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Are ATM/POS Data Relevant When Nowcasting Private Consumption?

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  • Paulo Esteves

Abstract

Policymakers need timely and reliable information on the current state of the economy as macroeconomic forecasts and policy decisions are strongly affected by the quality and completeness of this assessment. Therefore, forecasters are always in search of new indicators that are related with the macroeconomic variable of interest and available earlier. This paper proposes the use of the ATM/POS data as an indicator to estimate private consumption. An application for Portugal is presented as a case study, where the out of sample performance of this indicator is evaluated against some benchmark naïve models and other alternative bridge models. The results clearly support the use of this information to nowcast non durables private consumption.

Suggested Citation

  • Paulo Esteves, 2009. "Are ATM/POS Data Relevant When Nowcasting Private Consumption?," Working Papers w200925, Banco de Portugal, Economics and Research Department.
  • Handle: RePEc:ptu:wpaper:w200925
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    File URL: https://www.bportugal.pt/sites/default/files/anexos/papers/wp200925.pdf
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    References listed on IDEAS

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    1. Esteves, Rui Pedro & Reis, Jaime & Ferramosca, Fabiano, 2009. "Market Integration in the Golden Periphery. The Lisbon/London Exchange, 1854-1891," Explorations in Economic History, Elsevier, vol. 46(3), pages 324-345, July.
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    Cited by:

    1. Henryk Gurgul & Marcin Suder, 2013. "The Properties of ATMs Development Stages - an Empirical Analysis," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 443-466, September.
    2. repec:exl:29stat:v:17:y:2016:i:4:p:691-722 is not listed on IDEAS
    3. 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.
    4. 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.
    5. António Rua & Cláudia Duarte & Paulo M.M. Rodrigues, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
    6. Henryk Gurgul & Marcin Suder, 2013. "Modeling of withdrawals from selected ATMs of the "Euronet" network," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 13, pages 65-82.
    7. Filipa Lima, 2014. "The use of payments data to improve monetary and financial analysis," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Proceedings of the Porto Workshop on "Integrated management of micro-databases", volume 37, pages 111-114 Bank for International Settlements.
    8. 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.

    More about this item

    JEL classification:

    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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