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Ajit Desai

Personal Details

First Name:Ajit
Middle Name:
Last Name:Desai
Suffix:
RePEc Short-ID:pde1340
[This author has chosen not to make the email address public]
https://www.linkedin.com/in/ajit-desai/

Affiliation

Bank of Canada

Ottawa, Canada
http://www.bank-banque-canada.ca/
RePEc:edi:bocgvca (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Ajit Desai & Anneke Kosse & Jacob Sharples, 2024. "Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems," Staff Working Papers 24-15, Bank of Canada.
  2. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
  3. Ajit Desai & Zhentong Lu & Hiru Rodrigo & Jacob Sharples & Phoebe Tian & Nellie Zhang, 2023. "From LVTS to Lynx: Quantitative Assessment of Payment System Transition," Staff Working Papers 23-24, Bank of Canada.
  4. James T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.
  5. Christopher McMahon & Donald McGillivray & Ajit Desai & Francisco Rivadeneyra & Jean-Paul Lam & Thomas Lo & Danica Marsden & Vladimir Skavysh, 2022. "Improving the Efficiency of Payments Systems Using Quantum Computing," Staff Working Papers 22-53, Bank of Canada.
  6. Pablo S. Castro & Ajit Desai & Han Du & Rodney Garratt & Francisco Rivadeneyra, 2021. "Estimating Policy Functions in Payments Systems Using Reinforcement Learning," Staff Working Papers 21-7, Bank of Canada.
  7. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.

Articles

  1. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  2. Desai, Ajit & Lu, Zhentong & Rodrigo, Hiru & Sharples, Jacob & Tian, Phoebe & Zhang, Nellie, 2023. "From LVTS to Lynx: Quantitative assessment of payment system transition in Canada," Journal of Payments Strategy & Systems, Henry Stewart Publications, vol. 17(3), pages 291-314, September.
  3. Ajit Desai & Sunetra Sarkar, 2010. "Analysis of a Nonlinear Aeroelastic System with Parametric Uncertainties Using Polynomial Chaos Expansion," Mathematical Problems in Engineering, Hindawi, vol. 2010, pages 1-21, July.

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.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.

    Mentioned in:

    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Consequences > Macroeconomic

Working papers

  1. Ajit Desai & Zhentong Lu & Hiru Rodrigo & Jacob Sharples & Phoebe Tian & Nellie Zhang, 2023. "From LVTS to Lynx: Quantitative Assessment of Payment System Transition," Staff Working Papers 23-24, Bank of Canada.

    Cited by:

    1. Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
    2. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    3. Irving Fisher Committee, 2024. "Granular data: new horizons and challenges," IFC Bulletins, Bank for International Settlements, number 61.

  2. James T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.

    Cited by:

    1. Bogner Alexandra & Jerger Jürgen, 2023. "Big data in monetary policy analysis—a critical assessment," Economics and Business Review, Sciendo, vol. 9(2), pages 27-40, April.
    2. Vansh Murad Kalia, 2024. "Packing Peanuts: The Role Synthetic Data Can Play in Enhancing Conventional Economic Prediction Models," Papers 2405.07431, arXiv.org.
    3. Laura Felber & Dr. Simon Beyeler, 2023. "Nowcasting economic activity using transaction payments data," Working Papers 2023-01, Swiss National Bank.

  3. Christopher McMahon & Donald McGillivray & Ajit Desai & Francisco Rivadeneyra & Jean-Paul Lam & Thomas Lo & Danica Marsden & Vladimir Skavysh, 2022. "Improving the Efficiency of Payments Systems Using Quantum Computing," Staff Working Papers 22-53, Bank of Canada.

    Cited by:

    1. Raphael Auer & Angela Dupont & Leonardo Gambacorta & Joon Suk Park & Koji Takahashi & Andras Valko, 2024. "Quantum computing and the financial system: opportunities and risks," BIS Papers, Bank for International Settlements, number 149.
    2. Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
    3. Irving Fisher Committee, 2024. "Granular data: new horizons and challenges," IFC Bulletins, Bank for International Settlements, number 61.
    4. Skavysh, Vladimir & Priazhkina, Sofia & Guala, Diego & Bromley, Thomas R., 2023. "Quantum monte carlo for economics: Stress testing and macroeconomic deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).

  4. Pablo S. Castro & Ajit Desai & Han Du & Rodney Garratt & Francisco Rivadeneyra, 2021. "Estimating Policy Functions in Payments Systems Using Reinforcement Learning," Staff Working Papers 21-7, Bank of Canada.

    Cited by:

    1. Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
    2. Hinterlang, Natascha & Tänzer, Alina, 2021. "Optimal monetary policy using reinforcement learning," Discussion Papers 51/2021, Deutsche Bundesbank.
    3. Irving Fisher Committee, 2024. "Granular data: new horizons and challenges," IFC Bulletins, Bank for International Settlements, number 61.
    4. Francisco Rivadeneyra & Nellie Zhang, 2022. "Payment Coordination and Liquidity Efficiency in the New Canadian Wholesale Payments System," Discussion Papers 2022-3, Bank of Canada.

  5. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.

    Cited by:

    1. Kakuho Furukawa & Ryohei Hisano & Yukio Minoura & Tomoyuki Yagi, 2022. "A Nowcasting Model of Industrial Production using Alternative Data and Machine Learning Approaches," Bank of Japan Working Paper Series 22-E-16, Bank of Japan.
    2. Tatjana Dahlhaus & Angelika Welte, 2021. "Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data," Staff Working Papers 21-43, Bank of Canada.
    3. Tomohiro Okubo & Koji Takahashi & Haruhiko Inatsugu & Masato Takahashi, "undated". "Development of "Alternative Data Consumption Index":Nowcasting Private Consumption Using Alternative Data," Bank of Japan Working Paper Series 22-E-8, Bank of Japan.
    4. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    5. Ludmila Fadejeva & Boriss Siliverstovs & Karlis Vilerts & Anete Brinke, 2022. "Consumer Spending in the Covid-19 Pandemic: Evidence from Card Transactions in Latvia," Discussion Papers 2022/01, Latvijas Banka.
    6. Paulick, Jan, 2022. "Financial market infrastructures : Essays on liquidity, participant behaviour and information extraction," Other publications TiSEM 004942ed-f68d-40cc-a830-b, Tilburg University, School of Economics and Management.
    7. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    8. Sabetti, Leonard & Heijmans, Ronald, 2021. "Shallow or deep? Training an autoencoder to detect anomalous flows in a retail payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(2).

Articles

  1. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    See citations under working paper version above.
  2. Desai, Ajit & Lu, Zhentong & Rodrigo, Hiru & Sharples, Jacob & Tian, Phoebe & Zhang, Nellie, 2023. "From LVTS to Lynx: Quantitative assessment of payment system transition in Canada," Journal of Payments Strategy & Systems, Henry Stewart Publications, vol. 17(3), pages 291-314, September.

    Cited by:

    1. Ajit Desai & Jacob Sharples & Anneke Kosse, 2024. "Finding a needle in a haystack: a machine learning framework for anomaly detection in payment systems," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.
    2. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    3. Irving Fisher Committee, 2024. "Granular data: new horizons and challenges," IFC Bulletins, Bank for International Settlements, number 61.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

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 9 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-PAY: Payment Systems and Financial Technology (7) 2021-02-01 2021-03-01 2022-04-11 2022-09-26 2023-01-16 2023-05-08 2024-06-17. Author is listed
  2. NEP-BIG: Big Data (6) 2021-02-01 2022-04-11 2022-09-26 2023-05-22 2023-11-13 2024-06-17. Author is listed
  3. NEP-CMP: Computational Economics (5) 2021-02-01 2022-04-11 2022-09-26 2023-05-22 2023-11-13. Author is listed
  4. NEP-BAN: Banking (3) 2023-05-08 2023-11-13 2024-06-17
  5. NEP-MAC: Macroeconomics (3) 2021-02-01 2021-03-01 2022-04-11
  6. NEP-CBA: Central Banking (2) 2021-03-01 2023-11-13
  7. NEP-MON: Monetary Economics (2) 2023-05-08 2024-06-17
  8. NEP-CWA: Central and Western Asia (1) 2022-04-11
  9. NEP-ECM: Econometrics (1) 2023-05-22
  10. NEP-FDG: Financial Development and Growth (1) 2022-04-11
  11. NEP-ORE: Operations Research (1) 2021-03-01
  12. NEP-PKE: Post Keynesian Economics (1) 2023-11-13

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