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Henri Nyberg

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. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Ülkü, Numan & Kuruppuarachchi, Duminda & Kuzmicheva, Olga, 2017. "Stock market's response to real output shocks in Eastern European frontier markets: A VARwAL model," Emerging Markets Review, Elsevier, vol. 33(C), pages 140-154.
    2. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.

  2. Henri Nyberg & Harri Pönkä, 2015. "International Sign Predictability of Stock Returns: The Role of the United States," CREATES Research Papers 2015-20, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. James Nguyen & Wei-Xuan Li & Clara Chia-Sheng Chen, 2022. "Mean Reversions in Major Developed Stock Markets: Recent Evidence from Unit Root, Spectral and Abnormal Return Studies," JRFM, MDPI, vol. 15(4), pages 1-20, April.
    2. António R. Antunes & Diana Bonfim & Nuno Monteiro & Paulo M.M. Rodrigues, 2016. "Forecasting banking crises with dynamic panel probit models," Working Papers w201613, Banco de Portugal, Economics and Research Department.
    3. Yi-Chieh Wen & Bin Li, 2020. "Lagged country returns and international stock return predictability during business cycle recession periods," Applied Economics, Taylor & Francis Journals, vol. 52(46), pages 5005-5019, October.
    4. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    5. Ayedi Ahmed & Marjène Gana & Stéphane Goutte & Khaled Guesmi, 2023. "Managing Portfolio Risk During the BREXIT Crisis: A Cross-Quantilogram Analysis of Stock Markets and Commodities Across European Countries, the US, and BRICS," Working Papers halshs-04068651, HAL.
    6. 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.
    7. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    8. Hadhri, Sinda & Ftiti, Zied, 2017. "Stock return predictability in emerging markets: Does the choice of predictors and models matter across countries?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 39-60.
    9. Ahmed, Walid M.A. & Sleem, Mohamed A.E., 2023. "Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach," Energy Economics, Elsevier, vol. 124(C).
    10. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Narayan, Seema, 2018. "Technology-investing countries and stock return predictability," Emerging Markets Review, Elsevier, vol. 36(C), pages 159-179.
    11. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    12. Narayan, Paresh Kumar, 2018. "Profitability of technology-investing Islamic and non-Islamic stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 52(C), pages 70-81.
    13. Kang, Yong Joo & Park, Dojoon & Eom, Young Ho, 2024. "Global contagion of US COVID-19 panic news," Emerging Markets Review, Elsevier, vol. 59(C).
    14. Sadorsky, Perry, 2022. "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    15. Lauri Nevasalmi, 2022. "Recession forecasting with high‐dimensional data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 752-764, July.
    16. Salisu, Afees A. & Tchankam, Jean Paul, 2022. "US Stock return predictability with high dimensional models," Finance Research Letters, Elsevier, vol. 45(C).
    17. Nguyen, Dat Thanh & Phan, Dinh Hoang Bach & Anglingkusumo, Reza & Sasongko, Aryo, 2021. "US government shutdowns and Indonesian stock market," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    18. Jonathan Iworiso & Spyridon Vrontos, 2020. "On the directional predictability of equity premium using machine learning techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 449-469, April.
    19. Haibin Xie & Yuying Sun & Pengying Fan, 2023. "Return direction forecasting: a conditional autoregressive shape model with beta density," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
    20. Harri Pönkä, 2018. "Sentiment and sign predictability of stock returns," Economics Bulletin, AccessEcon, vol. 38(3), pages 1676-1684.
    21. Pönkä, Harri, 2015. "Real oil prices and the international sign predictability of stock returns," MPRA Paper 68330, University Library of Munich, Germany.
    22. Narayan, Paresh Kumar & Liu, Ruipeng, 2018. "A new GARCH model with higher moments for stock return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 93-103.
    23. Riza Erdugan & Nada Kulendran & Riccardo Natoli, 2019. "Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(4), pages 417-445, December.
    24. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    25. Liu, Jingzhen & Kemp, Alexander, 2019. "Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables," Energy Economics, Elsevier, vol. 81(C), pages 672-686.
    26. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting NFT coin prices using machine learning: Insights into feature significance and portfolio strategies," Global Finance Journal, Elsevier, vol. 58(C).
    27. Ana Monteiro & Nuno Silva & Helder Sebastião, 2023. "Industry return lead-lag relationships between the US and other major countries," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-48, December.
    28. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    29. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.

  3. Markku Lanne & Henri Nyberg, 2014. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," CREATES Research Papers 2014-17, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Gian Paulo Soave, 2023. "A panel threshold VAR with stochastic volatility-in-mean model: an application to the effects of financial and uncertainty shocks in emerging economies," Applied Economics, Taylor & Francis Journals, vol. 55(4), pages 397-431, January.
    2. Giovanni Caggiano & Efrem Castelnuovo & Juan Manuel Figueres, 2018. "Economic Policy Uncertainty Spillovers in Booms and Busts," "Marco Fanno" Working Papers 0220, Dipartimento di Scienze Economiche "Marco Fanno".
    3. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Spillover effects in oil-related CDS markets during and after the sub-prime crisis," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    4. Clausen Volker & Schlösser Alexander & Thiem Christopher, 2019. "Economic Policy Uncertainty in the Euro Area: Cross-Country Spillovers and Macroeconomic Impact," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(5-6), pages 957-981, October.
    5. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2021. "Uncertainty and Monetary Policy during the Great Recession," Economics Working Papers 2021-05, Department of Economics and Business Economics, Aarhus University.
    6. Zahra Naoar Masih, 2017. "Causality between Defence Spending and Economic Growth in Sub-Saharan Africa: Evidence on a Controversial Empirical Issue," International Journal of Economics and Financial Issues, Econjournals, vol. 7(5), pages 169-177.
    7. Pierre Mabille, 2019. "Aggregate Precautionary Savings Motives," 2019 Meeting Papers 344, Society for Economic Dynamics.
    8. Mumtaz, Haroon & Theodoridis, Konstantinos, 2018. "Dynamic Effects of Monetary Policy Shocks on Macroeconomic Volatility," Cardiff Economics Working Papers E2018/21, Cardiff University, Cardiff Business School, Economics Section.
    9. Mansur, Alfan, 2023. "Capital flow volatility regimes and monetary policy dilemma: Evidence from New Zealand," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    10. Tyler Atkinson & Michael D. Plante & Alexander W. Richter & Nathaniel A. Throckmorton, 2020. "Complementarity and Macroeconomic Uncertainty," Working Papers 2009, Federal Reserve Bank of Dallas.
    11. Taifeng Yang & Xuetao Huang & Yue Wang & Houjian Li & Lili Guo, 2022. "Dynamic Linkages among Climate Change, Mechanization and Agricultural Carbon Emissions in Rural China," IJERPH, MDPI, vol. 19(21), pages 1-24, November.
    12. Mr. Jorge A Chan-Lau, 2017. "Variance Decomposition Networks: Potential Pitfalls and a Simple Solution," IMF Working Papers 2017/107, International Monetary Fund.
    13. Maximilian Böck & Martin Feldkircher & Florian Huber, 2020. "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R," Globalization Institute Working Papers 395, Federal Reserve Bank of Dallas.
    14. Li Wang & Jinyang Tang & Mengqian Tang & Mengying Su & Lili Guo, 2022. "Scale of Operation, Financial Support, and Agricultural Green Total Factor Productivity: Evidence from China," IJERPH, MDPI, vol. 19(15), pages 1-18, July.
    15. Pierre L. Siklos & Martin Stefan & Claudia Wellenreuther, 2020. "Metal prices made in China? A network analysis of industrial metal futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(9), pages 1354-1374, September.
    16. Nalban, Valeriu & Smădu, Andra, 2021. "Asymmetric effects of uncertainty shocks: Normal times and financial disruptions are different," Journal of Macroeconomics, Elsevier, vol. 69(C).
    17. Gaysset, Isabelle & Lagoarde-Segot, Thomas & Neaime, Simon, 2019. "Twin deficits and fiscal spillovers in the EMU's periphery. A Keynesian perspective," Economic Modelling, Elsevier, vol. 76(C), pages 101-116.
    18. Böhl, Gregor & Strobel, Felix, 2020. "US business cycle dynamics at the zero lower bound," Discussion Papers 65/2020, Deutsche Bundesbank.
    19. Beckmann, Joscha & Davidson, Sharada Nia & Koop, Gary & Schüssler, Rainer, 2023. "Cross-country uncertainty spillovers: Evidence from international survey data," Journal of International Money and Finance, Elsevier, vol. 130(C).
    20. Timo Bettendorf & Reinhold Heinlein, 2023. "Connectedness between G10 currencies: Searching for the causal structure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3938-3959, October.
    21. Martin Feldkircher & Elizaveta Lukmanova & Gabriele Tondl, 2019. "Global Factors Driving Inflation and Monetary Policy: A Global VAR Assessment," Department of Economics Working Papers wuwp289, Vienna University of Economics and Business, Department of Economics.
    22. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2020. "How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics," Energy Economics, Elsevier, vol. 90(C).
    23. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Transmission of US and EU Economic Policy Uncertainty Shock to Asian Economies in Bad and Good Times," IZA Discussion Papers 13274, Institute of Labor Economics (IZA).
    24. Pierre L Siklos, 2019. "US monetary policy since the 1950s and the changing content of FOMC minutes," CAMA Working Papers 2019-69, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    25. Donadelli, Michael & Grüning, Patrick, 2017. "Innovation dynamics and fiscal policy: Implications for growth, asset prices, and welfare," SAFE Working Paper Series 171, Leibniz Institute for Financial Research SAFE.
    26. Caggiano, Giovanni & Castelnuovo, Efrem & Figueres, Juan Manuel, 2017. "Economic policy uncertainty and unemployment in the United States: A nonlinear approach," Economics Letters, Elsevier, vol. 151(C), pages 31-34.
    27. Christis Katsouris, 2023. "Limit Theory under Network Dependence and Nonstationarity," Papers 2308.01418, arXiv.org, revised Aug 2023.
    28. Wu, Gabriel Shui Tang & Leung, Pak Ho, 2023. "Do asset-backed stablecoins spread crypto volatility to traditional financial assets? Evidence from Tether," Economics Letters, Elsevier, vol. 229(C).
    29. Kang, Sang Hoon & Maitra, Debasish & Dash, Saumya Ranjan & Brooks, Robert, 2019. "Dynamic spillovers and connectedness between stock, commodities, bonds, and VIX markets," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    30. Cui, Yu & Khan, Sufyan Ullah & Sauer, Johannes & Kipperberg, Gorm & Zhao, Minjuan, 2023. "Agricultural carbon footprint, energy utilization and economic quality: What causes what, and where?," Energy, Elsevier, vol. 278(PA).
    31. Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018. "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers 2018-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    32. Mehmet Balcilar & Zeynel Abidin Ozdemir & Huseyin Ozdemir & Gurcan Aygun & Mark E. Wohar, 2022. "Effectiveness of monetary policy under the high and low economic uncertainty states: evidence from the major Asian economies," Empirical Economics, Springer, vol. 63(4), pages 1741-1769, October.
    33. Lorenzo Bretscher & Alex Hsu & Andrea Tamoni, 2017. "Level and Volatility Shocks to Fiscal Policy: Term Structure Implications," 2017 Meeting Papers 258, Society for Economic Dynamics.
    34. Stolbov, Mikhail & Shchepeleva, Maria & Karminsky, Alexander, 2022. "When central bank research meets Google search: A sentiment index of global financial stress," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    35. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Fed’s unconventional monetary policy and risk spillover in the US financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 42-52.
    36. Yoshihiko Hogen & Yoshiyasu Koide & Yuji Shinozaki, 2022. "Rise of NBFIs and the Global Structural Change in the Transmission of Market Shocks," Bank of Japan Working Paper Series 22-E-14, Bank of Japan.
    37. Ahmed, Khalid & Rehman, Mujeeb Ur & Ozturk, Ilhan, 2017. "What drives carbon dioxide emissions in the long-run? Evidence from selected South Asian Countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1142-1153.
    38. M. Raddant & T. Di Matteo, 2023. "A look at financial dependencies by means of econophysics and financial economics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(4), pages 701-734, October.
    39. Lyu, Yongjian & Yi, Heling & Cao, Jin & Yang, Mo, 2022. "Time-varying monetary policy shocks and the dynamics of Chinese commodity prices," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
    40. Balcilar, Mehmet & Roubaud, David & Usman, Ojonugwa & Wohar, Mark E., 2021. "Moving out of the linear rut: A period-specific and regime-dependent exchange rate and oil price pass-through in the BRICS countries," Energy Economics, Elsevier, vol. 98(C).
    41. Liu, Jianing & Man, Yuanyuan & Dong, Xiuliang, 2023. "Tail dependence and risk spillover effects between China's carbon market and energy markets," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 553-567.
    42. Nhlangwini, Pamela & Mongale, Itumeleng Pleasure, 2019. "Mining Production and Economic Growth Nexus," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 53(3), pages 103-116.
    43. Giovanni Pellegrino & Federico Ravenna & Gabriel Züllig, 2021. "The Impact of Pessimistic Expectations on the Effects of COVID‐19‐Induced Uncertainty in the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 841-869, August.
    44. Bettendorf, Timo & Heinlein, Reinhold, 2019. "Connectedness between G10 currencies: Searching for the causal structure," Discussion Papers 06/2019, Deutsche Bundesbank.
    45. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    46. Ghosh, Bikramaditya & Gubareva, Mariya & Ghosh, Anandita & Paparas, Dimitrios & Vo, Xuan Vinh, 2024. "Food, energy, and water nexus: A study on interconnectedness and trade-offs," Energy Economics, Elsevier, vol. 133(C).
    47. Caggiano, Giovanni & Castelnuovo, Efrem & Pellegrino, Giovanni, 2017. "Estimating the real effects of uncertainty shocks at the Zero Lower Bound," European Economic Review, Elsevier, vol. 100(C), pages 257-272.
    48. Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021. "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers 2021-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    49. Joshua Bernstein & Michael D. Plante & Alexander W. Richter & Nathaniel A. Throckmorton, 2021. "Countercyclical Fluctuations in Uncertainty are Endogenous," Working Papers 2109, Federal Reserve Bank of Dallas.
    50. Martin Feldkircher & Pierre L. Siklos, 2018. "Global inflation dynamics and inflation expectations," CAMA Working Papers 2018-60, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    51. Maksim Isakin & Phuong V. Ngo, 2020. "Variance Decomposition Analysis for Nonlinear Economic Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1362-1374, December.
    52. Raul Ibarra, 2016. "How important is the credit channel in the transmission of monetary policy in Mexico?," Applied Economics, Taylor & Francis Journals, vol. 48(36), pages 3462-3484, August.
    53. Alexander M. Karminsky & Ekaterina V. Seryakova, 2019. "Assessment of Cross-Border Transmission of Systemic Financial Risk in EU Countries," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 5, pages 119-129, October.
    54. Houjian Li & Xiaolei Zhou & Mengqian Tang & Lili Guo, 2022. "Impact of Population Aging and Renewable Energy Consumption on Agricultural Green Total Factor Productivity in Rural China: Evidence from Panel VAR Approach," Agriculture, MDPI, vol. 12(5), pages 1-19, May.
    55. Xingwei Hu, 2021. "Decoding Causality by Fictitious VAR Modeling," Papers 2111.07465, arXiv.org, revised Nov 2021.
    56. Chen, Peng & Miao, Xinru, 2024. "Understanding the role of China's factors in international commodity price fluctuations: A perspective of monetary-fiscal policy interaction," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1464-1483.
    57. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    58. Donal Smith, 2016. "The International Impact of Financial Shocks: A Global VAR and Connectedness Measures Approach," Discussion Papers 16/07, Department of Economics, University of York.
    59. Niels Gillmann & Ostap Okhrin, 2023. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Papers 2302.02808, arXiv.org.
    60. David Ubilava, 2018. "The Role of El Niño Southern Oscillation in Commodity Price Movement and Predictability," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(1), pages 239-263.
    61. Verena Dominique Kouassi & Hongyi Xu & Chukwunonso Philip Bosah & Twum Edwin Ayimadu & Mbula Ngoy Nadege, 2024. "Sustainable Energy Usage for Africa: The Role of Foreign Direct Investment in Green Growth Practices to Mitigate CO 2 Emissions," Energies, MDPI, vol. 17(15), pages 1-23, August.
    62. Kyriaki-Argyro Tsioptsia & Eleni Zafeiriou & Dimitrios Niklis & Nikolaos Sariannidis & Constantin Zopounidis, 2022. "The Corporate Economic Performance of Environmentally Eligible Firms Nexus Climate Change: An Empirical Research in a Bayesian VAR Framework," Energies, MDPI, vol. 15(19), pages 1-16, October.
    63. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
    64. Ghosh, Bikramaditya & Pham, Linh & Gubareva, Mariya & Teplova, Tamara, 2023. "Energy transition metals and global sentiment: Evidence from extreme quantiles," Resources Policy, Elsevier, vol. 86(PA).
    65. Baek, Ingul & Liu, Jia & Noh, Sanha, 2024. "Real estate uncertainty and financial conditions over the business cycle," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 656-675.
    66. Colombo, Valentina & Paccagnini, Alessia, 2020. "Does the credit supply shock have asymmetric effects on macroeconomic variables?," Economics Letters, Elsevier, vol. 188(C).
    67. SBIA, Rashid & Al Rousan, Sahel, 2015. "Does Financial Development Induce Economic Growth in UAE? The Role of Foreign Direct Investment and Capitalization," MPRA Paper 64599, University Library of Munich, Germany.
    68. Kole, Erik & van Dijk, Dick, 2023. "Moments, shocks and spillovers in Markov-switching VAR models," Journal of Econometrics, Elsevier, vol. 236(2).
    69. Guangxi Cao & Fei Xie & Meijun Ling, 2022. "Spillover effects in Chinese carbon, energy and financial markets," International Finance, Wiley Blackwell, vol. 25(3), pages 416-434, December.
    70. Frédéric Karamé, 2015. "Asymmetries and Markov-switching structural VAR," Post-Print hal-02296101, HAL.
    71. Lili Guo & Shuang Zhao & Yuting Song & Mengqian Tang & Houjian Li, 2022. "Green Finance, Chemical Fertilizer Use and Carbon Emissions from Agricultural Production," Agriculture, MDPI, vol. 12(3), pages 1-18, February.
    72. Namahoro, J.P. & Wu, Q. & Zhou, N. & Xue, S., 2021. "Impact of energy intensity, renewable energy, and economic growth on CO2 emissions: Evidence from Africa across regions and income levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    73. Sharada Nia Davidson, 2022. "Regional Integration and Decoupling in the Asia Pacific: A Bayesian Panel VAR Approach," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(4), pages 773-807, December.
    74. Strobel, Johannes & Lee, Gabriel & Dorofeenko, Victor & Salyer, Kevin, 2019. "Time-Varying Risk Shocks and the Zero Lower Bound," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203491, Verein für Socialpolitik / German Economic Association.
    75. Namahoro, Jean Pierre & Wu, Qiaosheng & Hui, Su, 2023. "Asymmetric linkage between copper-cobalt productions and economic growth: Evidence from Republic Democratic of Congo," Resources Policy, Elsevier, vol. 83(C).
    76. Zhou, Xiaoran & Enilov, Martin & Parhi, Mamata, 2024. "Does oil spin the commodity wheel? Quantile connectedness with a common factor error structure across energy and agricultural markets," Energy Economics, Elsevier, vol. 132(C).
    77. Nam, Kyungsik, 2021. "Investigating the effect of climate uncertainty on global commodity markets," Energy Economics, Elsevier, vol. 96(C).
    78. Christopher Thiem, 2020. "Cross-Category, Trans-Pacific Spillovers of Policy Uncertainty and Financial Market Volatility," Open Economies Review, Springer, vol. 31(2), pages 317-342, April.
    79. Luis Alberto Delgado-de-la-Garza & Gonzalo Adolfo Garza-Rodríguez & Daniel Alejandro Jacques-Osuna & Alejandro Múgica-Lara & Carlos Alberto Carrasco, 2021. "Does the use of a big data variable improve monetary policy estimates? Evidence from Mexico," Economics and Business Letters, Oviedo University Press, vol. 10(4), pages 383-393.
    80. Michael Donadelli & Marcus Jüppner & Antonio Paradiso & Christian Schlag, 2019. "Temperature Volatility Risk," Working Papers 2019:05, Department of Economics, University of Venice "Ca' Foscari".
    81. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Aygun, Gurcan & Wohar, Mark E., 2021. "Effectives of Monetary Policy under the High and Low Economic Uncertainty States: Evidence from the Major Asian Economies," IZA Discussion Papers 14420, Institute of Labor Economics (IZA).
    82. Michael Donadelli & Marcus Jüppner & Antonio Paradiso & Christian Schlag, 2021. "Computing Macro-Effects and Welfare Costs of Temperature Volatility: A Structural Approach," Computational Economics, Springer;Society for Computational Economics, vol. 58(2), pages 347-394, August.
    83. Maria Bolboaca & Sarah Fischer, 2019. "News Shocks: Different Effects in Boom and Recession?," Working Papers 19.01, Swiss National Bank, Study Center Gerzensee.
    84. Yu, Zhen & Liu, Wei & Yang, Fuyu, 2023. "A central bankers’ sentiment index of global financial cycle," Finance Research Letters, Elsevier, vol. 57(C).
    85. Bikramaditya Ghosh & Dimitrios Paparas, 2023. "Is There Any Pattern Regarding the Vulnerability of Smart Contracts in the Food Supply Chain to a Stressed Event? A Quantile Connectedness Investigation," JRFM, MDPI, vol. 16(2), pages 1-12, January.
    86. Nyholm, Ken, 2016. "US-euro area term structure spillovers, implications for central banks," Working Paper Series 1980, European Central Bank.
    87. Hamill, Philip A. & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A. & Waterworth, James, 2021. "Was a deterioration in ‘connectedness’ a leading indicator of the European sovereign debt crisis?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    88. Rashid Sbia & Sahel Alrousan, 2016. "Does Financial Development Induce Economic Growth in UAE? The Role of Capitalization and Foreign Direct Investment," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 703-710.
    89. Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2019. "How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study," Energy Economics, Elsevier, vol. 84(C).
    90. Barbaglia, Luca & Croux, Christophe & Wilms, Ines, 2020. "Volatility spillovers in commodity markets: A large t-vector autoregressive approach," Energy Economics, Elsevier, vol. 85(C).
    91. Yépez, Carlos & Dzikpe, Francis, 2022. "Accounting for real exchange rates in emerging economies: The role of commodity prices," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 476-492.

  4. Karolin Kirschenmann & Tuomas Malinen & Henri Nyberg, 2014. "The risk of financial crises: Is it in real or financial factors?," Working Papers 336, ECINEQ, Society for the Study of Economic Inequality.

    Cited by:

    1. Tuomas Malinen, 2016. "Does income inequality contribute to credit cycles?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(3), pages 309-325, September.
    2. Mark Setterfield & Yun K. Kim, 2018. "Varieties of Capitalism, Increasing Income Inequality, and the Sustainability of Long-Run Growth," Working Papers 2018-01, University of Massachusetts Boston, Economics Department.

  5. Nyberg, Henri & Saikkonen, Pentti, 2012. "Forecasting with a noncausal VAR model," Bank of Finland Research Discussion Papers 33/2012, Bank of Finland.

    Cited by:

    1. Chun Deng & Jie-Fang Dong, 2016. "Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model," Sustainability, MDPI, vol. 8(9), pages 1-16, August.
    2. Christian Gourieroux & Joann Jasiak, 2016. "Filtering, Prediction and Simulation Methods for Noncausal Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 405-430, May.
    3. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    4. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    5. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
    6. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.
    7. Giusto Andrea & İşcan Talan B., 2018. "The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
    8. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.
    9. Pagnottoni, Paolo & Spelta, Alessandro, 2023. "The motifs of risk transmission in multivariate time series: Application to commodity prices," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    10. Gerui Li & Yalin Lei & Jianping Ge & Sanmang Wu, 2017. "The Empirical Relationship between Mining Industry Development and Environmental Pollution in China," IJERPH, MDPI, vol. 14(3), pages 1-20, March.

  6. Nyberg, Henri & Saikkonen, Pentti, 2012. "Forecasting with a noncausal VAR model," Bank of Finland Research Discussion Papers 33/2012, Bank of Finland.

    Cited by:

    1. Chun Deng & Jie-Fang Dong, 2016. "Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model," Sustainability, MDPI, vol. 8(9), pages 1-16, August.
    2. Christian Gourieroux & Joann Jasiak, 2016. "Filtering, Prediction and Simulation Methods for Noncausal Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(3), pages 405-430, May.
    3. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    4. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    5. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
    6. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.
    7. Giusto Andrea & İşcan Talan B., 2018. "The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
    8. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.
    9. Pagnottoni, Paolo & Spelta, Alessandro, 2023. "The motifs of risk transmission in multivariate time series: Application to commodity prices," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    10. Gerui Li & Yalin Lei & Jianping Ge & Sanmang Wu, 2017. "The Empirical Relationship between Mining Industry Development and Environmental Pollution in China," IJERPH, MDPI, vol. 14(3), pages 1-20, March.

  7. Lanne, Markku & Nyberg, Henri & Saarinen, Erkka, 2011. "Forecasting U.S. Macroeconomic and Financial Time Series with Noncausal and Causal AR Models: A Comparison," MPRA Paper 30254, University Library of Munich, Germany.

    Cited by:

    1. Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2010. "Optimal Forecasting of Noncausal Autoregressive Time Series," MPRA Paper 23648, University Library of Munich, Germany.
    2. Henri Nyberg & Markku Lanne & Erkka Saarinen, 2012. "Does noncausality help in forecasting economic time series?," Economics Bulletin, AccessEcon, vol. 32(4), pages 2849-2859.
    3. Gourieroux, Christian & Jasiak, Joann, 2018. "Misspecification of noncausal order in autoregressive processes," Journal of Econometrics, Elsevier, vol. 205(1), pages 226-248.

Articles

  1. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.

    Cited by:

    1. Stolyarov, Dmitriy & Tesar, Linda L., 2021. "Interest rate trends in a global context," Economic Modelling, Elsevier, vol. 101(C).
    2. BenSaïda, Ahmed & Litimi, Houda & Abdallah, Oussama, 2018. "Volatility spillover shifts in global financial markets," Economic Modelling, Elsevier, vol. 73(C), pages 343-353.
    3. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.

  2. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.

    Cited by:

    1. Christian Gourieroux & Andrew Hencic & Joann Jasiak, 2021. "Forecast performance and bubble analysis in noncausal MAR(1, 1) processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 301-326, March.
    2. Hecq, A.W. & Lieb, L.M. & Telg, J.M.A., 2015. "Identification of Mixed Causal-Noncausal Models : How Fat Should We Go?," Research Memorandum 035, Maastricht University, Graduate School of Business and Economics (GSBE).
    3. Pincheira, Pablo & Hardy, Nicolás, 2019. "Forecasting Aluminum Prices with Commodity Currencies," MPRA Paper 97005, University Library of Munich, Germany.
    4. Hecq, Alain & Voisin, Elisa, 2021. "Forecasting bubbles with mixed causal-noncausal autoregressive models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 29-45.
    5. Juan Manuel Candelo-Viáfara & Carlos Hernán Gonzáles-Campo, 2022. "Efecto de la incertidumbre en las organizaciones del mercado accionario: una herramienta para la toma de decisiones y la inteligencia organizacional," Estudios Gerenciales, Universidad Icesi, vol. 38(162), pages 57-68, March.
    6. Ding, Shusheng & Zhang, Yongmin, 2020. "Cross market predictions for commodity prices," Economic Modelling, Elsevier, vol. 91(C), pages 455-462.
    7. Pincheira, Pablo & Jarsun, Nabil, 2020. "Summary of the Paper Entitled: Forecasting Fuel Prices with the Chilean Exchange Rate," MPRA Paper 105056, University Library of Munich, Germany.
    8. Alain Hecq & Joao Issler & Elisa Voisin, 2022. "A short term credibility index for central banks under inflation targeting: an application to Brazil," Papers 2205.00924, arXiv.org, revised Jul 2022.
    9. Frédérique Bec & Heino Bohn Nielsen & Sarra Saïdi, 2020. "Mixed Causal–Noncausal Autoregressions: Bimodality Issues in Estimation and Unit Root Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1413-1428, December.
    10. Alain Hecq & Elisa Voisin, 2019. "Predicting crashes in oil prices during the COVID-19 pandemic with mixed causal-noncausal models," Papers 1911.10916, arXiv.org, revised May 2022.
    11. Kassouri, Yacouba & Altıntaş, Halil, 2020. "Commodity terms of trade shocks and real effective exchange rate dynamics in Africa's commodity-exporting countries," Resources Policy, Elsevier, vol. 68(C).
    12. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    13. Hardy, Nicolás & Ferreira, Tiago & Quinteros, Maria J. & Magner, Nicolás S., 2023. "“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone," Resources Policy, Elsevier, vol. 86(PA).
    14. Pincheira, Pablo & Hardy, Nicolas, 2018. "Forecasting Base Metal Prices with Commodity Currencies," MPRA Paper 83564, University Library of Munich, Germany.
    15. Pincheira, Pablo & Hardy, Nicolas, 2018. "The predictive relationship between exchange rate expectations and base metal prices," MPRA Paper 89423, University Library of Munich, Germany.
    16. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    17. Pincheira-Brown, Pablo & Bentancor, Andrea & Hardy, Nicolás & Jarsun, Nabil, 2022. "Forecasting fuel prices with the Chilean exchange rate: Going beyond the commodity currency hypothesis," Energy Economics, Elsevier, vol. 106(C).
    18. Juan Manuel Candelo-Viáfara, 2021. "Monthly Financial and Economic Uncertainty Index (IMIFE) for the Colombian Economy," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 95, pages 85-104, July-Dece.
    19. Alain Hecq & Daniel Velasquez-Gaviria, 2023. "Spectral identification and estimation of mixed causal-noncausal invertible-noninvertible models," Papers 2310.19543, arXiv.org.
    20. Jose Eduardo Gomez-Gonzalez & Jorge Hirs-Garzon & Jorge M. Uribe, 2017. "Dynamic Connectedness and Causality between Oil prices and Exchange Rates," Borradores de Economia 1025, Banco de la Republica de Colombia.
    21. Christian Gouriéroux & Yang Lu, 2023. "Noncausal affine processes with applications to derivative pricing," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 766-796, July.
    22. Alain Hecq & Daniel Velasquez-Gaviria, 2022. "Spectral estimation for mixed causal-noncausal autoregressive models," Papers 2211.13830, arXiv.org.
    23. Pablo Pincheira Brown & Nicolás Hardy, 2023. "Forecasting base metal prices with exchange rate expectations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2341-2362, December.

  3. Kirschenmann, Karolin & Malinen, Tuomas & Nyberg, Henri, 2016. "The risk of financial crises: Is there a role for income inequality?," Journal of International Money and Finance, Elsevier, vol. 68(C), pages 161-180.

    Cited by:

    1. Georgescu, Oana-Maria & Martín, Diego Vila, 2021. "Do macroprudential measures increase inequality? Evidence from the euro area household survey," Working Paper Series 2567, European Central Bank.
    2. António R. Antunes & Diana Bonfim & Nuno Monteiro & Paulo M.M. Rodrigues, 2016. "Forecasting banking crises with dynamic panel probit models," Working Papers w201613, Banco de Portugal, Economics and Research Department.
    3. Thanh Cong Nguyen, 2022. "The effects of financial crisis on income inequality," Development Policy Review, Overseas Development Institute, vol. 40(6), November.
    4. Luca Eduardo Fierro & Federico Giri & Alberto Russo, 2023. "Inequality-Constrained Monetary Policy in a Financialized Economy," Working Papers 2023/02, Economics Department, Universitat Jaume I, Castellón (Spain).
    5. Gu, Xinhua & Tam, Pui Sun & Lei, Chun Kwok, 2021. "The effects of inequality in the 1997–98 Asian crisis and the 2008–09 global tsunami: The case of five Asian economies," Journal of International Money and Finance, Elsevier, vol. 110(C).
    6. Li, Xiang & Su, Dan, 2020. "Capital account liberalisation does worsen income inequality," IWH Discussion Papers 7/2020, Halle Institute for Economic Research (IWH).
    7. Rym Ayadi & Sami B. Naceur & Sandra Challita, 2023. "Does income inequality really matter for credit booms?," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 52(1), February.
    8. Mehdi El Herradi & Aurélien Leroy, 2021. "The rich, the poor, and the middle class: banking crises and income distribution," AMSE Working Papers 2136, Aix-Marseille School of Economics, France.
    9. Ibrahim Mohamed Ali Ali & Imed Attiaoui & Rabeh Khalfaoui & Aviral Kumar Tiwari, 2022. "The Effect of Urbanization and Industrialization on Income Inequality: An Analysis Based on the Method of Moments Quantile Regression," Post-Print hal-03797572, HAL.
    10. Saktinil Roy, 2023. "Do Changes in Risk Perception Predict Systemic Banking Crises?," JRFM, MDPI, vol. 16(11), pages 1-15, October.
    11. Jérôme Creel & Paul Hubert & Fabien Labondance, 2023. "Credit, banking fragility, and economic performance," Post-Print hal-04523669, HAL.
    12. Mehmet Akif Destek & Bilge Koksel, 2019. "Income inequality and financial crises: evidence from the bootstrap rolling window," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-23, December.
    13. Kim, Dong-Hyeon & Lin, Shu-Chin, 2023. "Income inequality, inflation and financial development," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 468-487.
    14. Klein, Mathias & Winkler, Roland, 2019. "Austerity, inequality, and private debt overhang," European Journal of Political Economy, Elsevier, vol. 57(C), pages 89-106.
    15. Filip Chybalski, 2022. "Intergenerational income distribution before and after the great recession: winners and losers," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 49(3), pages 311-327, September.
    16. Shengquan Wang & Rong Luo, 2024. "Income distribution, financial liberalisations and banking stability: Theory and international evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2837-2864, July.
    17. Mehmet Balcilar & Edmond Berisha & Rangan Gupta & Christian Pierdzioch, 2020. "Time-Varying Evidence of Predictability of Financial Stress in the United States over a Century: The Role of Inequality," Working Papers 202054, University of Pretoria, Department of Economics.
    18. Bellettini, Giorgio & Delbono, Flavio & Karlström, Peter & Pastorello, Sergio, 2019. "Income inequality and banking crises: Testing the level hypothesis directly," Journal of Macroeconomics, Elsevier, vol. 62(C).
    19. Xiang Li & Dan Su, 2021. "Does Capital Account Liberalization Affect Income Inequality?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 377-410, April.
    20. Xinhua Gu & Chun Kwok Lei & Qingbin Zhao & Nian Liu, 2024. "Different experiences of Asian emerging‐market economies in the two major financial crises," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3286-3308, July.
    21. Pascal Paul, 2023. "Historical Patterns of Inequality and Productivity around Financial Crises," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(7), pages 1641-1665, October.
    22. Goodness C. Aye & Laurence Harris & Junior T. Chiweza, 2020. "Monetary policy and wealth inequality in South Africa: Evidence from tax administrative data," WIDER Working Paper Series wp-2020-174, World Institute for Development Economic Research (UNU-WIDER).
    23. Rémi Bazillier & Jérôme Héricourt & Samuel Ligonnière, 2019. "Structure of Income Inequality and Household Leverage: Theory and Cross-Country Evidence," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02079212, HAL.
    24. Thomas Hauner, 2020. "Aggregate wealth and its distribution as determinants of financial crises," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(3), pages 319-338, September.
    25. Salvatore Morelli, 2018. "Banking crises in the US: the response of top income shares in a historical perspective," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(2), pages 257-294, June.
    26. Muinelo-Gallo, Leonel, 2022. "Business cycles and redistribution: The role of government quality," Economic Systems, Elsevier, vol. 46(4).
    27. Bazillier, Rémi & Héricourt, Jérôme & Ligonnière, Samuel, 2021. "Structure of income inequality and household leverage: Cross-country causal evidence," European Economic Review, Elsevier, vol. 132(C).
    28. Roy, Saktinil, 2022. "What drives the systemic banking crises in advanced economies?," Global Finance Journal, Elsevier, vol. 54(C).
    29. Pascal Paul, 2018. "Historical Patterns of Inequality and Productivity around Financial Crises," 2018 Meeting Papers 583, Society for Economic Dynamics.
    30. Monroy-Taborda Sebastián, 2023. "Bank Runs and Inequality," Asociación Argentina de Economía Política: Working Papers 4672, Asociación Argentina de Economía Política.
    31. Wang, Shengquan, 2023. "Income inequality and systemic banking crises: A nonlinear nexus," Economic Systems, Elsevier, vol. 47(4).
    32. Peter Karlström, 2023. "Macroprudential Policy, Credit Booms, and Banks' Systemic Risk," CEMLA Working Paper Series 03/2023, CEMLA.
    33. Woo, Jaejoon, 2023. "Financial crises and inequality: New evidence from a panel of 17 advanced economies," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    34. Brett, Craig & Sarkar, Saikat, 2022. "Financial bubbles and income inequality," MPRA Paper 112070, University Library of Munich, Germany.

  4. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    See citations under working paper version above.
  5. Markku Lanne & Henri Nyberg, 2016. "Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(4), pages 595-603, August.
    See citations under working paper version above.
  6. Nyberg, Henri, 2014. "A Bivariate Autoregressive Probit Model: Business Cycle Linkages And Transmission Of Recession Probabilities," Macroeconomic Dynamics, Cambridge University Press, vol. 18(4), pages 838-862, June.

    Cited by:

    1. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    2. Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
    3. António R. Antunes & Diana Bonfim & Nuno Monteiro & Paulo M.M. Rodrigues, 2016. "Forecasting banking crises with dynamic panel probit models," Working Papers w201613, Banco de Portugal, Economics and Research Department.
    4. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    5. Daniel Ofori-Sasu & Emmanuel Sarpong-Kumankoma & Saint Kuttu & Elikplimi Komla Agbloyor & Joshua Yindenaba Abor, 2024. "Risk-taking and systemic banking crisis in Africa: do regulatory policy framework provide new insight in threshold models?," Risk Management, Palgrave Macmillan, vol. 26(2), pages 1-37, May.
    6. Baumann, Ursel & Gómez Salvador, Ramón & Seitz, Franz, 2018. "Global recessions and booms: What do probit models tell us?," Weidener Diskussionspapiere 61, University of Applied Sciences Amberg-Weiden (OTH).
    7. Marius M. Mihai, 2020. "Do credit booms predict US recessions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 887-910, September.
    8. Pönkä, Harri & Stenborg, Markku, 2018. "Forecasting the state of the Finnish business cycle," MPRA Paper 91226, University Library of Munich, Germany.
    9. Goodness C. Aye & Christina Christou & Luis A. Gil-Alana & Rangan Gupta, 2016. "Forecasting the Probability of Recessions in South Africa: The Role of Decomposed Term-Spread and Economic Policy Uncertainty," Working Papers 201680, University of Pretoria, Department of Economics.
    10. Harri Ponka, 2017. "The Role of Credit in Predicting US Recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 469-482, August.
    11. Mei-Chih Wang & Pao-Lan Kuo & Chan-Sheng Chen & Chien-Liang Chiu & Tsangyao Chang, 2020. "Yield Spread and Economic Policy Uncertainty: Evidence from Japan," Sustainability, MDPI, vol. 12(10), pages 1-14, May.
    12. Stanislav Anatolyev & Nikolay Gospodinov, 2019. "Multivariate Return Decomposition: Theory and Implications," Econometric Reviews, Taylor & Francis Journals, vol. 38(5), pages 487-508, May.
    13. Seulki Chung, 2023. "Inside the black box: Neural network-based real-time prediction of US recessions," Papers 2310.17571, arXiv.org, revised May 2024.
    14. Kar, Sabyasachi & Roy, Amrita & Sen, Kunal, 2019. "The double trap: Institutions and economic development," Economic Modelling, Elsevier, vol. 76(C), pages 243-259.
    15. Pönkä, Harri & Zheng, Yi, 2019. "The role of oil prices on the Russian business cycle," Research in International Business and Finance, Elsevier, vol. 50(C), pages 70-78.

  7. Nyberg, Henri & Saikkonen, Pentti, 2014. "Forecasting with a noncausal VAR model," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 536-555.
    See citations under working paper version above.
  8. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.

    Cited by:

    1. Edward J. Lusk, 2019. "A Benchmarked Evaluation of a Selected CapitalCube Interval-Scaled Market Performance Variable," Accounting and Finance Research, Sciedu Press, vol. 8(2), pages 1-1, May.
    2. Stanislav Anatolyev, 2021. "Directional news impact curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 94-107, January.
    3. Edward J. Lusk, 2019. "Time Series Forecasting in Stock Trading Markets: The Turning Point Curiosity," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 8(4), pages 01-16, July.
    4. 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.
    5. Yi-Chieh Wen & Bin Li, 2020. "Lagged country returns and international stock return predictability during business cycle recession periods," Applied Economics, Taylor & Francis Journals, vol. 52(46), pages 5005-5019, October.
    6. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
    7. Efthymios Pavlidis & Alisa Yusupova & Ivan Paya & David Peel & Enrique Martínez-García & Adrienne Mack & Valerie Grossman, 2016. "Episodes of Exuberance in Housing Markets: In Search of the Smoking Gun," The Journal of Real Estate Finance and Economics, Springer, vol. 53(4), pages 419-449, November.
    8. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    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. Pönkä, Harri, 2014. "Predicting the direction of US stock markets using industry returns," MPRA Paper 62942, University Library of Munich, Germany.
    11. 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.
    12. Linh Nguyen & Vilém Novák & Soheyla Mirshahi, 2020. "Trend‐cycle Estimation Using Fuzzy Transform and Its Application for Identifying Bull and Bear Phases in Markets," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(3), pages 111-124, July.
    13. Dockner, Engelbert J. & Mayer, Manuel & Zechner, Josef, 2013. "Sovereign bond risk premiums," CFS Working Paper Series 2013/28, Center for Financial Studies (CFS).
    14. 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.
    15. Fernandez-Perez, Adrian & Fernández-Rodríguez, Fernando & Sosvilla-Rivero, Simón, 2014. "The term structure of interest rates as predictor of stock returns: Evidence for the IBEX 35 during a bear market," International Review of Economics & Finance, Elsevier, vol. 31(C), pages 21-33.
    16. Gambarelli, Luca & Marchi, Gianluca & Muzzioli, Silvia, 2023. "Hedging effectiveness of cryptocurrencies in the European stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    17. Edward J. Lusk, 2018. "Evaluation of the Predictive Validity of the CapitalCubeâ„¢ Market Navigation Platform," Accounting and Finance Research, Sciedu Press, vol. 7(3), pages 1-39, August.
    18. Fangming Xu & Huainan Zhao & Liyi Zheng, 2022. "Investment momentum: A two‐dimensional behavioural strategy," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1191-1207, January.
    19. Baetje, Fabian & Menkhoff, Lukas, 2013. "Macro determinants of U.S. stock market risk premia in bull and bear markets," Hannover Economic Papers (HEP) dp-520, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    20. Fokianos, Konstantinos & Moysiadis, Theodoros, 2017. "Binary time series models driven by a latent process," Econometrics and Statistics, Elsevier, vol. 2(C), pages 117-130.
    21. Borjigin, Sumuya & Yang, Yating & Yang, Xiaoguang & Sun, Leilei, 2018. "Econometric testing on linear and nonlinear dynamic relation between stock prices and macroeconomy in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 107-115.
    22. Yang Lu, 2020. "A simple parameter‐driven binary time series model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 187-199, March.
    23. Jonathan Iworiso & Spyridon Vrontos, 2020. "On the directional predictability of equity premium using machine learning techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 449-469, April.
    24. Narayan, S. & Le, T.-H. & Sriananthakumar, S., 2018. "The influence of terrorism risk on stock market integration: Evidence from eight OECD countries," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 247-259.
    25. Wu, Shue-Jen & Lee, Wei-Ming, 2015. "Predicting severe simultaneous bear stock markets using macroeconomic variables as leading indicators," Finance Research Letters, Elsevier, vol. 13(C), pages 196-204.
    26. Ngene, Geoffrey M., 2021. "What drives dynamic connectedness of the U.S equity sectors during different business cycles?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    27. Gupta, Rangan & Risse, Marian & Volkman, David A. & Wohar, Mark E., 2019. "The role of term spread and pattern changes in predicting stock returns and volatility of the United Kingdom: Evidence from a nonparametric causality-in-quantiles test using over 250 years of data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 391-405.
    28. Mönch, Emanuel & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," Discussion Papers 25/2021, Deutsche Bundesbank.
    29. Markku Lanne & Henri Nyberg, 2015. "Nonlinear dynamic interrelationships between real activity and stock returns," CREATES Research Papers 2015-36, Department of Economics and Business Economics, Aarhus University.
    30. Wu, Shue-Jen & Lee, Wei-Ming, 2015. "Intertemporal risk–return relationships in bull and bear markets," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 308-325.
    31. Pawel Dlotko & Wanling Qiu & Simon Rudkin, 2022. "Topological Data Analysis Ball Mapper for Finance," Papers 2206.03622, arXiv.org.
    32. Lohrmann, Christoph & Luukka, Pasi, 2019. "Classification of intraday S&P500 returns with a Random Forest," International Journal of Forecasting, Elsevier, vol. 35(1), pages 390-407.
    33. Paulo M.M. Rodrigues & João Cruz, 2018. "Structural Changes in the Duration of Bull Markets and Business Cycle Dynamics," Working Papers w201814, Banco de Portugal, Economics and Research Department.
    34. 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.
    35. Julien Chevallier & Bangzhu Zhu & Lyuyuan Zhang, 2021. "Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 537-575, February.
    36. Chen, Nan-Kuang & Chen, Shiu-Sheng & Chou, Yu-Hsi, 2013. "Further evidence on bear market predictability: The role of the external finance premium," MPRA Paper 49093, University Library of Munich, Germany.
    37. Tzu-Pu Chang & Yu-Cheng Chang & Po-Ching Chou, 2022. "The Trend is Your Friend: A Note on An Ensemble Learning Approach to Finding It," Bulletin of Applied Economics, Risk Market Journals, vol. 9(1), pages 19-25.
    38. Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2021. "Sticky Stock Market Analysts," JRFM, MDPI, vol. 14(12), pages 1-27, December.
    39. Fokianos, Konstantinos & Truquet, Lionel, 2019. "On categorical time series models with covariates," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3446-3462.
    40. 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.

  9. Nyberg, Henri, 2012. "Risk-Return Tradeoff in U.S. Stock Returns over the Business Cycle," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(1), pages 137-158, February.

    Cited by:

    1. Ehab Yamani & David Rakowski, 2018. "Cash Flow and Discount Rate Risk in the Investment Effect: A Downside Risk Approach," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 8(03), pages 1-40, September.
    2. Yi-Chieh Wen & Bin Li, 2020. "Lagged country returns and international stock return predictability during business cycle recession periods," Applied Economics, Taylor & Francis Journals, vol. 52(46), pages 5005-5019, October.
    3. Hedegaard, Esben & Hodrick, Robert J., 2016. "Estimating the risk-return trade-off with overlapping data inference," Journal of Banking & Finance, Elsevier, vol. 67(C), pages 135-145.
    4. Naqi Shah, Sadia & Qayyum, Abdul, 2016. "Analyse Risk-Return Paradox: Evidence from Electricity Sector of Pakistan," MPRA Paper 68783, University Library of Munich, Germany.
    5. 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.
    6. Nektarios Aslanidis & Charlotte Christiansen & Neophytos Lambertides & Christos S. Savva, 2019. "Idiosyncratic volatility puzzle: influence of macro-finance factors," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 381-401, February.
    7. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    8. Esben Hedegaard & Robert J. Hodrick, 2014. "Measuring the Risk-Return Tradeoff with Time-Varying Conditional Covariances," NBER Working Papers 20245, National Bureau of Economic Research, Inc.
    9. Papadamou, Stephanos & Sidiropoulos, Moïse & Spyromitros, Eleftherios, 2014. "Does central bank transparency affect stock market volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 31(C), pages 362-377.
    10. Eric Ghysels & Pierre Guérin & Massimiliano Marcellino, 2013. "Regime Switches in the Risk-Return Trade-Off," Staff Working Papers 13-51, Bank of Canada.
    11. Cotter, John & Salvador, Enrique, 2022. "The non-linear trade-off between return and risk and its determinants," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 100-132.
    12. Hafner, Christian & Kyriakopoulou, Dimitra, 2020. "Exponential-Type GARCH Models With Linear-in-Variance Risk Premium," LIDAM Reprints ISBA 2020029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Ahmed, Mohamed S. & Alhadab, Mohammad, 2020. "Momentum, asymmetric volatility and idiosyncratic risk-momentum relation: Does technology-sector matter?," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 355-371.
    14. Jyri Kinnunen & Minna Martikainen, 2017. "Dynamic Autocorrelation and International Portfolio Allocation," Multinational Finance Journal, Multinational Finance Journal, vol. 21(1), pages 21-48, March.
    15. Kinnunen, Jyri, 2014. "Risk-return trade-off and serial correlation: Do volume and volatility matter?," Journal of Financial Markets, Elsevier, vol. 20(C), pages 1-19.
    16. Jonathan Iworiso & Spyridon Vrontos, 2020. "On the directional predictability of equity premium using machine learning techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 449-469, April.
    17. Piao, Xiaorui & Mei, Bin & Xue, Yuan, 2016. "Comparing the financial performance of timber REITs and other REITs," Forest Policy and Economics, Elsevier, vol. 72(C), pages 115-121.
    18. 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 Emerging Economies (BOFIT).
    19. Liu, Xiaochun, 2017. "Unfolded risk-return trade-offs and links to Macroeconomic Dynamics," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 1-19.
    20. Ma, Chaoqun & Mi, Xianhua & Cai, Zongwu, 2020. "Nonlinear and time-varying risk premia," China Economic Review, Elsevier, vol. 62(C).
    21. Wu, Shue-Jen & Lee, Wei-Ming, 2015. "Intertemporal risk–return relationships in bull and bear markets," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 308-325.
    22. Chelikani, Surya & Marks, Joseph M. & Nam, Kiseok, 2023. "Volatility feedback effect and risk-return tradeoff," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 49-65.
    23. Huang, Lin & Wang, Zijun, 2014. "Is the investment factor a proxy for time-varying investment opportunities? The US and international evidence," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 219-232.
    24. Jia, Yun & Yang, Chunpeng, 2017. "Disagreement and the risk-return relation," Economic Modelling, Elsevier, vol. 64(C), pages 97-104.
    25. Kinnunen, Jyri, 2013. "Dynamic return predictability in the Russian stock market," Emerging Markets Review, Elsevier, vol. 15(C), pages 107-121.
    26. Yang, Chunpeng & Jia, Yun, 2016. "Buy-sell imbalance and the mean-variance relation," Pacific-Basin Finance Journal, Elsevier, vol. 40(PA), pages 49-58.
    27. Shoka Hayaki, 2024. "The Impact of Individual Loss Aversion on Market Risk-Return Trade-off: A Non-linear Approach," Discussion Paper Series DP2024-05, Research Institute for Economics & Business Administration, Kobe University.
    28. Nektarios Aslanidis & Charlotte Christiansen & Christos S. Savva, 2013. "Risk-Return Trade-Off for European Stock Markets," CREATES Research Papers 2013-31, Department of Economics and Business Economics, Aarhus University.
    29. Tan, Zhengxun & Xiao, Binuo & Huang, Yilong & Zhou, Li, 2021. "Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    30. Krzysztof DRACHAL, 2017. "Volatility Clustering, Leverage Effects and Risk-Return Tradeoff in the Selected Stock Markets in the CEE Countries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-53, September.
    31. Kinnunen, Jyri, 2017. "Dynamic cross-autocorrelation in stock returns," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 162-173.
    32. Kroencke, Tim A., 2022. "Recessions and the stock market," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 61-77.

  10. Henri Nyberg & Markku Lanne & Erkka Saarinen, 2012. "Does noncausality help in forecasting economic time series?," Economics Bulletin, AccessEcon, vol. 32(4), pages 2849-2859.

    Cited by:

    1. Giurcanu, Mihai C., 2015. "A simulation algorithm for non-causal VARMA processes," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 65-72.
    2. Hecq, A.W. & Lieb, L.M. & Telg, J.M.A., 2015. "Identification of Mixed Causal-Noncausal Models : How Fat Should We Go?," Research Memorandum 035, Maastricht University, Graduate School of Business and Economics (GSBE).
    3. Fries, Sébastien, 2018. "Conditional moments of noncausal alpha-stable processes and the prediction of bubble crash odds," MPRA Paper 97353, University Library of Munich, Germany, revised Nov 2019.
    4. Christian Gourieroux & Joann Jasiak & Michelle Tong, 2021. "Convolution‐based filtering and forecasting: An application to WTI crude oil prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1230-1244, November.
    5. Hecq, Alain & Voisin, Elisa, 2021. "Forecasting bubbles with mixed causal-noncausal autoregressive models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 29-45.
    6. Fries, Sébastien & Zakoian, Jean-Michel, 2017. "Mixed Causal-Noncausal AR Processes and the Modelling of Explosive Bubbles," MPRA Paper 81345, University Library of Munich, Germany.
    7. Frédérique Bec & Heino Bohn Nielsen & Sarra Saïdi, 2020. "Mixed Causal–Noncausal Autoregressions: Bimodality Issues in Estimation and Unit Root Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1413-1428, December.
    8. Christian Gouriéroux & Jean-Michel Zakoïan, 2017. "Local explosion modelling by non-causal process," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 737-756, June.
    9. Jean-Baptiste MICHAU, 2019. "Helicopter Drops of Money under Secular Stagnation," Working Papers 2019-10, Center for Research in Economics and Statistics.
    10. Hecq, Alain & Issler, João Victor & Telg, Sean, 2017. "Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors," MPRA Paper 80767, University Library of Munich, Germany.
    11. Nyberg, Henri & Saikkonen, Pentti, 2012. "Forecasting with a noncausal VAR model," Bank of Finland Research Discussion Papers 33/2012, Bank of Finland.
    12. Lof, Matthijs & Nyberg, Henri, 2017. "Noncausality and the commodity currency hypothesis," Energy Economics, Elsevier, vol. 65(C), pages 424-433.
    13. Alain Hecq & Sean Telg & Lenard Lieb, 2017. "Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?," Econometrics, MDPI, vol. 5(4), pages 1-22, October.
    14. Nyholm, Juho, 2017. "Residual-based diagnostic tests for noninvertible ARMA models," MPRA Paper 81033, University Library of Munich, Germany.

  11. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578, April.

    Cited by:

    1. Hashmat Khan & Santosh Upadhayaya, 2017. "Does Business Confidence Matter for Investment?," Carleton Economic Papers 17-13, Carleton University, Department of Economics, revised 20 Mar 2019.
    2. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, vol. 61(C), pages 72-86.
    3. Huei-Wen Teng & Yu-Hsien Li, 2023. "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, vol. 5(1), pages 149-182, March.
    4. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    5. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    6. Pönkä, Harri, 2014. "Predicting the direction of US stock markets using industry returns," MPRA Paper 62942, University Library of Munich, Germany.
    7. Algieri, Bernardina & Leccadito, Arturo, 2019. "Ask CARL: Forecasting tail probabilities for energy commodities," Energy Economics, Elsevier, vol. 84(C).
    8. Zhang, Xinyu & Lu, Zudi & Zou, Guohua, 2013. "Adaptively combined forecasting for discrete response time series," Journal of Econometrics, Elsevier, vol. 176(1), pages 80-91.
    9. Khurshid Kiani, 2011. "Fluctuations in Economic and Activity and Stabilization Policies in the CIS," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 193-220, February.
    10. Garcia, M.M. & Machado Pereira, A.C. & Acebal, J.L. & Bosco de Magalhães, A.R., 2020. "Forecast model for financial time series: An approach based on harmonic oscillators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    11. Jiti Gao & Fei Liu & Bin Peng & Yayi Yan, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Papers 2012.03182, arXiv.org, revised Nov 2021.
    12. Hadhri, Sinda & Ftiti, Zied, 2017. "Stock return predictability in emerging markets: Does the choice of predictors and models matter across countries?," Research in International Business and Finance, Elsevier, vol. 42(C), pages 39-60.
    13. Papailias, Fotis & Liu, Jiadong & Thomakos, Dimitrios D., 2019. "Return Signal Momentum," QBS Working Paper Series 2019/04, Queen's University Belfast, Queen's Business School.
    14. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    15. Afees A. Salisu & Raymond Swaray & Tirimisyu F. Oloko, 2017. "A multi-factor predictive model for oil-US stock nexus with persistence, endogeneity and conditional heteroscedasticity effects," Working Papers 024, Centre for Econometric and Allied Research, University of Ibadan.
    16. Gu, Wentao & Peng, Yiqing, 2019. "Forecasting the market return direction based on a time-varying probability density model," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    17. Chaohua Dong & Jiti Gao & Bin Peng & Yayi Yan, 2023. "Estimation and Inference for a Class of Generalized Hierarchical Models," Papers 2311.02789, arXiv.org, revised Apr 2024.
    18. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    19. James W. Taylor & Keming Yu, 2016. "Using auto-regressive logit models to forecast the exceedance probability for financial risk management," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1069-1092, October.
    20. Sadorsky, Perry, 2022. "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    21. Nyberg, Henri, 2010. "QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles," MPRA Paper 23724, University Library of Munich, Germany.
    22. Bertrand Candelon & Jameel Ahmed & Stefan Straetmans, 2014. "Predicting and Capitalizing on Stock Market Bears in the U.S," Working Papers 2014-409, Department of Research, Ipag Business School.
    23. Fokianos, Konstantinos & Moysiadis, Theodoros, 2017. "Binary time series models driven by a latent process," Econometrics and Statistics, Elsevier, vol. 2(C), pages 117-130.
    24. Yang Lu, 2020. "A simple parameter‐driven binary time series model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 187-199, March.
    25. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.
    26. Liu, Jiadong & Papailias, Fotis & Quinn, Barry, 2021. "Direction-of-change forecasting in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 74(C).
    27. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    28. Haibin Xie & Yuying Sun & Pengying Fan, 2023. "Return direction forecasting: a conditional autoregressive shape model with beta density," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
    29. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    30. Harri Pönkä, 2018. "Sentiment and sign predictability of stock returns," Economics Bulletin, AccessEcon, vol. 38(3), pages 1676-1684.
    31. Thomas Bury, 2013. "Predicting trend reversals using market instantaneous state," Papers 1310.8169, arXiv.org, revised Mar 2014.
    32. Pawel Dlotko & Wanling Qiu & Simon Rudkin, 2022. "Topological Data Analysis Ball Mapper for Finance," Papers 2206.03622, arXiv.org.
    33. Jiti Gao & Fei Liu & Bin peng, 2020. "Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 44/20, Monash University, Department of Econometrics and Business Statistics.
    34. Pönkä, Harri, 2015. "Real oil prices and the international sign predictability of stock returns," MPRA Paper 68330, University Library of Munich, Germany.
    35. Ginker, Tim & Lieberman, Offer, 2017. "Robustness of binary choice models to conditional heteroscedasticity," Economics Letters, Elsevier, vol. 150(C), pages 130-134.
    36. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    37. de Resende, Charlene C. & Pereira, Adriano C.M. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2017. "Investigating market efficiency through a forecasting model based on differential equations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 199-212.
    38. Syed Abul, Basher & Perry, Sadorsky, 2022. "Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?," MPRA Paper 113293, University Library of Munich, Germany.
    39. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
    40. Chen, Nan-Kuang & Chen, Shiu-Sheng & Chou, Yu-Hsi, 2013. "Further evidence on bear market predictability: The role of the external finance premium," MPRA Paper 49093, University Library of Munich, Germany.
    41. Stanislav Anatolyev & Jozef Barunik, 2017. "Forecasting dynamic return distributions based on ordered binary choice," Papers 1711.05681, arXiv.org, revised Jan 2019.
    42. Rafik Nazarian & Ashkan Amiri, 2014. "Asymmetry of the Oil Price Pass Through to Inflation in Iran," International Journal of Energy Economics and Policy, Econjournals, vol. 4(3), pages 457-464.
    43. Luis H. R. Alvarez E. & Paavo Salminen, 2017. "Timing in the presence of directional predictability: optimal stopping of skew Brownian motion," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 86(2), pages 377-400, October.
    44. Rongning Wu & Yunwei Cui, 2014. "A Parameter-Driven Logit Regression Model For Binary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(5), pages 462-477, August.
    45. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    46. Erol Eğrioğlu & Robert Fildes, 2022. "A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1355-1383, April.
    47. Chevapatrakul, Thanaset, 2013. "Return sign forecasts based on conditional risk: Evidence from the UK stock market index," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2342-2353.
    48. Perry Sadorsky, 2021. "A Random Forests Approach to Predicting Clean Energy Stock Prices," JRFM, MDPI, vol. 14(2), pages 1-20, January.
    49. Fokianos, Konstantinos & Truquet, Lionel, 2019. "On categorical time series models with covariates," Stochastic Processes and their Applications, Elsevier, vol. 129(9), pages 3446-3462.
    50. Bury, Thomas, 2014. "Predicting trend reversals using market instantaneous state," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 79-91.

  12. Henri Nyberg, 2010. "Dynamic probit models and financial variables in recession forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 215-230.

    Cited by:

    1. Makram El-Shagi & Gregor von Schweinitz, 2016. "Qual VAR revisited: Good forecast, bad story," Journal of Applied Economics, Universidad del CEMA, vol. 19, pages 293-322, November.
    2. Hashmat Khan & Santosh Upadhayaya, 2017. "Does Business Confidence Matter for Investment?," Carleton Economic Papers 17-13, Carleton University, Department of Economics, revised 20 Mar 2019.
    3. Boss, Alfred & Dovern, Jonas & Meier, Carsten-Patrick & Scheide, Joachim, 2008. "Deutsche Konjunktur: leichte Rezession absehbar," Open Access Publications from Kiel Institute for the World Economy 28638, Kiel Institute for the World Economy (IfW Kiel).
    4. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
    5. Jean-Baptiste Hasse & Quentin Lajaunie, 2020. "Does the Yield Curve Signal Recessions? New Evidence from an International Panel Data Analysis," AMSE Working Papers 2013, Aix-Marseille School of Economics, France.
    6. Bofinger, Peter & Feld, Lars P. & Schmidt, Christoph M. & Schnabel, Isabel & Wieland, Volker, 2018. "Vor wichtigen wirtschaftspolitischen Weichenstellungen. Jahresgutachten 2018/19 [Setting the Right Course for Economic Policy. Annual Report 2018/19]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201819.
    7. Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
    8. António R. Antunes & Diana Bonfim & Nuno Monteiro & Paulo M.M. Rodrigues, 2016. "Forecasting banking crises with dynamic panel probit models," Working Papers w201613, Banco de Portugal, Economics and Research Department.
    9. Fernando Garcia Alvarado, 2022. "Detecting crisis vulnerability using yield spread interconnectedness," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3864-3880, October.
    10. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
    11. Raffaele Mattera, 2023. "Forecasting binary outcomes in soccer," Annals of Operations Research, Springer, vol. 325(1), pages 115-134, June.
    12. Weiling Liu & Emanuel Moench, 2014. "What predicts U.S. recessions?," Staff Reports 691, Federal Reserve Bank of New York.
    13. Peymankar, Mahboobeh & Davari, Morteza & Ranjbar, Mohammad, 2021. "Maximizing the expected net present value in a project with uncertain cash flows," European Journal of Operational Research, Elsevier, vol. 294(2), pages 442-452.
    14. Rebecca Stuart, 2020. "Monetary regimes, the term structure and business cycles in Ireland, 1972–2018," Manchester School, University of Manchester, vol. 88(5), pages 731-748, September.
    15. Theophilos Papadimitriou & Periklis Gogas & Maria Matthaiou & Efthymia Chrysanthidou, 2014. "Yield curve and Recession Forecasting in a Machine Learning Framework," Working Paper series 32_14, Rimini Centre for Economic Analysis.
    16. Dovern, Jonas & Gern, Klaus-Jürgen & Jannsen, Nils & Van Roye, Björn & Scheide, Joachim & Hogrefe, Jens & Boss, Alfred & Meier, Carsten-Patrick, 2008. "Weltkonjunktur und deutsche Konjunktur im Herbst 2008," Kiel Discussion Papers 456/457, Kiel Institute for the World Economy (IfW Kiel).
    17. Kuosmanen, Petri & Vataja, Juuso, 2014. "Forecasting GDP growth with financial market data in Finland: Revisiting stylized facts in a small open economy during the financial crisis," Review of Financial Economics, Elsevier, vol. 23(2), pages 90-97.
    18. Christiansen, Charlotte, 2013. "Predicting severe simultaneous recessions using yield spreads as leading indicators," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 1032-1043.
    19. Dongfeng Chang & Ryan S. Mattson & Biyan Tang, 2019. "The Predictive Power of the User Cost Spread for Economic Recession in China and the US," IJFS, MDPI, vol. 7(2), pages 1-12, June.
    20. Pönkä, Harri, 2014. "Predicting the direction of US stock markets using industry returns," MPRA Paper 62942, University Library of Munich, Germany.
    21. Ng, Eric C.Y., 2012. "Forecasting US recessions with various risk factors and dynamic probit models," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 112-125.
    22. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    23. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578.
    24. Pönkä, Harri & Stenborg, Markku, 2018. "Forecasting the state of the Finnish business cycle," MPRA Paper 91226, University Library of Munich, Germany.
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