Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Thibaut Duprey, 2020.
"Canadian Financial Stress and Macroeconomic Condition,"
Canadian Public Policy, University of Toronto Press, vol. 46(S3), pages 236-260, October.
- Thibaut Duprey, 2020. "Canadian Financial Stress and Macroeconomic Conditions," Discussion Papers 2020-4, Bank of Canada.
- Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
- Warwick McKibbin & Roshen Fernando, 2021.
"The Global Macroeconomic Impacts of COVID-19: Seven Scenarios,"
Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
- Warwick McKibbin & Roshen Fernando, 2020. "The global macroeconomic impacts of COVID-19: Seven scenarios," CAMA Working Papers 2020-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008.
"Nowcasting: The real-time informational content of macroeconomic data,"
Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
- Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
- Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
- Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
- Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2019. "Using Payment System Data to Forecast Economic Activity," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 55-80, October.
- Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
- Barnett, William & Chauvet, Marcelle & Leiva-Leon, Danilo & Su, Liting, 2016. "Nowcasting Nominal GDP with the Credit-Card Augmented Divisia Monetary," Studies in Applied Economics 59, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
- Galbraith, John W. & Tkacz, Greg, 2018. "Nowcasting with payments system data," International Journal of Forecasting, Elsevier, vol. 34(2), pages 366-376.
- George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
- Tony Chernis & Rodrigo Sekkel, 2017.
"A dynamic factor model for nowcasting Canadian GDP growth,"
Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
- Tony Chernis & Rodrigo Sekkel, 2017. "A Dynamic Factor Model for Nowcasting Canadian GDP Growth," Staff Working Papers 17-2, Bank of Canada.
- Gary Koop & Luca Onorante, 2019. "Macroeconomic Nowcasting Using Google Probabilities☆," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 17-40, Emerald Group Publishing Limited.
- Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018.
"Macroeconomic Nowcasting and Forecasting with Big Data,"
Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
- Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2017. "Macroeconomic nowcasting and forecasting with big data," Staff Reports 830, Federal Reserve Bank of New York.
- Giannone, Domenico & Tambalotti, Andrea & Sbordone, Argia & Bok, Brandyn & Caratelli, Daniele, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," CEPR Discussion Papers 12589, C.E.P.R. Discussion Papers.
- 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.
- 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.
- Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013.
"Should Macroeconomic Forecasters Use Daily Financial Data and How?,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
- Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
- Eric Ghysels & Andros Kourtellos & Elena Andreou, 2012. "Should macroeconomic forecasters use daily financial data and how?," 2012 Meeting Papers 1196, Society for Economic Dynamics.
- Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Working Paper series 42_10, Rimini Centre for Economic Analysis.
- David Bounie & Youssouf Camara & John Galbraith, 2020.
"Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data,"
Cahiers de recherche
14-2020, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- David Bounie & Youssouf Camara & John W. Galbraith, 2020. "Consumers’ Mobility, Expenditure and Online-Offline Substitution Response to COVID-19: Evidence from French Transaction Data," CIRANO Working Papers 2020s-28, CIRANO.
- David Bounie & Youssouf Camara & John Galbraith, 2020. "Consumers' Mobility, Expenditure and Online- Offline Substitution Response to COVID-19: Evidence from French Transaction Data," Working Papers hal-02566443, HAL.
- Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
- Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
- 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.
- 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.
- John Galbraith & Greg Tkacz, 2007.
"Electronic Transactions as High-Frequency Indicators of Economic Activity,"
Staff Working Papers
07-58, Bank of Canada.
- John Galbraith & Greg Tkacz, 2008. "Electronic Transactions As High-Frequency Indicators Of Economics Activity," Departmental Working Papers 2008-04, McGill University, Department of Economics.
- Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
- Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020.
"A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth,"
International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
- Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
- Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
- Roy Verbaan & Wilko Bolt & Carin van der Cruijsen, 2017. "Using debit card payments data for nowcasting Dutch household consumption," DNB Working Papers 571, Netherlands Central Bank, Research Department.
- Christopher Henry & Kim Huynh & Angelika Welte, 2018. "2017 Methods-of-Payment Survey Report," Discussion Papers 18-17, Bank of Canada.
- Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005.
"Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases,"
CEPR Discussion Papers
5178, C.E.P.R. Discussion Papers.
- Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 633, European Central Bank.
- Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
- Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
- Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
- Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
- Dave Donaldson & Adam Storeygard, 2016. "The View from Above: Applications of Satellite Data in Economics," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 171-198, Fall.
Citations
RePEc Biblio mentions
As found on the RePEc Biblio, the curated bibliography for Economics:Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- 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.
- 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.
- James T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.
- 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.
- Tatjana Dahlhaus & Angelika Welte, 2024.
"Payment habits during Covid-19: Evidence from high-frequency transaction data,"
IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61,
Bank for International Settlements.
- Tatjana Dahlhaus & Angelika Welte, 2021. "Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data," Staff Working Papers 21-43, Bank of Canada.
- 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.
- 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).
- 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.
- Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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 T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.
- James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
- Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
- Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
- Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
- Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
- Baumeister, Christiane & Guérin, Pierre, 2021.
"A comparison of monthly global indicators for forecasting growth,"
International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
- Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," NBER Working Papers 28014, National Bureau of Economic Research, Inc.
- Baumeister, Christiane & Guerin, Pierre, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CEPR Discussion Papers 15403, C.E.P.R. Discussion Papers.
- Christiane Baumeister & Pierre Guérin, 2020. "A comparison of monthly global indicators for forecasting growth," CAMA Working Papers 2020-93, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
- Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022.
"Machine Learning Time Series Regressions With an Application to Nowcasting,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Time Series Regressions with an Application to Nowcasting," Papers 2005.14057, arXiv.org, revised Dec 2020.
- Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Reprints LFIN 2021010, Université catholique de Louvain, Louvain Finance (LFIN).
- Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Discussion Papers LFIN 2021004, Université catholique de Louvain, Louvain Finance (LFIN).
- 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.
- 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.
- Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2024.
"Lessons from nowcasting GDP across the world,"
Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 8, pages 187-217,
Edward Elgar Publishing.
- Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023. "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers 1385, Board of Governors of the Federal Reserve System (U.S.).
- Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
- Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020.
"A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth,"
International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
- Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
- Juan Tenorio & Wilder Perez, 2024. "Monthly GDP nowcasting with Machine Learning and Unstructured Data," Papers 2402.04165, arXiv.org.
- Sarun Kamolthip, 2021.
"Macroeconomic Forecasting with LSTM and Mixed Frequency Time Series Data,"
PIER Discussion Papers
165, Puey Ungphakorn Institute for Economic Research.
- Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
- Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
- Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023. "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 213-234, March.
- Philip ME Garboden, 2019. "Sources and Types of Big Data for Macroeconomic Forecasting," Working Papers 2019-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
- Boriss Siliverstovs, 2017.
"Short-term forecasting with mixed-frequency data: a MIDASSO approach,"
Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
- Boriss Siliverstovs, 2015. "Short-term forecasting with mixed-frequency data: A MIDASSO approach," KOF Working papers 15-375, KOF Swiss Economic Institute, ETH Zurich.
- Benedikt Maas, 2020.
"Short‐term forecasting of the US unemployment rate,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.
- Maas, Benedikt, 2019. "Short-term forecasting of the US unemployment rate," MPRA Paper 94066, University Library of Munich, Germany.
- Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2023.
"Testing big data in a big crisis: Nowcasting under Covid-19,"
International Journal of Forecasting, Elsevier, vol. 39(4), pages 1548-1563.
- Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2022. "Testing big data in a big crisis: Nowcasting under COVID-19," JRC Working Papers in Economics and Finance 2022-06, Joint Research Centre, European Commission.
More about this item
Keywords
Econometric and statistical methods; Payment clearing and settlement systems;JEL classification:
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2021-02-01 (Big Data)
- NEP-CMP-2021-02-01 (Computational Economics)
- NEP-MAC-2021-02-01 (Macroeconomics)
- NEP-PAY-2021-02-01 (Payment Systems and Financial Technology)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bca:bocawp:21-2. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bocgvca.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.