IDEAS home Printed from https://ideas.repec.org/f/pso326.html
   My authors  Follow this author

Yong Song

Personal Details

First Name:Yong
Middle Name:
Last Name:Song
Suffix:
RePEc Short-ID:pso326
Terminal Degree:2011 Department of Economics; University of Toronto (from RePEc Genealogy)

Affiliation

(99%) Department of Economics
Faculty of Business and Economics
University of Melbourne

Melbourne, Australia
http://www.economics.unimelb.edu.au/
RePEc:edi:demelau (more details at EDIRC)

(1%) Rimini Centre for Economic Analysis (RCEA)

Waterloo, Canada
http://www.rcea.world/
RePEc:edi:rcfeaca (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Maheu, John M & Song, Yong & Yang, Qiao, 2018. "Oil Price Shocks and Economic Growth: The Volatility Link," MPRA Paper 83999, University Library of Munich, Germany.
  2. Joshua C C Chan & Yong Song, 2017. "Measuring inflation expectations uncertainty using high-frequency data," CAMA Working Papers 2017-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  3. Li, Zhuo & Panza, Laura & Song, Yong, 2017. "The evolution of Ottoman-European market linkages, 1469-1914: evidence from dynamic factor models," MPRA Paper 80953, University Library of Munich, Germany.
  4. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.
  5. Maheu, John & Song, Yong, 2012. "A new structural break model with application to Canadian inflation forecasting," MPRA Paper 36870, University Library of Munich, Germany.
  6. Song, Yong & Shi, Shuping, 2012. "Identifying speculative bubbles with an in finite hidden Markov model," MPRA Paper 36455, University Library of Munich, Germany.
  7. Yong Song, 2012. "Modelling Regime Switching and Structural Breaks with an Infinite Hidden Markov Model," Working Paper series 28_12, Rimini Centre for Economic Analysis.
  8. Yong Song, 2011. "Modelling Regime Switching and Structural Breaks with an Infinite Dimension Markov Switching Model," Working Papers tecipa-427, University of Toronto, Department of Economics.
  9. John M Maheu & Thomas H McCurdy & Yong Song, 2010. "Components of bull and bear markets: bull corrections and bear rallies," Working Papers tecipa-402, University of Toronto, Department of Economics.
  10. John M Maheu & Thomas H McCurdy & Yong Song, 2009. "Extracting bull and bear markets from stock returns," Working Papers tecipa-369, University of Toronto, Department of Economics.

Articles

  1. John M. Maheu & Yong Song, 2018. "An efficient Bayesian approach to multiple structural change in multivariate time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 251-270, March.
  2. Dong-Hyuk Kim & Yong Song & Huaxin Xu, 2017. "A fast estimation procedure for discrete choice random coefficients demand model," Applied Economics, Taylor & Francis Journals, vol. 49(58), pages 5849-5855, December.
  3. Shuping Shi & Yong Song, 2016. "Identifying Speculative Bubbles Using an Infinite Hidden Markov Model," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 159-184.
  4. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
  5. Maheu, John M. & Song, Yong, 2014. "A new structural break model, with an application to Canadian inflation forecasting," International Journal of Forecasting, Elsevier, vol. 30(1), pages 144-160.
  6. John M. Maheu & Thomas H. McCurdy & Yong Song, 2012. "Components of Bull and Bear Markets: Bull Corrections and Bear Rallies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 391-403, February.

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. Maheu, John M & Song, Yong & Yang, Qiao, 2018. "Oil Price Shocks and Economic Growth: The Volatility Link," MPRA Paper 83999, University Library of Munich, Germany.

    Cited by:

    1. Wen, Jun & Mughal, Nafeesa & Kashif, Maryam & Jain, Vipin & Ramos Meza, Carlos Samuel & Cong, Phan The, 2022. "Volatility in natural resources prices and economic performance: Evidence from BRICS economies," Resources Policy, Elsevier, vol. 75(C).
    2. Muntasir Murshed & Haider Mahmood & Tarek Tawfik Yousef Alkhateeb & Mohga Bassim, 2020. "The Impacts of Energy Consumption, Energy Prices and Energy Import-Dependency on Gross and Sectoral Value-Added in Sri Lanka," Energies, MDPI, vol. 13(24), pages 1-22, December.
    3. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    4. Sohag, Kazi & Sokhanvar, Amin & Belyaeva, Zhanna & Mirnezami, Seyed Reza, 2022. "Hydrocarbon prices shocks, fiscal stability and consolidation: Evidence from Russian Federation," Resources Policy, Elsevier, vol. 76(C).
    5. Adedeji, Abdulkabir N. & Ahmed, Funmilola F. & Adam, Shehu U., 2021. "Examining the dynamic effect of COVID-19 pandemic on dwindling oil prices using structural vector autoregressive model," Energy, Elsevier, vol. 230(C).
    6. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Zhu, Bo, 2021. "Oil shocks and stock market volatility: New evidence," Energy Economics, Elsevier, vol. 103(C).
    7. Ren, Xiaohang & Qin, Jianing & Jin, Chenglu & Yan, Cheng, 2022. "Global oil price uncertainty and excessive corporate debt in China," Energy Economics, Elsevier, vol. 115(C).
    8. Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).
    9. Tumala, Mohammed M. & Salisu, Afees A. & Atoi, Ngozi V., 2022. "Oil-growth nexus in Nigeria: An ADL-MIDAS approach," Resources Policy, Elsevier, vol. 77(C).
    10. Jiang, Qisheng & Cheng, Sheng, 2021. "How the fiscal and monetary policy uncertainty of China respond to global oil price volatility: A multi-regime-on-scale approach," Resources Policy, Elsevier, vol. 72(C).
    11. Badeeb, Ramez Abubakr & Szulczyk, Kenneth R. & Lean, Hooi Hooi, 2021. "Asymmetries in the effect of oil rent shocks on economic growth: A sectoral analysis from the perspective of the oil curse," Resources Policy, Elsevier, vol. 74(C).
    12. Zhou, Shuai & Qian, Yudan & Farmanesh, Panteha, 2022. "The economic cost of environmental laws: Volatility transmission mechanism and remedies," Resources Policy, Elsevier, vol. 79(C).
    13. Nima Nonejad, 2021. "Bayesian model averaging and the conditional volatility process: an application to predicting aggregate equity returns by conditioning on economic variables," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1387-1411, August.
    14. Nima Nonejad, 2021. "Using the conditional volatility channel to improve the accuracy of aggregate equity return predictions," Empirical Economics, Springer, vol. 61(2), pages 973-1009, August.
    15. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    16. Rosnawintang Rosnawintang & Tajuddin Tajuddin & Pasrun Adam & Yuwanda Purnamasari Pasrun & La Ode Saidi, 2021. "Effects of Crude Oil Prices Volatility, the Internet and Inflation on Economic Growth in ASEAN-5 Countries: A Panel Autoregressive Distributed Lag Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 15-21.
    17. Gürkan Bozma & Murat Akadg & Rahman Aydin, 2021. "Dynamic Relationships between Oil Price, Inflation and Economic Growth: A VARMA, GARCH-in-mean, asymmetric BEKK Model for Turkey," Economics Bulletin, AccessEcon, vol. 41(3), pages 1266-1281.
    18. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).

  2. Joshua C C Chan & Yong Song, 2017. "Measuring inflation expectations uncertainty using high-frequency data," CAMA Working Papers 2017-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    2. Miescu, Mirela S., 2023. "Uncertainty shocks in emerging economies: A global to local approach for identification," European Economic Review, Elsevier, vol. 154(C).
    3. Carlomagno, Guillermo & Fornero, Jorge & Sansone, Andrés, 2023. "A proposal for constructing and evaluating core inflation measures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(3).
    4. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    5. Fuest, Angela & Schmidt, Torsten, 2020. "Inflation expectation uncertainty in a New Keynesian framework," Ruhr Economic Papers 867, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Ryngaert, Jane M., 2022. "Inflation disasters and consumption," Journal of Monetary Economics, Elsevier, vol. 129(S), pages 67-81.
    7. Carola Binder & Wesley Janson & Randal J. Verbrugge, 2019. "Thinking Outside the Box: Do SPF Respondents Have Anchored Inflation Expectations?," Working Papers 19-15, Federal Reserve Bank of Cleveland.

  3. Li, Zhuo & Panza, Laura & Song, Yong, 2017. "The evolution of Ottoman-European market linkages, 1469-1914: evidence from dynamic factor models," MPRA Paper 80953, University Library of Munich, Germany.

    Cited by:

    1. Panza, Laura, 2020. "From a common empire to colonial rule: commodity market disintegration in the Near East," CEPR Discussion Papers 15434, C.E.P.R. Discussion Papers.

  4. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.

    Cited by:

    1. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    2. Florian Huber & Gregor Kastner & Martin Feldkircher, 2016. "Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models," Papers 1607.04532, arXiv.org, revised Jul 2018.
    3. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2023. "General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 69-87, January.
    4. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
    5. Ardia, David & Dufays, Arnaud & Ordás Criado, Carlos, 2023. "Linking Frequentist and Bayesian Change-Point Methods," MPRA Paper 119486, University Library of Munich, Germany.
    6. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2021. "General Bayesian time-varying parameter VARs for predicting government bond yields," Papers 2102.13393, arXiv.org.
    7. Fischer, Manfred M. & Hauzenberger, Niko & Huber, Florian & Pfarrhofer, Michael, 2022. "General Bayesian time-varying parameter VARs for modeling government bond yields," Working Papers in Regional Science 2021/01, WU Vienna University of Economics and Business.

  5. Maheu, John & Song, Yong, 2012. "A new structural break model with application to Canadian inflation forecasting," MPRA Paper 36870, University Library of Munich, Germany.

    Cited by:

    1. Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," FRB Atlanta Working Paper 2018-2, Federal Reserve Bank of Atlanta.
    2. Arnaud Dufays & Aristide Houndetoungan & Alain Coen, 2024. "Selective linear segmentation for detecting relevant parameter changes," Papers 2402.05329, arXiv.org.
    3. Maheu, John M & Song, Yong, 2017. "An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series," MPRA Paper 79211, University Library of Munich, Germany.
    4. Dufays, Arnaud & Rombouts, Jeroen V.K., 2020. "Relevant parameter changes in structural break models," Journal of Econometrics, Elsevier, vol. 217(1), pages 46-78.
    5. Ardia, David & Dufays, Arnaud & Ordás Criado, Carlos, 2023. "Linking Frequentist and Bayesian Change-Point Methods," MPRA Paper 119486, University Library of Munich, Germany.
    6. Arnaud Dufays & Jeroen V. K. Rombouts, 2019. "Sparse Change-point HAR Models for Realized Variance," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 857-880, September.
    7. Adam Check & Jeremy Piger, 2021. "Structural Breaks in U.S. Macroeconomic Time Series: A Bayesian Model Averaging Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(8), pages 1999-2036, December.
    8. Meligkotsidou, Loukia & Tzavalis, Elias & Vrontos, Ioannis, 2017. "On Bayesian analysis and unit root testing for autoregressive models in the presence of multiple structural breaks," Econometrics and Statistics, Elsevier, vol. 4(C), pages 70-90.

  6. Song, Yong & Shi, Shuping, 2012. "Identifying speculative bubbles with an in finite hidden Markov model," MPRA Paper 36455, University Library of Munich, Germany.

    Cited by:

    1. Anton Gerunov, 2023. "Stock Returns Under Different Market Regimes: An Application of Markov Switching Models to 24 European Indices," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 18-35.
    2. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1043-1078, November.
    3. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    4. Andras Fulop & Jun Yu, 2017. "Bayesian Analysis of Bubbles in Asset Prices," Econometrics, MDPI, vol. 5(4), pages 1-23, October.
    5. Balcombe, Kelvin & Fraser, Iain, 2017. "Do bubbles have an explosive signature in markov switching models?," Economic Modelling, Elsevier, vol. 66(C), pages 81-100.
    6. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.

  7. Yong Song, 2012. "Modelling Regime Switching and Structural Breaks with an Infinite Hidden Markov Model," Working Paper series 28_12, Rimini Centre for Economic Analysis.

    Cited by:

    1. Chen, Yiyang & Mamon, Rogemar & Spagnolo, Fabio & Spagnolo, Nicola, 2022. "Renewable energy and economic growth: A Markov-switching approach," Energy, Elsevier, vol. 244(PB).
    2. Bauwens, Luc & Carpantier, Jean-François & Dufays, Arnaud, 2015. "Autoregressive moving average infinite hidden markov-switching models," LIDAM Discussion Papers CORE 2015007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
    4. Smith, Simon C., 2017. "Equity premium estimates from economic fundamentals under structural breaks," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 49-61.
    5. Mark Fisher & Mark J. Jensen, 2018. "Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors," FRB Atlanta Working Paper 2018-2, Federal Reserve Bank of Atlanta.
    6. John M. Maheu & Qiao Yang, 2015. "An Infinite Hidden Markov Model for Short-term Interest Rates," Working Paper series 15-05, Rimini Centre for Economic Analysis.
    7. Min Jeong Kim & Dohyoung Kwon, 2023. "Dynamic asset allocation strategy: an economic regime approach," Journal of Asset Management, Palgrave Macmillan, vol. 24(2), pages 136-147, March.
    8. Liu, Jia & Maheu, John M & Song, Yong, 2023. "Identification and Forecasting of Bull and Bear Markets using Multivariate Returns," MPRA Paper 119515, University Library of Munich, Germany.
    9. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    10. Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.
    11. Xin Jin & John M. Maheu, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," Working Paper series 34_14, Rimini Centre for Economic Analysis.
    12. Luo, Deqing & Pang, Tao & Xu, Jiawen, 2021. "Forecasting U.S. Yield Curve Using the Dynamic Nelson–Siegel Model with Random Level Shift Parameters," Economic Modelling, Elsevier, vol. 94(C), pages 340-350.
    13. DESCHAMPS, Philippe J., 2016. "Bayesian Semiparametric Forecasts of Real Interest Rate Data," LIDAM Discussion Papers CORE 2016050, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    15. Cross, Jamie L. & Hou, Chenghan & Nguyen, Bao H., 2021. "On the China factor in the world oil market: A regime switching approach11We thank Hilde Bjørnland, Tatsuyoshi Okimoto, Ippei Fujiwara, Knut Aastveit, Leif Anders Thorsrud, Francesco Ravazzolo, Renee ," Energy Economics, Elsevier, vol. 95(C).
    16. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
    17. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    18. Kaihatsu, Sohei & Nakajima, Jouchi, 2018. "Has trend inflation shifted?: An empirical analysis with an equally-spaced regime-switching model," Economic Analysis and Policy, Elsevier, vol. 59(C), pages 69-83.
    19. Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan, 2022. "Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models," International Journal of Forecasting, Elsevier, vol. 38(1), pages 51-73.
    20. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    21. Didier Nibbering & Richard Paap & Michel van der Wel, 2016. "A Bayesian Infinite Hidden Markov Vector Autoregressive Model," Tinbergen Institute Discussion Papers 16-107/III, Tinbergen Institute, revised 13 Oct 2017.
    22. Urbi Garay & Enrique Ter Horst & German Molina & Abel Rodriguez, 2016. "Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns," Econometrics, MDPI, vol. 4(1), pages 1-23, March.
    23. Simon C. Smith, 2020. "Equity premium prediction and structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 412-429, July.
    24. Jia Liu & John M. Maheu, 2018. "Improving Markov switching models using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 297-318, April.
    25. Juergen Amann & Paul Middleditch, 2017. "Growth in a time of austerity: evidence from the UK," Scottish Journal of Political Economy, Scottish Economic Society, vol. 64(4), pages 349-375, September.
    26. Samet G nay, 2015. "Markov Regime Switching Generalized Autoregressive Conditional Heteroskedastic Model and Volatility Modeling for Oil Returns," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 979-985.
    27. Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.
    28. Hou, Chenghan & Nguyen, Bao H., 2018. "Understanding the US natural gas market: A Markov switching VAR approach," Energy Economics, Elsevier, vol. 75(C), pages 42-53.
    29. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    30. Ozdemir, Dicle, 2019. "Sectoral Business Cycle Asymmetries and Regime Shifts: Evidence from Turkey," Asian Journal of Applied Economics, Kasetsart University, Center for Applied Economics Research, vol. 26(2), December.
    31. Griffin, Jim E. & Mitrodima, Gelly, 2020. "A Bayesian quantile time series model for asset returns," LSE Research Online Documents on Economics 105610, London School of Economics and Political Science, LSE Library.

  8. Yong Song, 2011. "Modelling Regime Switching and Structural Breaks with an Infinite Dimension Markov Switching Model," Working Papers tecipa-427, University of Toronto, Department of Economics.

    Cited by:

    1. DUFAYS, Arnaud, 2012. "Infinite-state Markov-switching for dynamic volatility and correlation models," LIDAM Discussion Papers CORE 2012043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    3. Jean-François Carpantier & Arnaud Dufays, 2014. "Specific Markov-switching behaviour for ARMA parameters," Working Papers hal-01821134, HAL.

  9. John M Maheu & Thomas H McCurdy & Yong Song, 2010. "Components of bull and bear markets: bull corrections and bear rallies," Working Papers tecipa-402, University of Toronto, Department of Economics.

    Cited by:

    1. Zeng, Songlin & Bec, Frédérique, 2015. "Do stock returns rebound after bear markets? An empirical analysis from five OECD countries," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 50-61.
    2. Heidari , Hassan & Refah-Kahriz, Arash & Hashemi Berenjabadi, Nayyer, 2018. "Dynamic Relationship between Macroeconomic Variables and Stock Return Volatility in Tehran Stock Exchange: Multivariate MS ARMA GARCH Approach," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 5(2), pages 223-250, August.
    3. Aliyu, Shehu Usman Rano, 2020. "What have we learnt from modelling stock returns in Nigeria: Higgledy-piggledy?," MPRA Paper 110382, University Library of Munich, Germany, revised 06 Jun 2021.
    4. Mathieu Gatumel & Florian Ielpo, 2011. "The Number of Regimes Across Asset Returns: Identification and Economic Value," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00658540, HAL.
    5. Chang, Kuang-Liang, 2016. "Does the return-state-varying relationship between risk and return matter in modeling the time series process of stock return?," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 72-87.
    6. Frédérique Bec & Annabelle de Gaye, 2019. "Le modèle autorégressif autorégressif à seuil avec effet rebond : Une application aux rendements boursiers français et américains ," Working Papers hal-02014663, HAL.
    7. Erik Kole & Dick Dijk, 2017. "How to Identify and Forecast Bull and Bear Markets?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 120-139, January.
    8. Maheu, John M & McCurdy, Thomas H & Song, Yong, 2020. "Bull and Bear Markets During the COVID-19 Pandemic," MPRA Paper 104504, University Library of Munich, Germany.
    9. Mendes, Fernando Henrique de Paula e Silva & Caldeira, João Frois & Moura, Guilherme Valle, 2018. "Evidence of Bull and Bear Markets in the Bovespa index: An application of Markovian regime-switching Models with Duration Dependence," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(1), May.
    10. Felix Haase & Matthias Neuenkirch, 2020. "Predictability of Bull and Bear Markets: A New Look at Forecasting Stock Market Regimes (and Returns) in the US," Research Papers in Economics 2020-01, University of Trier, Department of Economics.
    11. Gupta, Priyanshi & Sehgal, Sanjay & Deisting, Florent, 2015. "Time-Varying Bond Market Integration in EMU," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 30(4), pages 708-760.
    12. Kirby, Chris, 2023. "A closer look at the regime-switching evidence of bull and bear markets," Finance Research Letters, Elsevier, vol. 52(C).
    13. Kurov, Alexander & Olson, Eric & Zaynutdinova, Gulnara R., 2022. "When does the fed care about stock prices?," Journal of Banking & Finance, Elsevier, vol. 142(C).
    14. Liu, Jia & Maheu, John M & Song, Yong, 2023. "Identification and Forecasting of Bull and Bear Markets using Multivariate Returns," MPRA Paper 119515, University Library of Munich, Germany.
    15. Damir Tokic & Dave Jackson, 2023. "When a correction turns into a bear market: What explains the depth of the stock market drawdown? A discretionary global macro approach," Journal of Asset Management, Palgrave Macmillan, vol. 24(3), pages 184-197, May.
    16. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "Amplitude-Duration-Persistence Trade-off Relationship for Long Term Bear Stock Markets," MPRA Paper 54177, University Library of Munich, Germany.
    17. Nicolau, João, 2016. "Structural change test in duration of bull and bear markets," Economics Letters, Elsevier, vol. 146(C), pages 64-67.
    18. Collet, Jerome & Ielpo, Florian, 2018. "Sector spillovers in credit markets," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 267-278.
    19. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    20. Valeriy Zakamulin, 2023. "Not all bull and bear markets are alike: insights from a five-state hidden semi-Markov model," Risk Management, Palgrave Macmillan, vol. 25(1), pages 1-25, March.
    21. Giner, Javier & Zakamulin, Valeriy, 2023. "A regime-switching model of stock returns with momentum and mean reversion," Economic Modelling, Elsevier, vol. 122(C).
    22. Jeff Fleming & Chris Kirby, 2013. "Component-Driven Regime-Switching Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 11(2), pages 263-301, March.
    23. Aliyu, Shehu Usman Rano & Aminu, Abubakar Wambai, 2018. "Economic regimes and stock market performance in Nigeria: Evidence from regime switching model," MPRA Paper 91430, University Library of Munich, Germany, revised 03 Oct 2018.
    24. Kuang-Liang Chang & Nan-Kuang Chen & Charles Ka Yui Leung, 2016. "Losing Track of the Asset Markets: the Case of Housing and Stock," International Real Estate Review, Global Social Science Institute, vol. 19(4), pages 435-492.
    25. Jia Liu & John M. Maheu, 2018. "Improving Markov switching models using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 297-318, April.
    26. Ntantamis, Christos & Zhou, Jun, 2015. "Bull and bear markets in commodity prices and commodity stocks: Is there a relation?," Resources Policy, Elsevier, vol. 43(C), pages 61-81.
    27. Bejaoui, Azza & Karaa, Adel, 2016. "Revisiting the bull and bear markets notions in the Tunisian stock market: New evidence from multi-state duration-dependence Markov-switching models," Economic Modelling, Elsevier, vol. 59(C), pages 529-545.
    28. Sehgal, Sanjay & Gupta, Priyanshi & Deisting, Florent, 2014. "Assessing Time-Varying Stock Market Integration in EMU for Normal and Crisis Periods," MPRA Paper 64078, University Library of Munich, Germany.
    29. Ayadi, Mohamed A. & Lazrak, Skander & Liao, Yusui & Welch, Robert, 2018. "Performance of fixed-income mutual funds with regime-switching models," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 217-231.
    30. Julien Chevallier & Mathieu Gatumel & Florian Ielpo, 2013. "Understanding momentum in commodity markets," Applied Economics Letters, Taylor & Francis Journals, vol. 20(15), pages 1383-1402, October.
    31. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
    32. Blanka Horvath & Zacharia Issa & Aitor Muguruza, 2021. "Clustering Market Regimes using the Wasserstein Distance," Papers 2110.11848, arXiv.org.
    33. Mathieu Gatumel & Florian Ielpo, 2011. "The Number of Regimes Across Asset Returns: Identification and Economic Value," Post-Print halshs-00658540, HAL.
    34. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    35. Hanna, Alan J., 2018. "A top-down approach to identifying bull and bear market states," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 93-110.
    36. Zegadło, Piotr, 2022. "Identifying bull and bear market regimes with a robust rule-based method," Research in International Business and Finance, Elsevier, vol. 60(C).

  10. John M Maheu & Thomas H McCurdy & Yong Song, 2009. "Extracting bull and bear markets from stock returns," Working Papers tecipa-369, University of Toronto, Department of Economics.

    Cited by:

    1. Zeng, Songlin & Bec, Frédérique, 2015. "Do stock returns rebound after bear markets? An empirical analysis from five OECD countries," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 50-61.
    2. Cem Cakmakli & Richard Paap & Dick J.C. van Dijk, 2011. "Modeling and Estimation of Synchronization in Multistate Markov-Switching Models," Tinbergen Institute Discussion Papers 11-002/4, Tinbergen Institute.
    3. Vassilios Babalos & Mehmet Balcilar & Rangan Gupta & Nikolaos Philippas, 2014. "Revisiting Herding Behavior in REITs: A RegimeSwitching Approach," Working Papers 15-15, Eastern Mediterranean University, Department of Economics.
    4. Elsayed Elsiefy & Moustafa Ahmed AbdElaal, 2017. "Analyzing Foreign Investors Behavior in the Emerging Stock Market: Evidence from Qatar Stock Market," Accounting and Finance Research, Sciedu Press, vol. 6(4), pages 197-197, Novebmer.
    5. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat, 2015. "Regional and global spillovers and diversification opportunities in the GCC equity sectors," Emerging Markets Review, Elsevier, vol. 24(C), pages 160-187.
    6. Balcilar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat, 2013. "Investor herds and regime-switching: Evidence from Gulf Arab stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 23(C), pages 295-321.
    7. Mehmet Balcilar & Riza Demirer & Shawkat Hammoudeh & Ahmed Khalifa, 2013. "Do Global Shocks Drive Investor Herds in Oil-Rich Frontier Markets?," Working Papers 819, Economic Research Forum, revised Dec 2013.
    8. Ibrahim M. Awad & Abdel-Rahman Al-Ewesat, 2017. "Volatility Persistence in Palestine Exchange Bulls and Bears: An Econometric Analysis of Time Series Data," Review of Economics & Finance, Better Advances Press, Canada, vol. 9, pages 83-97, August.
    9. Samet Günay, 2014. "Are the Scaling Properties of Bull and Bear Markets Identical? Evidence from Oil and Gold Markets," IJFS, MDPI, vol. 2(4), pages 1-20, October.
    10. Mehmet Balcilar & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2019. "The Risk Exposures of Safe Havens to Global and Regional Stock Market Shocks: A Novel Approach," Working Papers 201915, University of Pretoria, Department of Economics.

Articles

  1. John M. Maheu & Yong Song, 2018. "An efficient Bayesian approach to multiple structural change in multivariate time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 251-270, March.
    See citations under working paper version above.
  2. Dong-Hyuk Kim & Yong Song & Huaxin Xu, 2017. "A fast estimation procedure for discrete choice random coefficients demand model," Applied Economics, Taylor & Francis Journals, vol. 49(58), pages 5849-5855, December.

    Cited by:

    1. Doug J. Chung & Kyoungwon Seo & Reo Song, 2023. "Efficient computation of discrete games: Estimating the effect of Apple on market structure," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2245-2263, July.

  3. Shuping Shi & Yong Song, 2016. "Identifying Speculative Bubbles Using an Infinite Hidden Markov Model," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 159-184.

    Cited by:

    1. Sébastien Laurent & Shuping Shi, 2018. "Volatility Estimation and Jump Detection for drift-diffusion Processes," AMSE Working Papers 1843, Aix-Marseille School of Economics, France.
    2. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    3. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    4. Pang, Tianxiao & Du, Lingjie & Chong, Terence Tai-Leung, 2021. "Estimating multiple breaks in nonstationary autoregressive models," Journal of Econometrics, Elsevier, vol. 221(1), pages 277-311.
    5. Qin, Meng & Su, Chi-Wei & Hao, Lin-Na & Tao, Ran, 2020. "The stability of U.S. economic policy: Does it really matter for oil price?," Energy, Elsevier, vol. 198(C).
    6. Moreira, Afonso M. & Martins, Luis F., 2020. "A new mechanism for anticipating price exuberance," International Review of Economics & Finance, Elsevier, vol. 65(C), pages 199-221.
    7. Shuping Shi, 2016. "Speculative bubbles or market fundamentals? An investigation of US regional housing markets," CAMA Working Papers 2016-46, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Yang, Qiao, 2019. "Stock returns and real growth: A Bayesian nonparametric approach," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 53-69.
    9. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    10. Balcombe, Kelvin & Fraser, Iain, 2017. "Do bubbles have an explosive signature in markov switching models?," Economic Modelling, Elsevier, vol. 66(C), pages 81-100.
    11. Janusz Sobieraj & Dominik Metelski, 2021. "Testing Housing Markets for Episodes of Exuberance: Evidence from Different Polish Cities," JRFM, MDPI, vol. 14(9), pages 1-29, September.
    12. GHERBOVEȚ, Sergiu, 2017. "The Poorest In The World Pays For Crisis," Journal of Financial and Monetary Economics, Centre of Financial and Monetary Research "Victor Slavescu", vol. 4(1), pages 141-148.

  4. Yong Song, 2014. "Modelling Regime Switching And Structural Breaks With An Infinite Hidden Markov Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 825-842, August.
    See citations under working paper version above.
  5. Maheu, John M. & Song, Yong, 2014. "A new structural break model, with an application to Canadian inflation forecasting," International Journal of Forecasting, Elsevier, vol. 30(1), pages 144-160.
    See citations under working paper version above.
  6. John M. Maheu & Thomas H. McCurdy & Yong Song, 2012. "Components of Bull and Bear Markets: Bull Corrections and Bear Rallies," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 391-403, February.
    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 15 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 (9) 2011-04-23 2012-02-20 2012-03-08 2012-03-21 2012-06-25 2012-06-25 2018-02-19 2018-02-19 2018-03-05. Author is listed
  2. NEP-ETS: Econometric Time Series (8) 2010-04-17 2011-04-23 2012-02-20 2012-03-21 2012-06-25 2012-06-25 2012-06-25 2017-05-28. Author is listed
  3. NEP-ECM: Econometrics (6) 2009-08-16 2011-04-23 2012-02-20 2012-03-08 2017-05-28 2017-10-29. Author is listed
  4. NEP-MAC: Macroeconomics (3) 2017-05-28 2017-10-29 2018-02-19
  5. NEP-MON: Monetary Economics (3) 2012-03-08 2012-06-25 2017-10-29
  6. NEP-ENE: Energy Economics (2) 2018-02-19 2018-03-05
  7. NEP-GRO: Economic Growth (1) 2018-02-19
  8. NEP-HIS: Business, Economic and Financial History (1) 2017-09-03
  9. NEP-MST: Market Microstructure (1) 2017-10-29
  10. NEP-ORE: Operations Research (1) 2017-05-28

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, Yong Song 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. RePEc uses bibliographic data supplied by the respective publishers.