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Aaron Smalter Hall

Personal Details

First Name:Aaron
Middle Name:
Last Name:Smalter Hall
Suffix:
RePEc Short-ID:psm212
https://scholar.google.com/citations?user=DlUgDOQAAAAJ

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. Jason Brown & Maeve Maloney & Jordan Rappaport & Aaron Smalter Hall, 2017. "How Centralized is U.S. Metropolitan Employment?," Research Working Paper RWP 17-16, Federal Reserve Bank of Kansas City.
  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.
  3. Troy Davig & Aaron Smalter Hall, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City.

Articles

  1. Aaron Smalter Hall, 2018. "Machine Learning Approaches to Macroeconomic Forecasting," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 63-81.
  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.

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 & 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. 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.
    3. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
    4. 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.
    5. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    6. Suproteem K. Sarkar & Kojin Oshiba & Daniel Giebisch & Yaron Singer, 2018. "Robust Classification of Financial Risk," Papers 1811.11079, arXiv.org.
    7. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    8. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    9. 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.
    10. 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.
    11. 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.
    12. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    13. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
    14. 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.
    15. 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.
    16. 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.
    17. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    18. 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).
    19. 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.
    20. 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.
    21. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.
    27. 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.
    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.

  2. Troy Davig & Aaron Smalter Hall, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City.

    Cited by:

    1. Zihao Wang & Kun Li & Steve Q. Xia & Hongfu Liu, 2021. "Economic Recession Prediction Using Deep Neural Network," Papers 2107.10980, arXiv.org.
    2. Alexander James & Yaser S. Abu-Mostafa & Xiao Qiao, 2019. "Nowcasting Recessions using the SVM Machine Learning Algorithm," Papers 1903.03202, arXiv.org, revised Jun 2019.
    3. Heiberger, Raphael H., 2018. "Predicting economic growth with stock networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 102-111.

Articles

  1. Aaron Smalter Hall, 2018. "Machine Learning Approaches to Macroeconomic Forecasting," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 63-81.

    Cited by:

    1. Marijn A. Bolhuis & Brett Rayner, 2020. "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers 2020/044, International Monetary Fund.
    2. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    3. Anastasios Petropoulos & Vassilis Siakoulis & Konstantinos P. Panousis & Loukas Papadoulas & Sotirios Chatzis, 2023. "Macroeconomic forecasting and sovereign risk assessment using deep learning techniques," Papers 2301.09856, arXiv.org.
    4. Phi-Hung Nguyen & Jung-Fa Tsai & Ihsan Erdem Kayral & Ming-Hua Lin, 2021. "Unemployment Rates Forecasting with Grey-Based Models in the Post-COVID-19 Period: A Case Study from Vietnam," Sustainability, MDPI, vol. 13(14), pages 1-27, July.
    5. Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.

  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," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 29(1), pages 61-86, January.

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 3 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-FOR: Forecasting (2) 2016-10-09 2018-01-22. Author is listed
  2. NEP-MAC: Macroeconomics (2) 2016-10-09 2018-01-22. Author is listed
  3. NEP-ORE: Operations Research (2) 2016-10-09 2018-01-22. Author is listed
  4. NEP-BIG: Big Data (1) 2018-01-22. Author is listed
  5. NEP-CMP: Computational Economics (1) 2018-01-22. Author is listed
  6. NEP-ECM: Econometrics (1) 2016-10-09. Author is listed
  7. NEP-ETS: Econometric Time Series (1) 2018-01-22. Author is listed
  8. NEP-GEO: Economic Geography (1) 2018-01-22. Author is listed
  9. NEP-URE: Urban and Real Estate Economics (1) 2018-01-22. Author is listed

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