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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 & 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.
  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.
  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.

Articles

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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 & 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. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.
    3. 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.
    4. 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.
    5. 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.

  2. 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. 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. 53(6), pages 286-303, January.
    2. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org.
    3. 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.
    4. Suproteem K. Sarkar & Kojin Oshiba & Daniel Giebisch & Yaron Singer, 2018. "Robust Classification of Financial Risk," Papers 1811.11079, arXiv.org.
    5. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    6. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    7. Adam Richardson & Thomas van Florenstein Mulder & Tugrul Vehbi, 2019. "Nowcasting New Zealand GDP using machine learning algorithms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
    8. 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.
    9. 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.
    10. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
    11. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    12. Livia Paranhos, 2021. "Predicting Inflation with Neural Networks," Papers 2104.03757, arXiv.org.
    13. 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.
    14. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    15. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    16. 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).
    17. 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.
    18. Anna Ter-Martirosyan & Manasa Patnam & Jin-Kyu Jung, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
    19. 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.
    20. 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 & 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," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 29(1), pages 61-86, January.

  2. 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).

  3. 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. 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.

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 4 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 (3) 2018-01-22 2019-10-14 2022-02-21. Author is listed
  2. NEP-CMP: Computational Economics (3) 2018-01-22 2019-10-14 2022-02-21. Author is listed
  3. NEP-FOR: Forecasting (2) 2018-01-22 2019-10-14. Author is listed
  4. NEP-ORE: Operations Research (2) 2018-01-22 2022-02-21. Author is listed
  5. NEP-ECM: Econometrics (1) 2022-02-21. Author is listed
  6. NEP-ETS: Econometric Time Series (1) 2018-01-22. Author is listed
  7. NEP-GTH: Game Theory (1) 2022-02-21. Author is listed
  8. NEP-RMG: Risk Management (1) 2020-03-30. Author is listed

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