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Thomas R. Cook

Personal Details

First Name:Thomas
Middle Name:R.
Last Name:Cook
Suffix:
RePEc Short-ID:pco928
[This author has chosen not to make the email address public]
http://thomasrcook.com

Affiliation

Economic Research
Federal Reserve Bank of Kansas City

Kansas City, Missouri (United States)
http://www.kansascityfed.org/research/
RePEc:edi:efrbkus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Thomas R. Cook & Zach Modig & Nathan M. Palmer, 2024. "Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values," Finance and Economics Discussion Series 2024-075, Board of Governors of the Federal Reserve System (U.S.).
  2. Thomas R. Cook & Nathan M. Palmer, 2023. "Understanding Models and Model Bias with Gaussian Processes," Research Working Paper RWP 23-07, Federal Reserve Bank of Kansas City.
  3. Thomas R. Cook & Sophia Kazinnik & Anne Lundgaard Hansen & Peter McAdam, 2023. "Evaluating Local Language Models: An Application to Bank Earnings Calls," Research Working Paper RWP 23-12, Federal Reserve Bank of Kansas City.
  4. Thomas R. Cook & Greg Gupton & Zach Modig & Nathan M. Palmer, 2021. "Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values," Research Working Paper RWP 21-12, Federal Reserve Bank of Kansas City.
  5. Thomas R. Cook & Taeyoung Doh, 2019. "Assessing Macroeconomic Tail Risks in a Data-Rich Environment," Research Working Paper RWP 19-12, Federal Reserve Bank of Kansas City.
  6. Thomas R. Cook & Aaron Smalter Hall, 2017. "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper RWP 17-11, Federal Reserve Bank of Kansas City.

    repec:fip:fedkrr:96511 is not listed on IDEAS

Articles

  1. Thomas R. Cook & Amaze Lusompa & Johannes Matschke, 2024. "Testing Hybrid Forecasts for Imports and Exports," Economic Review, Federal Reserve Bank of Kansas City, June.
  2. Thomas R. Cook & Johannes Matschke, 2023. "China's Post-COVID Recovery: Implications and Risks," Economic Bulletin, Federal Reserve Bank of Kansas City, pages 1-4, May.
  3. Thomas R. Cook & Amaze Lusompa & Jun Nie, 2022. "Disruptions to Russian Energy Supply Likely to Weigh on European Output," Economic Bulletin, Federal Reserve Bank of Kansas City, issue November , pages 1-4, November.
  4. Thomas R. Cook & Taeyoung Doh, 2021. "To Improve the Accuracy of GDP Growth Forecasts, Add Financial Market Conditions," Economic Bulletin, Federal Reserve Bank of Kansas City, issue June 2, 2, pages 1-5, June.
  5. Thomas R. Cook & Taeyoung Doh, 2019. "Assessing the Risk of Extreme Unemployment Outcomes," Economic Bulletin, Federal Reserve Bank of Kansas City, issue Aug 28, 2, pages 1-4, August.
  6. Thomas R. Cook & Jun Nie & Aaron Smalter Hall, 2018. "How Much Would China’s GDP Respond to a Slowdown in Housing Activity?," Macro Bulletin, Federal Reserve Bank of Kansas City, issue September, pages 1-5, September.
  7. David H. Bearce & Thomas R. Cook, 2018. "The first image reversed: IGO signals and mass political attitudes," The Review of International Organizations, Springer, vol. 13(4), pages 595-619, December.
  8. Thomas R. Cook & Taeyoung Doh, 2018. "Revamping the Kansas City Financial Stress Index Using the Treasury Repo Rate," Macro Bulletin, Federal Reserve Bank of Kansas City, issue October 2, pages 1-2, October.
  9. Cook, Thomas R. & Liu, Amy H., 2016. "Using Linguistic Networks to Explain Strength of Intellectual Property Rights," World Development, Elsevier, vol. 87(C), pages 128-138.

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. Thomas R. Cook & Greg Gupton & Zach Modig & Nathan M. Palmer, 2021. "Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values," Research Working Paper RWP 21-12, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Thomas R. Cook & Nathan M. Palmer, 2023. "Understanding Models and Model Bias with Gaussian Processes," Research Working Paper RWP 23-07, Federal Reserve Bank of Kansas City.

  2. Thomas R. Cook & Taeyoung Doh, 2019. "Assessing Macroeconomic Tail Risks in a Data-Rich Environment," Research Working Paper RWP 19-12, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    3. Deng, Chuang & Wu, Jian, 2023. "Macroeconomic downside risk and the effect of monetary policy," Finance Research Letters, Elsevier, vol. 54(C).
    4. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Papers 2110.03411, arXiv.org.
    5. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    6. Gupta, Rangan & Sheng, Xin & Pierdzioch, Christian & Ji, Qiang, 2021. "Disaggregated oil shocks and stock-market tail risks: Evidence from a panel of 48 economics," Research in International Business and Finance, Elsevier, vol. 58(C).
    7. Rangan Gupta & Xin Sheng & Christian Pierdzioch & Qiang Ji, 2021. "Disaggregated Oil Shocks and Stock-Market Tail Risks: Evidence from a Panel of 48 Countries," Working Papers 202106, University of Pretoria, Department of Economics.

  3. Thomas R. Cook & Aaron Smalter Hall, 2017. "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper RWP 17-11, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Bank of Finland Research Discussion Papers 14/2019, Bank of Finland.
    2. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
    3. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    4. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    5. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    6. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    7. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2018. "Nowcasting New Zealand GDP using machine learning algorithms," CAMA Working Papers 2018-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
    9. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    10. Cameron Fen & Samir Undavia, 2022. "Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model," Papers 2203.06540, arXiv.org.
    11. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    12. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    13. Jin-Kyu Jung & Manasa Patnam & Anna Ter-Martirosyan, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
    14. Suproteem K. Sarkar & Kojin Oshiba & Daniel Giebisch & Yaron Singer, 2018. "Robust Classification of Financial Risk," Papers 1811.11079, arXiv.org.
    15. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    16. Mihail Yanchev, 2022. "Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 20-41.
    17. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    18. Anna Almosova & Niek Andresen, 2023. "Nonlinear inflation forecasting with recurrent neural networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 240-259, March.
    19. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    20. Alexey Bilgaev & Suocheng Dong & Fujia Li & Hao Cheng & Arnold Tulohonov & Erzhena Sadykova & Anna Mikheeva, 2020. "Baikal Region (Russia) Development Prospects Based on the Green Economy Principles," Sustainability, MDPI, vol. 13(1), pages 1-22, December.
    21. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    22. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
    23. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    24. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org, revised Oct 2024.
    25. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting GDP using machine learning algorithms: A real-time assessment," Reserve Bank of New Zealand Discussion Paper Series DP2019/03, Reserve Bank of New Zealand.
    26. Pedro Gerber Machado & Julia Tomei & Adam Hawkes & Celma de Oliveira Ribeiro, 2020. "A Simulator to Determine the Evolution of Disparities in Food Consumption between Socio-Economic Groups: A Brazilian Case Study," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
    27. Periklis Gogas & Theophilos Papadimitriou & Emmanouil Sofianos, 2022. "Forecasting unemployment in the euro area with machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 551-566, April.
    28. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.

Articles

  1. Thomas R. Cook & Taeyoung Doh, 2019. "Assessing the Risk of Extreme Unemployment Outcomes," Economic Bulletin, Federal Reserve Bank of Kansas City, issue Aug 28, 2, pages 1-4, August.

    Cited by:

    1. Ling Lin & Qiumei Li & Jin Li & Zuominyang Zhang & Xuan Zhong, 2023. "Industry Volatility and Employment Extreme Risk Transmission: Evidence from China," Sustainability, MDPI, vol. 15(17), pages 1-22, August.

  2. Thomas R. Cook & Jun Nie & Aaron Smalter Hall, 2018. "How Much Would China’s GDP Respond to a Slowdown in Housing Activity?," Macro Bulletin, Federal Reserve Bank of Kansas City, issue September, pages 1-5, September.

    Cited by:

    1. Yongming Huang & Jamal Khan & Eric Girardin & Umair Shad, 2021. "The Role of the Real Estate Sector in the Structural Dynamics of the Chinese Economy: An Input–Output Analysis," Post-Print hal-03541283, HAL.

  3. David H. Bearce & Thomas R. Cook, 2018. "The first image reversed: IGO signals and mass political attitudes," The Review of International Organizations, Springer, vol. 13(4), pages 595-619, December.

    Cited by:

    1. Plouffe, Michael, 2024. "The Public and the Assembly: Foreign Public Opinion and Voting in the UNGA," OSF Preprints pu2yv, Center for Open Science.
    2. Asif Efrat & Omer Yair, 2023. "International rankings and public opinion: Compliance, dismissal, or backlash?," The Review of International Organizations, Springer, vol. 18(4), pages 607-629, October.
    3. Christoph Mikulaschek, 2023. "The responsive public: How European Union decisions shape public opinion on salient policies," European Union Politics, , vol. 24(4), pages 645-665, December.

  4. Thomas R. Cook & Taeyoung Doh, 2018. "Revamping the Kansas City Financial Stress Index Using the Treasury Repo Rate," Macro Bulletin, Federal Reserve Bank of Kansas City, issue October 2, pages 1-2, October.

    Cited by:

    1. Haddou, Samira, 2022. "International financial stress spillovers to bank lending: Do internal characteristics matter?," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2. Elias, Nikolaos & Smyrnakis, Dimitris & Tzavalis, Elias, 2024. "The forward premium anomaly and the currency carry trade hypothesis," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 203-218.

  5. Cook, Thomas R. & Liu, Amy H., 2016. "Using Linguistic Networks to Explain Strength of Intellectual Property Rights," World Development, Elsevier, vol. 87(C), pages 128-138.

    Cited by:

    1. Gnangnon, Sèna Kimm, 2023. "The Least developed countries' TRIPS Waiver and the Strength of Intellectual Property Protection," EconStor Preprints 271537, ZBW - Leibniz Information Centre for Economics.
    2. Konara, Palitha & Wei, Yingqi, 2019. "The complementarity of human capital and language capital in foreign direct investment," International Business Review, Elsevier, vol. 28(2), pages 391-404.
    3. Gnangnon, Sèna Kimm, 2023. "The least developed countries' transitional exemption in the TRIPS agreement and the strength of intellectual property protection," Information Economics and Policy, Elsevier, vol. 65(C).

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (6) 2018-01-22 2019-10-14 2022-02-21 2023-09-11 2023-11-20 2023-12-04. Author is listed
  2. NEP-CMP: Computational Economics (5) 2018-01-22 2019-10-14 2022-02-21 2023-11-20 2023-12-04. Author is listed
  3. NEP-MAC: Macroeconomics (3) 2018-01-22 2019-10-14 2020-03-30. Author is listed
  4. NEP-AIN: Artificial Intelligence (2) 2023-09-11 2023-12-04. Author is listed
  5. NEP-ECM: Econometrics (2) 2022-02-21 2023-09-11. Author is listed
  6. NEP-FOR: Forecasting (2) 2018-01-22 2019-10-14. Author is listed
  7. NEP-ORE: Operations Research (2) 2018-01-22 2022-02-21. Author is listed
  8. NEP-BAN: Banking (1) 2023-12-04
  9. NEP-ETS: Econometric Time Series (1) 2018-01-22
  10. NEP-GTH: Game Theory (1) 2022-02-21
  11. NEP-RMG: Risk Management (1) 2020-03-30

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