Using the payment system data to forecast the Italian GDP
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Cited by:
- Diego Bodas & Juan R. García López & Tomasa Rodrigo López & Pep Ruiz de Aguirre & Camilo A. Ulloa & Juan Murillo Arias & Juan de Dios Romero Palop & Heribert Valero Lapaz & Matías J. Pacce, 2019. "Measuring retail trade using card transactional data," Working Papers 1921, Banco de España.
- 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.
- Concetta Rondinelli & Roberta Zizza, 2020. "Spend today or spend tomorrow? The role of inflation expectations in consumer behaviour," Temi di discussione (Economic working papers) 1276, Bank of Italy, Economic Research and International Relations Area.
- Guerino Ardizzi & Andrea Nobili & Giorgia Rocco, 2020. "A game changer in payment habits: evidence from daily data during a pandemic," Questioni di Economia e Finanza (Occasional Papers) 591, Bank of Italy, Economic Research and International Relations Area.
- Natalia Turdyeva & Anna Tsvetkova & Levon Movsesyan & Alexey Porshakov & Dmitriy Chernyadyev, 2021. "Data of Sectoral Financial Flows as a High-Frequency Indicator of Economic Activity," Russian Journal of Money and Finance, Bank of Russia, vol. 80(2), pages 28-49, June.
- Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2021. "Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey," Papers 2107.03299, arXiv.org.
- María Gil & Javier J. Pérez & Alberto Urtasun, 2019.
"Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data,"
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.
- María Gil & Javier J. Pérez & A. Jesús Sánchez & Alberto Urtasun, 2018. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," Working Papers 1842, Banco de España.
- Tut, Daniel, 2023.
"FinTech and the COVID-19 pandemic: Evidence from electronic payment systems,"
Emerging Markets Review, Elsevier, vol. 54(C).
- Tut, Daniel, 2020. "FinTech and the COVID-19 Pandemic: Evidence from Electronic Payment Systems," MPRA Paper 102401, University Library of Munich, Germany.
- Guerino Ardizzi & Elisa Bonifacio & Cristina Demma & Laura Painelli, 2020. "Regional Differences in Retail Payment Habits in Italy," Questioni di Economia e Finanza (Occasional Papers) 576, Bank of Italy, Economic Research and International Relations Area.
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More about this item
Keywords
short term forecasting; LASSO; mixed frequency models; Kalman smoothing; payment systems; TARGET2;All these keywords.
JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
NEP fields
This paper has been announced in the following NEP Reports:- NEP-EEC-2017-03-05 (European Economics)
- NEP-FOR-2017-03-05 (Forecasting)
- NEP-MAC-2017-03-05 (Macroeconomics)
- NEP-PAY-2017-03-05 (Payment Systems and Financial Technology)
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