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Toward a more reliable picture of the economic activity: An application to Argentina

Author

Listed:
  • Camacho, Maximo
  • Dal Bianco, Marcos
  • Martinez-Martin, Jaime

Abstract

We advocate a dynamic factor model to provide alternative measures of output data using indirect information from economic indicators. We apply the method to show evidence of a significant gap between estimated and official measures of Argentine GDP since 2007.

Suggested Citation

  • Camacho, Maximo & Dal Bianco, Marcos & Martinez-Martin, Jaime, 2015. "Toward a more reliable picture of the economic activity: An application to Argentina," Economics Letters, Elsevier, vol. 132(C), pages 129-132.
    • Handle: RePEc:eee:ecolet:v:132:y:2015:i:c:p:129-132
    DOI: 10.1016/j.econlet.2015.03.032
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    References listed on IDEAS

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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Rawski, Tom, 1993. "How fast has Chinese industry grown?," Policy Research Working Paper Series 1194, The World Bank.
    3. Tomasz Michalski & Gilles Stoltz, 2013. "Do Countries Falsify Economic Data Strategically? Some Evidence That They Might," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 591-616, May.
    4. Tomasz Kamil Michalski & Guillaume Stoltz, 2010. "Do countries falsify economic date strategically? Some evidence that they do," Working Papers hal-00540794, HAL.
    5. John G. Fernald & Israel Malkin & Mark M. Spiegel, 2013. "On the reliability of Chinese output figures," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue mar25.
    6. Ariel Coremberg, 2014. "Measuring Argentina's GDP Growth," World Economics, World Economics, 1 Ivory Square, Plantation Wharf, London, United Kingdom, SW11 3UE, vol. 15(1), pages 1-32, January.
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    Cited by:

    1. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    2. Luciano Campos & Danilo Leiva-León & Steven Zapata- Álvarez, 2022. "Latin American Falls, Rebounds and Tail Risks," Borradores de Economia 1201, Banco de la Republica de Colombia.
    3. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    4. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially-Biased Statistics: Household Inflation Perceptions and Expectations in Argentina," NBER Working Papers 22103, National Bureau of Economic Research, Inc.
    5. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.

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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • 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

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