Chiranjit Chakraborty
Personal Details
First Name: | Chiranjit |
Middle Name: | |
Last Name: | Chakraborty |
Suffix: | |
RePEc Short-ID: | pch1601 |
[This author has chosen not to make the email address public] | |
Affiliation
Bank of England
London, United Kingdomhttp://www.bankofengland.co.uk/
RePEc:edi:boegvuk (more details at EDIRC)
Research output
Jump to: Working papersWorking papers
- Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
- Chakraborty, Chiranjit & Gimpelewicz, Mariana & Uluc, Arzu, 2017. "A tiger by the tail: estimating the UK mortgage market vulnerabilities from loan-level data," Bank of England working papers 703, Bank of England.
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
- Chakraborty, Chiranjit & Joseph, Andreas, 2017.
"Machine learning at central banks,"
Bank of England working papers
674, Bank of England.
Cited by:
- Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
- Amarda Cano, 2021. "Evolution of Public Debt in Albania during 1990-2017 and its impact on the Economic Growth," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 4, ejme_v4_i.
- Emanuel Kohlscheen, 2022.
"Quantifying the role of interest rates, the Dollar and Covid in oil prices,"
BIS Working Papers
1040, Bank for International Settlements.
- Emanuel Kohlscheen, 2022. "Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices," Papers 2208.14254, arXiv.org, revised Oct 2022.
- Amadxarif, Zahid & Brookes, James & Garbarino, Nicola & Patel, Rajan & Walczak, Eryk, 2019. "The language of rules: textual complexity in banking reforms," Bank of England working papers 834, Bank of England.
- Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
- Ryan Defina, 2021.
"Machine Learning Methods: Potential for Deposit Insurance,"
IADI Fintech Briefs
3, International Association of Deposit Insurers.
- Defina, Ryan, 2021. "Machine Learning Methods: Potential for Deposit Insurance," MPRA Paper 110712, University Library of Munich, Germany.
- Andreas Joseph, 2019.
"Parametric inference with universal function approximators,"
Papers
1903.04209, arXiv.org, revised Oct 2020.
- Joseph, Andreas, 2019. "Parametric inference with universal function approximators," Bank of England working papers 784, Bank of England, revised 22 Jul 2020.
- Joseph, Andreas & Vasios, Michalis, 2022. "OTC Microstructure in a period of stress: A Multi-layered network approach," Journal of Banking & Finance, Elsevier, vol. 138(C).
- Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
- Guerra, Pedro & Castelli, Mauro & Côrte-Real, Nadine, 2022. "Machine learning for liquidity risk modelling: A supervisory perspective," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 175-187.
- Funke, Michael & Tsang, Andrew, 2019. "The direction and intensity of China's monetary policy conduct: A dynamic factor modelling approach," BOFIT Discussion Papers 8/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
- Tsang, Andrew, 2021.
"Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy,"
MPRA Paper
110703, University Library of Munich, Germany.
- Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," WiSo-HH Working Paper Series 62, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021.
"Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks,"
Bank of Israel Working Papers
2021.06, Bank of Israel.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2020. "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Papers 2011.07920, arXiv.org, revised Feb 2022.
- Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," Post-Print emse-04624940, HAL.
- Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
- Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
- Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2021. "Learning from revisions: a tool for detecting potential errors in banks' balance sheet statistical reporting," Questioni di Economia e Finanza (Occasional Papers) 611, Bank of Italy, Economic Research and International Relations Area.
- Tamara, Novian & Dwi Muchisha, Nadya & Andriansyah, Andriansyah & Soleh, Agus M, 2020. "Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms," MPRA Paper 105235, University Library of Munich, Germany.
- 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.
- 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.
- Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
- 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.
- Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
- Zahner, Johannes & Baumgärtner, Martin, 2022.
"Whatever it Takes to Understand a Central Banker – Embedding their Words Using Neural Networks,"
VfS Annual Conference 2022 (Basel): Big Data in Economics
264019, Verein für Socialpolitik / German Economic Association.
- Martin Baumgaertner & Johannes Zahner, 2021. "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics 202130, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- Natalia Nehrebecka, 2021. "Internal Credit Risk Models and Digital Transformation: What to Prepare for? An Application to Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 3), pages 719-736.
- Andrei Shevelev & Maria Kvaktun & Kristina Virovets, 2021. "Effect of Monetary Policy on Investment in Russian Regions," Russian Journal of Money and Finance, Bank of Russia, vol. 80(4), pages 31-49, December.
- Karol Szafranek, 2017.
"Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks,"
NBP Working Papers
262, Narodowy Bank Polski.
- Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
- Fabio Zambuto & Simona Arcuti & Roberto Sabatini & Daniele Zambuto, 2021. "Application of classification algorithms for the assessment of confirmation to quality remarks," Questioni di Economia e Finanza (Occasional Papers) 631, Bank of Italy, Economic Research and International Relations Area.
- Joe McLaughlin & Nathan Palmer & Adam Minson & Eric Parolin, 2018. "The OFR Financial System Vulnerabilities Monitor," Working Papers 18-01, Office of Financial Research, US Department of the Treasury.
- Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
- Joseph, Andreas & Vasios, Michalis & Maizels, Olga & Shreyas, Ujwal & Tanner, John, 2019. "OTC microstructure in a period of stress: a multi‑layered network approach," Bank of England working papers 832, Bank of England.
- 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.
- 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.
- Niko Hauzenberger & Florian Huber & Karin Klieber, 2020. "Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques," Papers 2012.08155, arXiv.org, revised Dec 2021.
- 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.
- Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
- James T. E. Chapman & Ajit Desai, 2022.
"Macroeconomic Predictions using Payments Data and Machine Learning,"
Papers
2209.00948, arXiv.org.
- James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
- James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
- Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021.
"Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach,"
Working Paper Series
2614, European Central Bank.
- Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kang, Miao & Kapadia, Sujit & Simsek, Özgür, 2020. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Bank of England working papers 848, Bank of England.
- Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
- Dmytro Krukovets, 2020. "Data Science Opportunities at Central Banks: Overview," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 249, pages 13-24.
- Fabio Zambuto, 2021.
"Quality checks on granular banking data: an experimental approach based on machine learning,"
IFC Bulletins chapters, in: Bank for International Settlements (ed.), Micro data for the macro world, volume 53,
Bank for International Settlements.
- Fabio Zambuto & Maria Rosaria Buzzi & Giuseppe Costanzo & Marco Di Lucido & Barbara La Ganga & Pasquale Maddaloni & Fabio Papale & Emiliano Svezia, 2020. "Quality checks on granular banking data: an experimental approach based on machine learning?," Questioni di Economia e Finanza (Occasional Papers) 547, Bank of Italy, Economic Research and International Relations Area.
- Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
- Lisa-Cheree Martin, 2019. "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers 12/2019, Stellenbosch University, Department of Economics.
- Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
- Carlos Moreno Pérez & Marco Minozzo, 2022. "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers 2240, Banco de España.
- Rohan Arora & Chen Fan & Guillaume Ouellet Leblanc, 2019. "Liquidity Management of Canadian Corporate Bond Mutual Funds: A Machine Learning Approach," Staff Analytical Notes 2019-7, Bank of Canada.
- Carlos León & Fabio Ortega, 2018.
"Nowcasting economic activity with electronic payments data: A predictive modeling approach,"
Borradores de Economia
1037, Banco de la Republica de Colombia.
- Carlos León & Fabio Ortega, 2018. "Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach," Revista de Economía del Rosario, Universidad del Rosario, vol. 21(2), pages 381-407, December.
- Kim Long Tran & Hoang Anh Le & Thanh Hien Nguyen & Duc Trung Nguyen, 2022. "Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam," Data, MDPI, vol. 7(11), pages 1-12, November.
- Emanuel Kohlscheen, 2021.
"What does machine learning say about the drivers of inflation?,"
BIS Working Papers
980, Bank for International Settlements.
- Emanuel Kohlscheen, 2022. "What does machine learning say about the drivers of inflation?," Papers 2208.14653, arXiv.org, revised Jan 2023.
- Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
- Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
- Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- Felipe Leal & Carlos Molina & Eduardo Zilberman, 2020. "Proyección de la Inflación en Chile con Métodos de Machine Learning," Working Papers Central Bank of Chile 860, Central Bank of Chile.
- 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.
- Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
- Romain Plassard, 2020. "Making a Breach: The Incorporation of Agent-Based Models into the Bank of England's Toolkit," GREDEG Working Papers 2020-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
- Leonard Sabetti & Ronald Heijmans, 2020. "Shallow or deep? Detecting anomalous flows in the Canadian Automated Clearing and Settlement System using an autoencoder," Working Papers 681, DNB.
- Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
- Parley Ruogu Yang, 2021. "Forecasting high-frequency financial time series: an adaptive learning approach with the order book data," Papers 2103.00264, arXiv.org.
- Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
- Bholat, David & Brookes, James & Cai, Chris & Grundy, Katy & Lund, Jakob, 2017. "Sending firm messages: text mining letters from PRA supervisors to banks and building societies they regulate," Bank of England working papers 688, Bank of England.
- David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.
- Andrea Carboni & Alessandro Moro, 2018. "Imputation techniques for the nationality of foreign shareholders in Italian firms," IFC Bulletins chapters, in: Bank for International Settlements (ed.), External sector statistics: current issues and new challenges, volume 48, Bank for International Settlements.
- Carlos Moreno Pérez & Marco Minozzo, 2022. "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers 2229, Banco de España.
- Andrew Clark, 2020. "A Pound Centric look at the Pound vs. Krona Exchange Rate Movement from 1844 to 1965," Economics Discussion Papers em-dp2020-22, Department of Economics, University of Reading.
- Ivan Baybuza, 2018. "Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 42-59, December.
- 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.
- 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).
- Vadim Grishchenko & Ivan Krylov, 2024. "New Approaches to Measuring, Analysing, and Forecasting Prices: A Review of the Bank of Russia, NES, and HSE University Workshop," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 92-111, June.
- Klieber, Karin, 2024. "Non-linear dimension reduction in factor-augmented vector autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 159(C).
- Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.
- Anil Savio Kavuri & Alistair Milne, 2019. "FinTech and the future of financial services: What are the research gaps?," CAMA Working Papers 2019-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Chakraborty, Chiranjit & Gimpelewicz, Mariana & Uluc, Arzu, 2017.
"A tiger by the tail: estimating the UK mortgage market vulnerabilities from loan-level data,"
Bank of England working papers
703, Bank of England.
Cited by:
- Cumming, Fergus, 2022.
"Mortgage cash-flows and employment,"
European Economic Review, Elsevier, vol. 144(C).
- Fergus Cumming, 2019. "Mortgage Cash-flows and Employment," Discussion Papers 1922, Centre for Macroeconomics (CFM).
- Cumming, Fergus, 2018. "Mortgages, cash-flow shocks and local employment," Bank of England working papers 773, Bank of England.
- Fergus Cumming & Lisa Dettling, 2024.
"Monetary Policy and Birth Rates: The Effect of Mortgage Rate Pass-Through on Fertility,"
The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(1), pages 229-258.
- Cumming, Fergus & Dettling, Lisa, 2019. "Monetary policy and birth rates: the effect of mortgage rate pass-through on fertility," Bank of England working papers 835, Bank of England.
- Fergus Cumming & Lisa J. Dettling, 2020. "Monetary Policy and Birth Rates: The Effect of Mortgage Rate Pass-Through on Fertility," Finance and Economics Discussion Series 2020-002, Board of Governors of the Federal Reserve System (U.S.).
- Levina, Iren & Sturrock, Robert & Varadi, Alexandra & Wallis, Gavin, 2019. "Modelling the distribution of mortgage debt," Bank of England working papers 808, Bank of England.
- Bracke, Philippe & Datta, Anupam & Jung, Carsten & Sen, Shayak, 2019. "Machine learning explainability in finance: an application to default risk analysis," Bank of England working papers 816, Bank of England.
- Jagjit S. Chadha & Richard Barwell, 2019. "Renewing our Monetary Vows: Open Letters to the Governor of the Bank of England," National Institute of Economic and Social Research (NIESR) Occasional Papers 58, National Institute of Economic and Social Research.
- Cumming, Fergus, 2022.
"Mortgage cash-flows and employment,"
European Economic Review, Elsevier, vol. 144(C).
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 2 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.- NEP-BAN: Banking (1) 2018-04-02. Author is listed
- NEP-BIG: Big Data (1) 2017-09-10. Author is listed
- NEP-CBA: Central Banking (1) 2017-09-10. Author is listed
- NEP-CMP: Computational Economics (1) 2017-09-10. Author is listed
- NEP-ECM: Econometrics (1) 2017-09-10. Author is listed
- NEP-MAC: Macroeconomics (1) 2017-09-10. Author is listed
- NEP-MON: Monetary Economics (1) 2017-09-10. Author is listed
- NEP-RMG: Risk Management (1) 2018-04-02. Author is listed
- NEP-URE: Urban and Real Estate Economics (1) 2018-04-02. Author is listed
Corrections
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