," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 275-293.
    7. Naqi Shah, Sadia & Qayyum, Abdul, 2016. "Analyse Risk-Return Paradox: Evidence from Electricity Sector of Pakistan," MPRA Paper 85528, University Library of Munich, Germany.
    8. Aslanidis, Nektarios & Christiansen, Charlotte & Savva, Christos S., 2016. "Risk-return trade-off for European stock markets," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 84-103.
    9. Frazier, David T. & Liu, Xiaochun, 2016. "A new approach to risk-return trade-off dynamics via decomposition," Journal of Economic Dynamics and Control, Elsevier, vol. 62(C), pages 43-55.
    10. Licheng Sun & Liang Meng & Mohammad Najand, 2017. "The Role of U.S. Market on International Risk-Return Tradeoff Relations," The Financial Review, Eastern Finance Association, vol. 52(3), pages 499-526, August.
    11. Jorge M. Uribe, 2018. "“Scaling Down Downside Risk with Inter-Quantile Semivariances”," IREA Working Papers 201826, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    12. Petar Sabtchevsky & Paul Whelan & Andrea Vedolin & Philippe Mueller, 2017. "Variance Risk Premia on Stocks and Bonds," 2017 Meeting Papers 1161, Society for Economic Dynamics.
    13. Salvador, Enrique & Floros, Christos & Arago, Vicent, 2014. "Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 60-77.
    14. Liu, Xiaochun, 2017. "Unfolded risk-return trade-offs and links to Macroeconomic Dynamics," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 1-19.

  12. Mohr, Matthias & Maurin, Laurent & Guérin, Pierre, 2011. "Trend-cycle decomposition of output and euro area inflation forecasts: a real-time approach based on model combination," Working Paper Series 1384, European Central Bank.

    Cited by:

    1. Mendieta-Muñoz, Ivan, 2017. "On The Interaction Between Economic Growth And Business Cycles," Macroeconomic Dynamics, Cambridge University Press, vol. 21(04), pages 982-1022, June.
    2. Lise Pichette & Marie-Noëlle Robitaille & Mohanad Salameh & Pierre St-Amant, 2018. "Dismiss the Gap? A Real-Time Assessment of the Usefulness of Canadian Output Gaps in Forecasting Inflation," Staff Working Papers 18-10, Bank of Canada.
    3. Pichette, Lise & Robitaille, Marie-Noëlle & Salameh, Mohanad & St-Amant, Pierre, 2019. "Dismiss the output gaps? To use with caution given their limitations," Economic Modelling, Elsevier, vol. 76(C), pages 199-215.
    4. Susanne Maidorn, 2018. "Is there a trade-off between procyclicality and revisions in EC trend TFP estimations?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 45(1), pages 59-82, February.

  13. Guérin, Pierre & Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    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. Banbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    4. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    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.
    6. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
    7. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, Elsevier.
    8. Casarin, Roberto & Foroni, Claudia & Marcellino, Massimiliano & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    9. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    10. Pierre Guérin & Danilo Leiva-Leon, 2015. "Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data," Staff Working Papers 15-24, Bank of Canada.
    11. 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.
    12. 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.
    13. Liu, Xiaochun, 2017. "An integrated macro-financial risk-based approach to the stressed capital requirement," Review of Financial Economics, Elsevier, vol. 34(C), pages 86-98.
    14. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.
    15. Michael Funke & Hao Yu & Aaron Mehrota, 2011. "Tracking Chinese CPI inflation in real time," Quantitative Macroeconomics Working Papers 21112, Hamburg University, Department of Economics.
    16. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    17. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    18. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    19. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    20. Xiaochun Liu, 2016. "Markov switching quantile autoregression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(4), pages 356-395, November.
    21. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    22. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    23. de Bruijn, L.P. & Franses, Ph.H.B.F., 2015. "Stochastic levels and duration dependence in US unemployment," Econometric Institute Research Papers EI2015-20, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    24. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    25. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    26. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    27. 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.
    28. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    29. Thomas Walther & Lanouar Charfeddine & Tony Klein, 2018. "Oil Price Changes and U.S. Real GDP Growth: Is this Time Different?," Working Papers on Finance 1816, University of St. Gallen, School of Finance.

Articles

  1. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high‐frequency uncertainty shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(5), pages 662-679, August.
    See citations under working paper version above.
  2. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    See citations under working paper version above.
  3. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    See citations under working paper version above.
  4. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2017. "Explaining the time-varying effects of oil market shocks on US stock returns," Economics Letters, Elsevier, vol. 155(C), pages 84-88.
    See citations under working paper version above.
  5. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(02), pages 363-393, March.
    See citations under working paper version above.
  6. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    See citations under working paper version above.
  7. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    See citations under working paper version above.
  8. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
    See citations under working paper version above.
  9. Russell Barnett & Pierre Guérin, 2013. "Monitoring Short-Term Economic Developments in Foreign Economies," Bank of Canada Review, Bank of Canada, vol. 2013(Summer), pages 22-31.

    Cited by:

    1. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.

  10. Pierre Guérin & Massimiliano Marcellino, 2013. "Markov-Switching MIDAS Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 45-56, January.
    See citations under working paper version above.
  11. Bijsterbosch, Martin & Guérin, Pierre, 2013. "Characterizing very high uncertainty episodes," Economics Letters, Elsevier, vol. 121(2), pages 239-243.
    See citations under working paper version above.

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 22 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-MAC: Macroeconomics (11) 2011-10-15 2014-06-02 2014-11-17 2015-04-19 2015-11-07 2016-03-06 2016-06-09 2017-07-30 2017-08-27 2018-02-12 2018-07-16. Author is listed
  2. NEP-FOR: Forecasting (9) 2011-10-15 2013-11-29 2014-05-09 2014-06-02 2014-06-02 2014-11-17 2015-11-07 2016-03-06 2017-07-30. Author is listed
  3. NEP-ORE: Operations Research (8) 2014-04-05 2014-06-02 2014-11-17 2015-11-07 2016-03-06 2017-05-07 2017-07-30 2018-01-29. Author is listed
  4. NEP-ETS: Econometric Time Series (6) 2014-06-02 2015-11-07 2016-03-06 2017-01-01 2017-07-30 2018-01-29. Author is listed
  5. NEP-ECM: Econometrics (5) 2011-10-15 2014-06-02 2014-11-17 2015-11-07 2017-01-01. Author is listed
  6. NEP-MST: Market Microstructure (5) 2013-11-29 2014-05-09 2014-06-02 2015-04-19 2015-11-07. Author is listed
  7. NEP-ENE: Energy Economics (4) 2013-11-29 2014-05-09 2014-06-02 2017-03-12
  8. NEP-MON: Monetary Economics (3) 2011-10-15 2016-03-29 2017-08-27
  9. NEP-RMG: Risk Management (2) 2013-12-29 2014-06-02
  10. NEP-URE: Urban & Real Estate Economics (2) 2016-03-06 2017-07-30
  11. NEP-BAN: Banking (1) 2018-02-12
  12. NEP-CBA: Central Banking (1) 2011-10-15
  13. NEP-CSE: Economics of Strategic Management (1) 2018-07-16
  14. NEP-EEC: European Economics (1) 2011-10-15
  15. NEP-ENT: Entrepreneurship (1) 2018-07-16
  16. NEP-EUR: Microeconomic European Issues (1) 2018-07-16
  17. NEP-FMK: Financial Markets (1) 2014-06-02
  18. NEP-INO: Innovation (1) 2018-07-16
  19. NEP-LMA: Labor Markets - Supply, Demand, & Wages (1) 2016-06-09
  20. NEP-SBM: Small Business Management (1) 2018-07-16
  21. NEP-TRA: Transition Economics (1) 2018-07-16

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Pierre Guérin
(Pierre Guerin) should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.