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Seth Pruitt

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Stefano Giglio & Bryan T. Kelly & Seth Pruitt, 2015. "Systemic Risk and the Macroeconomy: An Empirical Evaluation," NBER Working Papers 20963, National Bureau of Economic Research, Inc.

    Mentioned in:

    1. The mythic quest for early warnings
      by Steve Cecchetti and Kim Schoenholtz in Money, Banking and Financial Markets on 2015-04-13 17:40:01

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Nir Jaimovich & Seth Pruitt & Henry E. Siu, 2013. "The Demand for Youth: Explaining Age Differences in the Volatility of Hours," American Economic Review, American Economic Association, vol. 103(7), pages 3022-3044, December.

    Mentioned in:

    1. The Demand for Youth: Explaining Age Differences in the Volatility of Hours (AER 2013) in ReplicationWiki ()
  2. James D. Hamilton & Seth Pruitt & Scott Borger, 2011. "Estimating the Market-Perceived Monetary Policy Rule," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(3), pages 1-28, July.

    Mentioned in:

    1. Estimating the Market-Perceived Monetary Policy Rule (AEJ:MA 2011) in ReplicationWiki ()

Working papers

  1. Bryan Kelly & Seth Pruitt & Yinan Su, 2018. "Characteristics Are Covariances: A Unified Model of Risk and Return," NBER Working Papers 24540, National Bureau of Economic Research, Inc.

    Cited by:

    1. Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020. "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers 14266, C.E.P.R. Discussion Papers.
    2. Bandi, Federico M. & Chaudhuri, Shomesh E. & Lo, Andrew W. & Tamoni, Andrea, 2021. "Spectral factor models," Journal of Financial Economics, Elsevier, vol. 142(1), pages 214-238.
    3. Alex Chinco & Samuel M. Hartzmark & Abigail B. Sussman, 2022. "A New Test of Risk Factor Relevance," Journal of Finance, American Finance Association, vol. 77(4), pages 2183-2238, August.
    4. Gonçalves, Andrei S. & Leonard, Gregory, 2023. "The fundamental-to-market ratio and the value premium decline," Journal of Financial Economics, Elsevier, vol. 147(2), pages 382-405.
    5. Alexander Arimond & Damian Borth & Andreas Hoepner & Michael Klawunn & Stefan Weisheit, 2020. "Neural Networks and Value at Risk," Papers 2005.01686, arXiv.org, revised May 2020.
    6. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    7. Jorge Guijarro-Ordonez & Markus Pelger & Greg Zanotti, 2021. "Deep Learning Statistical Arbitrage," Papers 2106.04028, arXiv.org, revised Oct 2022.
    8. Christian Fieberg & Lars Hornuf & Gerrit Liedtke & Thorsten Poddig, 2020. "Are Characteristics Covariances? A Comment on Instrumented Principal Component Analysis," CESifo Working Paper Series 8377, CESifo.
    9. Elizaveta V. Anufrieva, 2019. "Influence of Macroeconomic Factors on the Return of Russian Stock Exchange Indices," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 75-87, August.
    10. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
    11. Nicolae Gârleanu & Lasse Heje Pedersen, 2022. "Active and Passive Investing: Understanding Samuelson’s Dictum [A noisy rational expectations equilibrium for multi-asset securities markets]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 12(2), pages 389-446.
    12. Gagliardini, Patrick & Ossola, Elisa & Scaillet, Olivier, 2019. "Estimation of large dimensional conditional factor models in finance," Working Papers unige:125031, University of Geneva, Geneva School of Economics and Management.
    13. Chinco, Alex & Neuhierl, Andreas & Weber, Michael, 2021. "Estimating the anomaly base rate," Journal of Financial Economics, Elsevier, vol. 140(1), pages 101-126.
    14. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    15. Bruno Spilak & Wolfgang Karl Hardle, 2022. "Risk budget portfolios with convex Non-negative Matrix Factorization," Papers 2204.02757, arXiv.org, revised Jun 2023.
    16. Andersen, Torben G. & Riva, Raul & Thyrsgaard, Martin & Todorov, Viktor, 2023. "Intraday cross-sectional distributions of systematic risk," Journal of Econometrics, Elsevier, vol. 235(2), pages 1394-1418.
    17. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    18. Son, Bumho & Lee, Jaewook, 2022. "Graph-based multi-factor asset pricing model," Finance Research Letters, Elsevier, vol. 44(C).
    19. Chen, Zhenhua & Liu, Zhenya & Teka, Hanen & Zhang, Yifan, 2022. "Smart money in China's A-share market: Evidence from big data," Research in International Business and Finance, Elsevier, vol. 61(C).
    20. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    21. Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "A diagnostic criterion for approximate factor structure," Papers 1612.04990, arXiv.org, revised Aug 2017.
    22. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    23. Zihang Peng, 2023. "Do risk exposures explain accounting anomalies? A new testing method," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 2965-2983, September.
    24. John B. Guerard, 2024. "Sir David Hendry: An Appreciation from Wall Street and What Macroeconomics Got Right," Working Papers 2024-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Feb 2024.
    25. Langlois, Hugues, 2023. "What matters in a characteristic?," Journal of Financial Economics, Elsevier, vol. 149(1), pages 52-72.
    26. Shirui Wang & Tianyang Zhang, 2024. "Predictability of commodity futures returns with machine learning models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(2), pages 302-322, February.
    27. Bartram, Söhnke & Djuranovik, Leslie & Garratt, Anthony, 2021. "Currency Anomalies," CEPR Discussion Papers 15653, C.E.P.R. Discussion Papers.
    28. Weichuan Deng & Pawel Polak & Abolfazl Safikhani & Ronakdilip Shah, 2023. "A Unified Framework for Fast Large-Scale Portfolio Optimization," Papers 2303.12751, arXiv.org, revised Nov 2023.
    29. Cederburg, Scott & O’Doherty, Michael S. & Wang, Feifei & Yan, Xuemin (Sterling), 2020. "On the performance of volatility-managed portfolios," Journal of Financial Economics, Elsevier, vol. 138(1), pages 95-117.
    30. Ni, Xuanming & Zheng, Tiantian & Zhao, Huimin & Zhu, Shushang, 2023. "High-dimensional portfolio optimization based on tree-structured factor model," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
    31. Uddin, Ajim & Yu, Dantong, 2020. "Latent factor model for asset pricing," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    32. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    33. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
    34. Guillaume Coqueret & Tony Guida, 2020. "Training trees on tails with applications to portfolio choice," Annals of Operations Research, Springer, vol. 288(1), pages 181-221, May.
    35. Hanauer, Matthias X. & Kalsbach, Tobias, 2023. "Machine learning and the cross-section of emerging market stock returns," Emerging Markets Review, Elsevier, vol. 55(C).
    36. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    37. Michael Hasler & Charles Martineau, 2023. "Explaining the Failure of the Unconditional CAPM with the Conditional CAPM," Management Science, INFORMS, vol. 69(3), pages 1835-1855, March.
    38. Tobek, Ondrej & Hronec, Martin, 2021. "Does it pay to follow anomalies research? Machine learning approach with international evidence," Journal of Financial Markets, Elsevier, vol. 56(C).
    39. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    40. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
    41. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
    42. Konstantin Gorgen & Abdolreza Nazemi & Melanie Schienle, 2022. "Robust Knockoffs for Controlling False Discoveries With an Application to Bond Recovery Rates," Papers 2206.06026, arXiv.org.
    43. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    44. Ge, S. & Li, S. & Linton, O., 2020. "A Dynamic Network of Arbitrage Characteristics," Cambridge Working Papers in Economics 2060, Faculty of Economics, University of Cambridge.
    45. Chaieb, Ines & Langlois, Hugues & Scaillet, Olivier, 2021. "Factors and risk premia in individual international stock returns," Journal of Financial Economics, Elsevier, vol. 141(2), pages 669-692.
    46. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
    47. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    48. Patton, Andrew J. & Weller, Brian M., 2020. "What you see is not what you get: The costs of trading market anomalies," Journal of Financial Economics, Elsevier, vol. 137(2), pages 515-549.
    49. Daniele Bianchi & Mykola Babiak, 2021. "A Factor Model for Cryptocurrency Returns," CERGE-EI Working Papers wp710, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    50. Ma, Tian & Leong, Wen Jun & Jiang, Fuwei, 2023. "A latent factor model for the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 87(C).
    51. B. Li & S. Boubaker & Z. Liu & W. Louhichi & Y. Yao, 2023. "Exploring the Nonlinear Idiosyncratic Volatility Puzzle: Evidence from China," Post-Print hal-04435519, HAL.
    52. Szybisz, Martin Andres, 2019. "Interactions between Credit and Market Risk, Diversification vs Compounding effects," MPRA Paper 93173, University Library of Munich, Germany.
    53. Luyang Chen & Markus Pelger & Jason Zhu, 2019. "Deep Learning in Asset Pricing," Papers 1904.00745, arXiv.org, revised Aug 2021.
    54. Ji Cao & Marc Oliver Rieger & Lei Zhao, 2019. "Safety First, Loss Probability, and the Cross Section of Expected Stock Returns," Working Paper Series 2019-02, University of Trier, Research Group Quantitative Finance and Risk Analysis.
    55. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    56. Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
    57. Ruoxuan Xiong & Markus Pelger, 2019. "Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference," Papers 1910.08273, arXiv.org, revised Jan 2022.
    58. Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factors’ CPDAG Roots and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_18, University of São Paulo (FEA-USP).
    59. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
    60. Yang, Huan & Cai, Jun & Huang, Lin & Marcus, Alan J., 2021. "Bank stocks, risk factors, and tail behavior," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 203-229.
    61. Jozef Barunik & Matej Nevrla, 2022. "Common Idiosyncratic Quantile Risk," Papers 2208.14267, arXiv.org, revised Jun 2023.
    62. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    63. Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023. "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, vol. 150(1), pages 94-138.
    64. Kelly, Bryan T. & Moskowitz, Tobias J. & Pruitt, Seth, 2021. "Understanding momentum and reversal," Journal of Financial Economics, Elsevier, vol. 140(3), pages 726-743.
    65. Clarke, Charles, 2022. "The level, slope, and curve factor model for stocks," Journal of Financial Economics, Elsevier, vol. 143(1), pages 159-187.
    66. Valentin Haddad & Tyler Muir, 2021. "Do Intermediaries Matter for Aggregate Asset Prices?," Journal of Finance, American Finance Association, vol. 76(6), pages 2719-2761, December.
    67. Yan, Jingda & Yu, Jialin, 2023. "Cross-stock momentum and factor momentum," Journal of Financial Economics, Elsevier, vol. 150(2).
    68. Amit Goyal & Alessio Saretto, 2022. "Are Equity Option Returns Abnormal? IPCA Says No," Working Papers 2214, Federal Reserve Bank of Dallas.
    69. Fabian Krause & Jan-Peter Calliess, 2024. "End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture," Papers 2402.08233, arXiv.org.
    70. Harvey, Campbell R. & Liu, Yan, 2021. "Lucky factors," Journal of Financial Economics, Elsevier, vol. 141(2), pages 413-435.
    71. Vu Le Tran & Guillaume Coqueret, 2023. "ESG news spillovers across the value chain," Financial Management, Financial Management Association International, vol. 52(4), pages 677-710, December.
    72. 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.
    73. Oleg Rytchkov & Xun Zhong, 2020. "Information Aggregation and P-Hacking," Management Science, INFORMS, vol. 66(4), pages 1605-1626, April.
    74. Krisna Nugraha & Muhtosim Arief & Sri Bramantoro Abdinagoro & Pantri Heriyati, 2022. "Factors Influencing Bank Customers’ Orientations toward Islamic Banks: Indonesian Banking Perspective," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    75. Söhnke M. Bartram & Harald Lohre & Peter F. Pope & Ananthalakshmi Ranganathan, 2021. "Navigating the factor zoo around the world: an institutional investor perspective," Journal of Business Economics, Springer, vol. 91(5), pages 655-703, July.
    76. Beckmeyer, Heiner & Wiedemann, Timo, 2022. "Recovering Missing Firm Characteristics with Attention-Based Machine Learning," VfS Annual Conference 2022 (Basel): Big Data in Economics 264135, Verein für Socialpolitik / German Economic Association.
    77. Vu Le Tran & Guillaume Coqueret, 2023. "ESG news spillovers across the value chain," Post-Print hal-04325746, HAL.
    78. Tran, Vu Le, 2023. "Sentiment and covariance characteristics," International Review of Financial Analysis, Elsevier, vol. 86(C).
    79. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.
    80. Dapeng Li & Feiyang Pan & Jia He & Zhiwei Xu & Dandan Tu & Guoliang Fan, 2023. "Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning," Papers 2303.11716, arXiv.org.
    81. Damir Filipovi'c & Puneet Pasricha, 2022. "Empirical Asset Pricing via Ensemble Gaussian Process Regression," Papers 2212.01048, arXiv.org.
    82. Feng, Guanhao & He, Jingyu, 2022. "Factor investing: A Bayesian hierarchical approach," Journal of Econometrics, Elsevier, vol. 230(1), pages 183-200.
    83. Atif Ellahie, 2021. "Earnings beta," Review of Accounting Studies, Springer, vol. 26(1), pages 81-122, March.
    84. Cao, Ji & Rieger, Marc Oliver & Zhao, Lei, 2023. "Safety first, loss probability, and the cross section of expected stock returns," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 345-369.
    85. van Binsbergen, Jules H. & Boons, Martijn & Opp, Christian C. & Tamoni, Andrea, 2023. "Dynamic asset (mis)pricing: Build-up versus resolution anomalies," Journal of Financial Economics, Elsevier, vol. 147(2), pages 406-431.
    86. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org.
    87. Theissen, Erik & Yilanci, Can, 2020. "Momentum? What Momentum?," CFR Working Papers 20-09, University of Cologne, Centre for Financial Research (CFR).
    88. Lioui, Abraham & Tarelli, Andrea, 2020. "Factor Investing for the Long Run," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    89. Valentin Haddad & Serhiy Kozak & Shrihari Santosh & Stijn Van Nieuwerburgh, 2020. "Factor Timing," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1980-2018.
    90. Dashan Huang & Fuwei Jiang & Kunpeng Li & Guoshi Tong & Guofu Zhou, 2022. "Scaled PCA: A New Approach to Dimension Reduction," Management Science, INFORMS, vol. 68(3), pages 1678-1695, March.
    91. Matthias Buechner & Bryan T. Kelly, 2021. "A Factor Model For Option Returns," NBER Working Papers 29369, National Bureau of Economic Research, Inc.
    92. Andrew Y. Chen, 2021. "The Limits of p‐Hacking: Some Thought Experiments," Journal of Finance, American Finance Association, vol. 76(5), pages 2447-2480, October.
    93. Wolfgang Drobetz & Rebekka Haller & Christian Jasperneite & Tizian Otto, 2019. "Predictability and the cross section of expected returns: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 20(7), pages 508-533, December.
    94. Bartram, Söhnke M. & Grinblatt, Mark, 2021. "Global market inefficiencies," Journal of Financial Economics, Elsevier, vol. 139(1), pages 234-259.
    95. Matias D. Cattaneo & Richard K. Crump & Weining Wang, 2023. "Beta-Sorted Portfolios," Staff Reports 1068, Federal Reserve Bank of New York.
    96. Caio Vigo Pereira, 2020. "Portfolio Efficiency with High-Dimensional Data as Conditioning Information," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202015, University of Kansas, Department of Economics, revised Sep 2020.
    97. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    98. Wang, Feifei & Yan, Xuemin Sterling, 2021. "Downside risk and the performance of volatility-managed portfolios," Journal of Banking & Finance, Elsevier, vol. 131(C).
    99. Alex R. Horenstein, 2021. "The Unintended Impact of Academic Research on Asset Returns: The Capital Asset Pricing Model Alpha," Management Science, INFORMS, vol. 67(6), pages 3655-3673, June.
    100. Guillaume Coqueret & Tony Guida, 2020. "Training trees on tails with applications to portfolio choice," Post-Print hal-04144665, HAL.
    101. Iason Kynigakis & Ekaterini Panopoulou, 2022. "Does model complexity add value to asset allocation? Evidence from machine learning forecasting models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 603-639, April.
    102. Grammig, Joachim & Hanenberg, Constantin & Schlag, Christian & Sönksen, Jantje, 2020. "Diverging roads: Theory-based vs. machine learning-implied stock risk premia," University of Tübingen Working Papers in Business and Economics 130, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    103. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
    104. Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2023. "Bloated Disclosures: Can ChatGPT Help Investors Process Information?," Papers 2306.10224, arXiv.org, revised Feb 2024.
    105. Hai Lin & Pengfei Liu & Cheng Zhang, 2023. "The trend premium around the world: Evidence from the stock market," International Review of Finance, International Review of Finance Ltd., vol. 23(2), pages 317-358, June.
    106. Ai He & Guofu Zhou, 2023. "Diagnostics for asset pricing models," Financial Management, Financial Management Association International, vol. 52(4), pages 617-642, December.
    107. Fernando Moraes & Rodrigo De-Losso, 2020. "Risk Factor Centrality and the Cross-Section of Expected Returns," Working Papers, Department of Economics 2020_17, University of São Paulo (FEA-USP).
    108. Loïc Maréchal, 2023. "A tale of two premiums revisited," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(5), pages 580-614, May.
    109. Wolfgang Drobetz & Tizian Otto, 2021. "Empirical asset pricing via machine learning: evidence from the European stock market," Journal of Asset Management, Palgrave Macmillan, vol. 22(7), pages 507-538, December.
    110. De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
    111. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    112. Büchner, Matthias & Kelly, Bryan, 2022. "A factor model for option returns," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1140-1161.
    113. Akbari, Amir & Ng, Lilian & Solnik, Bruno, 2021. "Drivers of economic and financial integration: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 61(C), pages 82-102.
    114. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    115. Lioui, Abraham & Tarelli, Andrea, 2022. "Chasing the ESG factor," Journal of Banking & Finance, Elsevier, vol. 139(C).
    116. Guanhao Feng & Jingyu He, 2019. "Factor Investing: A Bayesian Hierarchical Approach," Papers 1902.01015, arXiv.org, revised Sep 2020.

  2. Seth Pruitt & Nicholas Turner, 2018. "The Nature of Household Labor Income Risk," Finance and Economics Discussion Series 2018-034, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. , 2020. "Family and Government Insurance: Wage, Earnings, and Income Risks in the Netherlands and the U.S," Opportunity and Inclusive Growth Institute Working Papers 42, Federal Reserve Bank of Minneapolis.
    2. Christopher Busch & David Domeij & Fatih Guvenen & Rocio Madera, 2020. "Skewed Idiosyncratic Income Risk over the Business Cycle: Sources and Insurance," Working Papers 1180, Barcelona School of Economics.
    3. Storesletten, Kjetil & Halvorsen, Elin & Holter, Hans & Ozkan, Serdar, 2020. "Dissecting Idiosyncratic Earnings Risk," CEPR Discussion Papers 15395, C.E.P.R. Discussion Papers.

  3. Michiel De Pooter & Robert F. Martin & Seth Pruitt, 2015. "The Liquidity Effects of Official Bond Market Intervention," International Finance Discussion Papers 1138, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Trebesch, Christoph & Zettelmeyer, Jeromin, 2018. "ECB interventions in distressed sovereign debt markets: The case of Greek bonds," Kiel Working Papers 2101, Kiel Institute for the World Economy (IfW Kiel).
    2. Marco Casiraghi & Eugenio Gaiotti & Lisa Rodano & Alessandro Secchi, 2013. "The impact of unconventional monetary policy on the Italian economy during the sovereign debt crisis," Questioni di Economia e Finanza (Occasional Papers) 203, Bank of Italy, Economic Research and International Relations Area.
    3. Christophe Blot & Caroline Bozou & Jérôme Creel & Paul Hubert, 2021. "Are all Central Bank Asset Purchases the Same? Different Rationales, Different Effects," SciencePo Working papers Main hal-03554141, HAL.
    4. Blix Grimaldi, Marianna & Crosta, Alberto & Zhang, Dong, 2021. "The Liquidity of the Government Bond Market – What Impact Does Quantitative Easing Have? Evidence from Sweden," Working Paper Series 402, Sveriges Riksbank (Central Bank of Sweden).
    5. Gómez-Puig, Marta & Pieterse-Bloem, Mary & Sosvilla-Rivero, Simón, 2023. "Dynamic connectedness between credit and liquidity risks in euro area sovereign debt markets," Journal of Multinational Financial Management, Elsevier, vol. 68(C).
    6. Weigerding, Michael, 2023. "Long-term liquidity effects of large-scale asset purchase programs: Evidence from the euro covered bond market," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 244-264.
    7. Caballero, Diego & Lucas, André & Schwaab, Bernd & Zhang, Xin, 2019. "Risk endogeneity at the lender/investor-of-last-resort," Working Paper Series 2225, European Central Bank.
    8. Hartmann, Philipp & Smets, Frank, 2018. "The first twenty years of the European Central Bank: monetary policy," CEPR Discussion Papers 13411, C.E.P.R. Discussion Papers.
    9. Albertazzi, Ugo & Barbiero, Francesca & Marqués-Ibáñez, David & Popov, Alexander & Rodriguez d’Acri, Costanza & Vlassopoulos, Thomas, 2020. "Monetary policy and bank stability: the analytical toolbox reviewed," Working Paper Series 2377, European Central Bank.
    10. Havlik, Annika & Heinemann, Friedrich & Helbig, Samuel & Nover, Justus, 2021. "Dispelling the shadow of fiscal dominance? Fiscal and monetary announcement effects for euro area sovereign spreads in the corona pandemic," ZEW Discussion Papers 21-050, ZEW - Leibniz Centre for European Economic Research.
    11. Arvind Krishnamurthy & Stefan Nagel & Annette Vissing-Jorgensen, 2017. "ECB Policies Involving Government Bond Purchases: Impact and Channels," NBER Working Papers 23985, National Bureau of Economic Research, Inc.
    12. Carnazza, Giovanni & Liberati, Paolo, 2021. "The asymmetric impact of the pandemic crisis on interest rates on public debt in the Eurozone," Journal of Policy Modeling, Elsevier, vol. 43(3), pages 521-542.
    13. John Muellbauer, 2013. "Conditional Eurobonds and the Eurozone Sovereign Debt Crisis," Economics Series Working Papers 681, University of Oxford, Department of Economics.
    14. Hülsewig, Oliver & Rottmann, Horst, 2021. "Euro area periphery countries' fiscal policy and monetary policy surprises," Weidener Diskussionspapiere 81, University of Applied Sciences Amberg-Weiden (OTH).
    15. Urszula Szczerbowicz, 2012. "The ECB Unconventional Monetary Policies: Have They Lowered Market Borrowing Costs for Banks and Governments?," Working Papers 2012-36, CEPII research center.
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    Cited by:

    1. Markmann, Holger & Zietz, Joachim, 2017. "Determining the effectiveness of the Eurosystem’s Covered Bond Purchase Programs on secondary markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 314-327.

  5. Stefano Giglio & Bryan T. Kelly & Seth Pruitt, 2015. "Systemic Risk and the Macroeconomy: An Empirical Evaluation," NBER Working Papers 20963, National Bureau of Economic Research, Inc.

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    228. Yin, Libo & Feng, Jiabao & Han, Liyan, 2021. "Systemic risk in international stock markets: Role of the oil market," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 592-619.
    229. Silva, Walmir & Kimura, Herbert & Sobreiro, Vinicius Amorim, 2017. "An analysis of the literature on systemic financial risk: A survey," Journal of Financial Stability, Elsevier, vol. 28(C), pages 91-114.
    230. Cosmin Octavian Cepoi & Victor Dragotă & Ruxandra Trifan & Andreea Iordache, 2023. "Probability of informed trading during the COVID-19 pandemic: the case of the Romanian stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    231. Cabral, Inês & Detken, Carsten & Fell, John & Henry, Jérôme & Hiebert, Paul & Kapadia, Sujit & Pires, Fatima & Salleo, Carmelo & Constâncio, Vítor & Nicoletti Altimari, Sergio, 2019. "Macroprudential policy at the ECB: Institutional framework, strategy, analytical tools and policies," Occasional Paper Series 227, European Central Bank.

  6. Jinill Kim & Seth Pruitt, 2013. "Estimating Monetary Policy Rules When Nominal Interest Rates Are Stuck at Zero," CAMA Working Papers 2013-53, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Arai, Natsuki, 2023. "The FOMC’s new individual economic projections and macroeconomic theories," Journal of Banking & Finance, Elsevier, vol. 151(C).
    2. Michael D. Bauer & Carolin Pflueger & Adi Sunderam, 2023. "Perceptions about Monetary Policy," Working Paper Series 2023-31, Federal Reserve Bank of San Francisco.
    3. Yoshiyuki Nakazono, 2016. "Inflation expectations and monetary policy under disagreements," Bank of Japan Working Paper Series 16-E-1, Bank of Japan.
    4. Brent Bundick, 2015. "Estimating the monetary policy rule perceived by forecasters," Macro Bulletin, Federal Reserve Bank of Kansas City, pages 1-3, December.
    5. Hirokuni Iiboshi & Mototsugu Shintani & Kozo Ueda, 2018. "Estimating a Nonlinear New Keynesian Model with a Zero Lower Bound for Japan," Working Papers e120, Tokyo Center for Economic Research.
    6. Jinill Kim & Seth Pruitt, 2017. "Estimating Monetary Policy Rules When Nominal Interest Rates Are Stuck at Zero," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(4), pages 585-602, June.
    7. Kozo Ueda & Yoshiyuki Nakazono & Ippei Fujiwara, 2014. "Policy Regime Change against Chronic Deflation? Policy option under long-term liquidity trap," AJRC Working Papers 1402, Australia-Japan Research Centre, Crawford School of Public Policy, The Australian National University.
    8. Ippei Fujiwara & Yoshiyuki Nakazono & Kozo Ueda, 2015. "Policy Regime Change Against Chronic Deflation?," Working Papers halshs-01545830, HAL.
    9. Hibiki Ichiue & Yoichi Ueno, 2018. "A Survey-based Shadow Rate and Unconventional Monetary Policy Effects," IMES Discussion Paper Series 18-E-05, Institute for Monetary and Economic Studies, Bank of Japan.

  7. James D. Hamilton & Seth Pruitt & Scott Borger, 2010. "Estimating the Market-Perceived Monetary Policy Rule," NBER Working Papers 16412, National Bureau of Economic Research, Inc.

    Cited by:

    1. Michael D. Bauer & Carolin Pflueger & Adi Sunderam, 2023. "Perceptions about Monetary Policy," Working Paper Series 2023-31, Federal Reserve Bank of San Francisco.
    2. Barrdear, John, 2015. "Towards a new Keynesian theory of the price level," LSE Research Online Documents on Economics 86315, London School of Economics and Political Science, LSE Library.
    3. Michael Ehrmann, 2015. "Targeting Inflation from Below: How Do Inflation Expectations Behave?," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 213-249, September.
    4. Luís Francisco Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2014. "Analyzing the Taylor Rule with Wavelet Lenses," NIPE Working Papers 18/2014, NIPE - Universidade do Minho.
    5. Lapp, John S. & Pearce, Douglas K., 2012. "The impact of economic news on expected changes in monetary policy," Journal of Macroeconomics, Elsevier, vol. 34(2), pages 362-379.
    6. Ricardo Nunes & Ali Ozdagli & Jenny Tang, 2023. "Interest Rate Surprises: A Tale of Two Shocks," Discussion Papers 2320, Centre for Macroeconomics (CFM).
    7. Carvalho, Carlos & Nechio, Fernanda, 2014. "Do people understand monetary policy?," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 108-123.
    8. Luca Metelli & Filippo Natoli & Luca Rossi, 2020. "Monetary policy gradualism and the nonlinear effects of monetary shocks," Temi di discussione (Economic working papers) 1275, Bank of Italy, Economic Research and International Relations Area.
    9. John Y. Campbell & Carolin Pflueger & Luis M. Viceira, 2014. "Macroeconomic Drivers of Bond and Equity Risks," NBER Working Papers 20070, National Bureau of Economic Research, Inc.
    10. Jinill Kim & Seth Pruitt, 2017. "Estimating Monetary Policy Rules When Nominal Interest Rates Are Stuck at Zero," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(4), pages 585-602, June.
    11. Bedri Kamil Onur Tas & Ishak Demir, 2014. "Keep your Word: Time-varying Inflation Targets and Inflation Targeting Performance," Manchester School, University of Manchester, vol. 82(2), pages 160-182, March.
    12. Lakdawala, Aeimit, 2016. "Changes in Federal Reserve preferences," Journal of Economic Dynamics and Control, Elsevier, vol. 70(C), pages 124-143.
    13. Sandra Schmidt & Dieter Nautz, 2012. "Central Bank Communication and the Perception of Monetary Policy by Financial Market Experts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44, pages 323-340, March.
    14. Ma, Yong & Li, Shushu, 2015. "Bayesian estimation of China's monetary policy transparency: A New Keynesian approach," Economic Modelling, Elsevier, vol. 45(C), pages 236-248.
    15. Vijay A Murik, 2013. "Measuring monetary policy expectations," Australian Journal of Management, Australian School of Business, vol. 38(1), pages 49-65, April.
    16. Christopher Healy & Chengcheng Jia, 2024. "Financial Markets’ Perceptions of the FOMC’s Data-Dependent Monetary Policy," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2024(03), pages 1-5, February.
    17. Luís Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2018. "Estimating the Taylor Rule in the Time-Frequency Domain," NIPE Working Papers 04/2018, NIPE - Universidade do Minho.
    18. Gorodnichenko, Y & Coibion, O, 2016. "How inertial is monetary policy? implications for the fed’s exit strategy," Department of Economics, Working Paper Series qt2qc6f09b, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    19. Klodiana Istrefi, 2019. "In Fed Watchers’ Eyes: Hawks, Doves and Monetary Policy," Working papers 725, Banque de France.
    20. Marwil J. Dávila-Fernández & Germana Giombini & Edgar J. Sánchez-Carrera, 2023. "Climateflation and monetary policy in an environmental OLG growth model," Department of Economics University of Siena 905, Department of Economics, University of Siena.
    21. Haitao Li & Tao Li & Cindy Yu, 2013. "No-Arbitrage Taylor Rules with Switching Regimes," Management Science, INFORMS, vol. 59(10), pages 2278-2294, October.
    22. Linda S. Goldberg & Dr. Christian Grisse, 2013. "Time variation in asset price responses to macro announcements," Working Papers 2013-11, Swiss National Bank.

  8. Nir Jaimovich & Seth Pruitt & Henry E. Siu, 2009. "The Demand for Youth: Implications for the Hours Volatility Puzzle," NBER Working Papers 14697, National Bureau of Economic Research, Inc.

    Cited by:

    1. Greg Kaplan, 2014. "Business Cycles and Household Formation," 2014 Meeting Papers 82, Society for Economic Dynamics.
    2. Lugauer, Steven, 2012. "Demographic Change And The Great Moderation In An Overlapping Generations Model With Matching Frictions," Macroeconomic Dynamics, Cambridge University Press, vol. 16(5), pages 706-731, November.
    3. Diana Alessandrini & Stephen Kosempel & Thanasis Stengos, 2014. "The business cycle human capital accumulation nexus and its effect on hours worked volatility," Working Papers 1407, University of Guelph, Department of Economics and Finance.
    4. Ariel Burstein & Javier Cravino & Jonathan Vogel, 2013. "Importing Skill-Biased Technology," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 32-71, April.
    5. Janiak, Alexandre & Santos Monteiro, Paulo, 2016. "Towards a quantitative theory of automatic stabilizers: The role of demographics," Journal of Monetary Economics, Elsevier, vol. 78(C), pages 35-49.
    6. Steven Lugauer, 2012. "Estimating the Effect of the Age Distribution on Cyclical Output Volatility Across the United States," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 896-902, November.
    7. Sebastian Dyrda & Greg Kaplan & José-Víctor Ríos-Rull, 2012. "Business Cycles and Household Formation: The Micro vs the Macro Labor Elasticity," NBER Working Papers 17880, National Bureau of Economic Research, Inc.
    8. Julia Dennett & Alicia Sasser Modestino, 2013. "Uncertain futures?: youth attachment to the labor market in the United States and New England," New England Public Policy Center Research Report 13-3, Federal Reserve Bank of Boston.
    9. Lugauer, Steven & Redmond, Michael, 2012. "The age distribution and business cycle volatility: International evidence," Economics Letters, Elsevier, vol. 117(3), pages 694-696.

  9. Max Floetotto & Nir Jaimovich & Seth Pruitt, 2009. "Markup variation and endogenous fluctuations in the price of investment goods," International Finance Discussion Papers 968, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Alain Gabler, 2014. "Relative Price Fluctuations in a Two-Sector Model with Imperfect Competition," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(3), pages 474-483, July.
    2. Tambalotti, Andrea & Primiceri, Giorgio & Justiniano, Alejandro, 2009. "Investment Shocks and the Relative Price of Investment," CEPR Discussion Papers 7598, C.E.P.R. Discussion Papers.
    3. Marc-Andre Letendre & Joel Wagner, 2015. "Agnecy Costs, Risk Shocks and International Cycles," Department of Economics Working Papers 2015-09, McMaster University.
    4. Yaniv Yedid-Levi, 2016. "Why does employment in all major sectors move together over the business cycle?," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 22, pages 131-156, October.
    5. Sohei Kaihatsu & Takushi Kurozumi, 2010. "Sources of Business Fluctuations: Financial or Technology Shocks?," Bank of Japan Working Paper Series 10-E-12, Bank of Japan.

  10. Scott C. Borger & James D. Hamilton & Seth Pruitt, 2009. "The market-perceived monetary policy rule," International Finance Discussion Papers 982, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    2. Barrdear, John, 2015. "Towards a new Keynesian theory of the price level," LSE Research Online Documents on Economics 86315, London School of Economics and Political Science, LSE Library.
    3. Carvalho, Carlos & Nechio, Fernanda, 2014. "Do people understand monetary policy?," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 108-123.
    4. Jeffrey R. Campbell & Charles L. Evans & Jonas D.M. Fisher & Alejandro Justiniano, 2012. "Macroeconomic Effects of Federal Reserve Forward Guidance," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(1 (Spring), pages 1-80.
    5. Michael D. Bauer, 2011. "Nominal interest rates and the news," Working Paper Series 2011-20, Federal Reserve Bank of San Francisco.
    6. James D. Hamilton & Seth Pruitt & Scott Borger, 2011. "Estimating the Market-Perceived Monetary Policy Rule," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(3), pages 1-28, July.
    7. Luis Viceira & Carolin Pflueger & John Campbell, 2014. "Monetary Policy Drivers of Bond and Equity Risks," 2014 Meeting Papers 137, Society for Economic Dynamics.
    8. Nikolay Markov & Dr. Thomas Nitschka, 2013. "Estimating Taylor Rules for Switzerland: Evidence from 2000 to 2012," Working Papers 2013-08, Swiss National Bank.
    9. Di Maggio, Marco, 2010. "The Political Economy of the Yield Curve," MPRA Paper 20697, University Library of Munich, Germany.

  11. Seth Pruitt, 2008. "Uncertainty over models and data: the rise and fall of American inflation," International Finance Discussion Papers 962, Board of Governors of the Federal Reserve System (U.S.).

    Cited by:

    1. Lubik, Thomas A. & Matthes, Christian, 2016. "Indeterminacy and learning: An analysis of monetary policy in the Great Inflation," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 85-106.
    2. Xueting Yu & Yuhan Zhu & Guangming Lv, 2020. "Analysis of the Impact of China’s GDP Data Revision on Monetary Policy from the Perspective of Uncertainty," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(6), pages 1251-1274, May.
    3. Pablo Aguilar & Jesús Vázquez, 2015. "The role of term structure in an estimated DSGE model with learning," LIDAM Discussion Papers IRES 2015007, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    4. Aguilar, Pablo & Vázquez, Jesús, 2021. "An Estimated Dsge Model With Learning Based On Term Structure Information," Macroeconomic Dynamics, Cambridge University Press, vol. 25(7), pages 1635-1665, October.
    5. Makin, Anthony J. & Robson, Alex & Ratnasiri, Shyama, 2017. "Missing money found causing Australia's inflation," Economic Modelling, Elsevier, vol. 66(C), pages 156-162.
    6. Casares, Miguel & Vázquez, Jesús, 2016. "Data Revisions In The Estimation Of Dsge Models," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1683-1716, October.
    7. Lafuente, Juan A. & Pérez, Rafaela & Ruiz, Jesús, 2014. "Time-varying inflation targeting after the nineties," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 400-408.

Articles

  1. Seth Pruitt & Nicholas Turner, 2020. "Earnings Risk in the Household: Evidence from Millions of US Tax Returns," American Economic Review: Insights, American Economic Association, vol. 2(2), pages 237-254, June.

    Cited by:

    1. Christopher Busch & David Domeij & Fatih Guvenen & Rocio Madera, 2020. "Skewed Idiosyncratic Income Risk over the Business Cycle: Sources and Insurance," Working Papers 1180, Barcelona School of Economics.
    2. Andrew Taeho Kim & Matt Erickson & Yurong Zhang & ChangHwan Kim, 2022. "Who is the “She” in the Pandemic “She-Cession”? Variation in COVID-19 Labor Market Outcomes by Gender and Family Status," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 1325-1358, June.
    3. Titan Alon & Matthias Doepke & Jane Olmstead-Rumsey & Michèle Tertilt, 2020. "This Time It's Different: The Role of Women's Employment in a Pandemic Recession," CRC TR 224 Discussion Paper Series crctr224_2020_198, University of Bonn and University of Mannheim, Germany.
    4. Søren Leth‐Petersen & Johan Sæverud, 2022. "Inequality and dynamics of earnings and disposable income in Denmark 1987–2016," Quantitative Economics, Econometric Society, vol. 13(4), pages 1493-1526, November.
    5. Bloemen, Hans, 2021. "Labor Market Transitions of Members of Opposite-Sex Couples: Nonparticipation, Unemployed Search, and Employment," IZA Discussion Papers 14673, Institute of Labor Economics (IZA).
    6. Ana Sofia Pessoa, 2021. "Earnings Dynamics in Germany," CESifo Working Paper Series 9117, CESifo.
    7. Joseph G. Altonji & Disa M. Hynsjö & Ivan Vidangos, 2022. "Individual Earnings and Family Income: Dynamics and Distribution," NBER Working Papers 30095, National Bureau of Economic Research, Inc.
    8. Tertilt, Michèle & Doepke, Matthias & Olmstead-Rumsey, Jane, 2020. "This Time It’s Different: The Role of Women’s Employment in a Pandemic Recession," CEPR Discussion Papers 15149, C.E.P.R. Discussion Papers.

  2. Kelly, Bryan T. & Pruitt, Seth & Su, Yinan, 2019. "Characteristics are covariances: A unified model of risk and return," Journal of Financial Economics, Elsevier, vol. 134(3), pages 501-524.
    See citations under working paper version above.
  3. De Pooter, Michiel & Martin, Robert F. & Pruitt, Seth, 2018. "The Liquidity Effects of Official Bond Market Intervention," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(1), pages 243-268, February.
    See citations under working paper version above.
  4. Jinill Kim & Seth Pruitt, 2017. "Estimating Monetary Policy Rules When Nominal Interest Rates Are Stuck at Zero," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(4), pages 585-602, June.
    See citations under working paper version above.
  5. Giglio, Stefano & Kelly, Bryan & Pruitt, Seth, 2016. "Systemic risk and the macroeconomy: An empirical evaluation," Journal of Financial Economics, Elsevier, vol. 119(3), pages 457-471.
    See citations under working paper version above.
  6. Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.

    Cited by:

    1. Liya Chu & Xue-Zhong He & Kai Li & Jun Tu, 2022. "Investor Sentiment and Paradigm Shifts in Equity Return Forecasting," Management Science, INFORMS, vol. 68(6), pages 4301-4325, June.
    2. Dai, Zhifeng & Zhang, Xiaotong & Li, Tingyu, 2023. "Forecasting stock return volatility in data-rich environment: A new powerful predictor," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    3. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    4. Elie Bouri & Christina Christou & Rangan Gupta, 2022. "Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models," Working Papers 202213, University of Pretoria, Department of Economics.
    5. Zhang, Lixia & Luo, Qin & Guo, Xiaozhu & Umar, Muhammad, 2022. "Medium-term and long-term volatility forecasts for EUA futures with country-specific economic policy uncertainty indices," Resources Policy, Elsevier, vol. 77(C).
    6. Wen, Chufu & Zhu, Haoyang & Dai, Zhifeng, 2023. "Forecasting commodity prices returns: The role of partial least squares approach," Energy Economics, Elsevier, vol. 125(C).
    7. Lin, Qi, 2022. "Understanding idiosyncratic momentum in the Chinese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    8. Hyeongwoo Kim & Kyunghwan Ko, 2017. "Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach," Auburn Economics Working Paper Series auwp2017-03, Department of Economics, Auburn University.
    9. Zhang, Yaojie & Zeng, Qing & Ma, Feng & Shi, Benshan, 2019. "Forecasting stock returns: Do less powerful predictors help?," Economic Modelling, Elsevier, vol. 78(C), pages 32-39.
    10. Stefano Giglio & Dacheng Xiu, 2017. "Inference on Risk Premia in the Presence of Omitted Factors," NBER Working Papers 23527, National Bureau of Economic Research, Inc.
    11. Roberto S. Mariano & Suleyman Ozmucur, 2021. "Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 383-400, December.
    12. Rangan Gupta & Shawkat Hammoudeh & Mampho P. Modise & Duc Khuong Nguyen, 2013. "Can Economic Uncertainty, Financial Stress and Consumer Sentiments Predict U.S. Equity Premium?," Working Papers 201351, University of Pretoria, Department of Economics.
    13. Chen, Juan & Ma, Feng & Qiu, Xuemei & Li, Tao, 2023. "The role of categorical EPU indices in predicting stock-market returns," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 365-378.
    14. Liao, Cunfei & Luo, Qianlin & Tang, Guohao, 2021. "Aggregate liquidity premium and cross-sectional returns: Evidence from China," Economic Modelling, Elsevier, vol. 104(C).
    15. Chunya Bu & John Rogers & Wenbin Wu, 2019. "A Unified Measure of Fed Monetary Policy Shocks," Finance and Economics Discussion Series 2019-043, Board of Governors of the Federal Reserve System (U.S.).
    16. Shu, Lei & Lu, Feiyang & Chen, Yu, 2023. "Robust forecasting with scaled independent component analysis," Finance Research Letters, Elsevier, vol. 51(C).
    17. Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong, 2023. "Forecasting crude oil market volatility using variable selection and common factor," International Journal of Forecasting, Elsevier, vol. 39(1), pages 486-502.
    18. Marine Carrasco & Barbara Rossi, 2016. "In-Sample Inference and Forecasting in Misspecified Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 313-338, July.
    19. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
    20. Samuel YM Ze‐To, 2022. "Fundamental index aligned and excess market return predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 592-614, April.
    21. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    22. Dai, Zhifeng & Kang, Jie, 2021. "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, vol. 97(C).
    23. Sean P. Grover & Michael W. McCracken, 2014. "Factor-based prediction of industry-wide bank stress," Review, Federal Reserve Bank of St. Louis, vol. 96(2), pages 173-194.
    24. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    25. Matthew F. Dixon & Nicholas G. Polson & Kemen Goicoechea, 2022. "Deep Partial Least Squares for Empirical Asset Pricing," Papers 2206.10014, arXiv.org.
    26. Likun Lei & Yaojie Zhang & Yu Wei & Yi Zhang, 2021. "Forecasting the volatility of Chinese stock market: An international volatility index," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1336-1350, January.
    27. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
    28. Wang, Yunqi & Zhou, Ti, 2023. "Out-of-sample equity premium prediction: The role of option-implied constraints," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 199-226.
    29. Hai Lin & Chunchi Wu & Guofu Zhou, 2018. "Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach," Management Science, INFORMS, vol. 64(9), pages 4218-4238, September.
    30. Caporin, Massimiliano & Costola, Michele & Garibal, Jean-Charles & Maillet, Bertrand, 2022. "Systemic risk and severe economic downturns: A targeted and sparse analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    31. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
    32. Sarthak Behera & Hyeongwoo Kim, 2019. "Forecasting Dollar Real Exchange Rates and the Role of Real Activity Factors," Auburn Economics Working Paper Series auwp2019-04, Department of Economics, Auburn University.
    33. Hao, Yijun & Su, Hao & Zhu, Xiaoneng, 2020. "Rare disaster concerns and economic fluctuations," Economics Letters, Elsevier, vol. 195(C).
    34. Sean P. Grover & Kevin L. Kliesen & Michael W. McCracken, 2016. "A Macroeconomic News Index for Constructing Nowcasts of U.S. Real Gross Domestic Product Growth," Review, Federal Reserve Bank of St. Louis, vol. 98(4), pages 277-296.
    35. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    36. Alois Weigand, 2019. "Machine learning in empirical asset pricing," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(1), pages 93-104, March.
    37. Jan J. J. Groen & Michael Nattinger, 2020. "Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression," Economic Policy Review, Federal Reserve Bank of New York, vol. 26(4), pages 39-68, October.
    38. Michael T. Kiley, 2020. "Financial Conditions and Economic Activity: Insights from Machine Learning," Finance and Economics Discussion Series 2020-095, Board of Governors of the Federal Reserve System (U.S.).
    39. Zhang, Yaojie & He, Mengxi & Wang, Yudong & Liang, Chao, 2023. "Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1318-1332.
    40. Oguzhan Cepni & Rangan Gupta & I. Ethem Guney & M. Hasan Yilmaz, 2019. "Forecasting Local Currency Bond Risk Premia of Emerging Markets: The Role of Cross-Country Macro-Financial Linkages," Working Papers 201957, University of Pretoria, Department of Economics.
    41. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.
    42. Patrick Bielstein, 2018. "International asset allocation using the market implied cost of capital," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(1), pages 17-51, February.
    43. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    44. Antoine A. Djogbenou, 2017. "Model Selection In Factor-augmented Regressions With Estimated Factors," Working Paper 1391, Economics Department, Queen's University.
    45. Gong, Xue & Ye, Xin & Zhang, Weiguo & Zhang, Yue, 2023. "Predicting energy futures high-frequency volatility using technical indicators: The role of interaction," Energy Economics, Elsevier, vol. 119(C).
    46. Shu‐Lien Chang & Hsiu‐Chuan Lee & Donald Lien, 2022. "The global latent factor and international index futures returns predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 514-538, April.
    47. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    48. Stivers, Adam, 2018. "Equity premium predictions with many predictors: A risk-based explanation of the size and value factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 126-140.
    49. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    50. Han, Liyan & Xu, Yang & Yin, Libo, 2018. "Forecasting the CNY-CNH pricing differential: The role of investor attention," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 232-247.
    51. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    52. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
    53. Alessandro Barbarino & Efstathia Bura, 2017. "A Unified Framework for Dimension Reduction in Forecasting," Finance and Economics Discussion Series 2017-004, Board of Governors of the Federal Reserve System (U.S.).
    54. Giglio, Stefano & Kelly, Bryan & Pruitt, Seth, 2016. "Systemic risk and the macroeconomy: An empirical evaluation," Journal of Financial Economics, Elsevier, vol. 119(3), pages 457-471.
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  7. Nir Jaimovich & Seth Pruitt & Henry E. Siu, 2013. "The Demand for Youth: Explaining Age Differences in the Volatility of Hours," American Economic Review, American Economic Association, vol. 103(7), pages 3022-3044, December.

    Cited by:

    1. Mankart, Jochen & Oikonomou, Rigas, 2015. "Household search and the aggregate labor market," Discussion Papers 26/2015, Deutsche Bundesbank.
    2. Guisinger, Amy Y., 2020. "Gender differences in the volatility of work hours and labor demand," Journal of Macroeconomics, Elsevier, vol. 66(C).
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    4. Doepke, M. & Tertilt, M., 2016. "Families in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1789-1891, Elsevier.
    5. Lorenzo Carbonari & Vincenzo Atella & Paola Samà, 2018. "Hours worked in selected OECD countries: an empirical assessment," International Review of Applied Economics, Taylor & Francis Journals, vol. 32(4), pages 525-545, July.
    6. Gerdie Everaert & Hauke Vierke, 2016. "Demographics and Business Cycle Volatility: A Spurious Relationship?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1467-1477, November.
    7. Michael Olabisi, 2020. "Input–Output Linkages and Sectoral Volatility," Economica, London School of Economics and Political Science, vol. 87(347), pages 713-746, July.
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    22. Kathrin Ellieroth, 2017. "Cyclicality of Hours Worked by Married Women and Spousal Insurance," CAEPR Working Papers 2017-009, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

  8. Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.

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    6. Han, Liyan & Xu, Yang & Yin, Libo, 2017. "Does investor attention matter? The attention-return relation in gold futures market," Economics Discussion Papers 2017-37, Kiel Institute for the World Economy (IfW Kiel).
    7. Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021. "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, vol. 78(C).
    8. Tobias Adrian & Evan Friedman & Tyler Muir, 2015. "The cost of capital of the financial sector," Staff Reports 755, Federal Reserve Bank of New York.
    9. Matthew R. Lyle, 2016. "Valuation: Accounting for Risk and the Expected Return. Discussion of Penman," Abacus, Accounting Foundation, University of Sydney, vol. 52(1), pages 131-139, March.
    10. Fernando M. Duarte & Carlo Rosa, 2015. "The equity risk premium: a review of models," Economic Policy Review, Federal Reserve Bank of New York, issue 2, pages 39-57.
    11. Souza, Thiago de Oliveira, 2020. "Dollar carry timing," Discussion Papers on Economics 10/2020, University of Southern Denmark, Department of Economics.
    12. Song, Ziyu & Gong, Xiaomin & Zhang, Cheng & Yu, Changrui, 2023. "Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 528-545.
    13. Wen, Chufu & Zhu, Haoyang & Dai, Zhifeng, 2023. "Forecasting commodity prices returns: The role of partial least squares approach," Energy Economics, Elsevier, vol. 125(C).
    14. Lin, Qi, 2022. "Understanding idiosyncratic momentum in the Chinese stock market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
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    16. Zhi Da & Ravi Jagannathan & Jianfeng Shen, 2014. "Growth Expectations, Dividend Yields, and Future Stock Returns," NBER Working Papers 20651, National Bureau of Economic Research, Inc.
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    18. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
    19. Rangan Gupta & Shawkat Hammoudeh & Mampho P. Modise & Duc Khuong Nguyen, 2013. "Can Economic Uncertainty, Financial Stress and Consumer Sentiments Predict U.S. Equity Premium?," Working Papers 201351, University of Pretoria, Department of Economics.
    20. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2018. "High-frequency Cash Flow Dynamics," Working Papers 120, Brandeis University, Department of Economics and International Business School.
    21. Liao, Cunfei & Luo, Qianlin & Tang, Guohao, 2021. "Aggregate liquidity premium and cross-sectional returns: Evidence from China," Economic Modelling, Elsevier, vol. 104(C).
    22. Andreas Neuhierl & Michael Weber, 2016. "Monetary Policy and the Stock Market: Time-Series Evidence," NBER Working Papers 22831, National Bureau of Economic Research, Inc.
    23. Chunya Bu & John Rogers & Wenbin Wu, 2019. "A Unified Measure of Fed Monetary Policy Shocks," Finance and Economics Discussion Series 2019-043, Board of Governors of the Federal Reserve System (U.S.).
    24. Neuhierl, Andreas & Weber, Michael, 2019. "Monetary policy communication, policy slope, and the stock market," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 140-155.
    25. Moreira, Alan & Muir, Tyler, 2019. "Should Long-Term Investors Time Volatility?," Journal of Financial Economics, Elsevier, vol. 131(3), pages 507-527.
    26. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2020. "Cash Flow News and Stock Price Dynamics," Journal of Finance, American Finance Association, vol. 75(4), pages 2221-2270, August.
    27. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
    28. João F. Caldeira & Rangan Gupta & Hudson S. Torrent, 2020. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
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      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    31. Bruno Feunou & Mohammad R. Jahan-Parvar & Cédric Okou, 2015. "Downside Variance Risk Premium," Staff Working Papers 15-36, Bank of Canada.
    32. Li, Jun & Wang, Huijun & Yu, Jianfeng, 2018. "Aggregate Expected Investment Growth and Stock Market Returns," ADBI Working Papers 808, Asian Development Bank Institute.
    33. Dai, Zhifeng & Kang, Jie, 2021. "Bond yield and crude oil prices predictability," Energy Economics, Elsevier, vol. 97(C).
    34. Arim Jin & Dahan Lee & Jong-Bae Park & Jae Hyung Roh, 2023. "Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation," Energies, MDPI, vol. 16(7), pages 1-19, April.
    35. Gabriela ANGHELACHE & Alexandru MANOLE & Mugurel POPOVICI, 2016. "The evolution of the insurances market in Romania," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(11), pages 55-66, November.
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    40. Davide Pettenuzzo & Allan Timmermann & Rossen Valkanov, 2013. "Forecasting Stock Returns under Economic Constraints," Working Papers 57, Brandeis University, Department of Economics and International Business School.
    41. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
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    44. Wang, Yudong & Liu, Li & Ma, Feng & Diao, Xundi, 2018. "Momentum of return predictability," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 141-156.
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    46. Soosung Hwang & Youngha Cho & Jinho Shin, 2020. "The impact of UK household overconfidence in public information on house prices," Journal of Property Research, Taylor & Francis Journals, vol. 37(4), pages 360-389, October.
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