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Aggregate Indicators of Economic Activity for Argentina: The Principal Components Method

Author

Listed:
  • Pedro Elosegui

    (Central Bank of Argentina)

  • Lorena Garegnani

    (Central Bank of Argentina)

  • Emilio Blanco

    (Central Bank of Argentina)

Abstract
The principal components methodology allows us to summarize the relevant information of a series of economic indicators. In this paper, this methodology is used on series commonly used by the BCRA to follow up aggregate demand and supply conditions. The principal components are assessed in terms of their correlation with the economic activity level and the price index evolution. In general, they have been found to be leading indicators of the business cycle (2 to 6 months) and significant predictors of the inflation rate. In particular, the performance of a demand indicator based on real time data (information available with a quarter lag) is especially outlined.

Suggested Citation

  • Pedro Elosegui & Lorena Garegnani & Emilio Blanco, 2008. "Aggregate Indicators of Economic Activity for Argentina: The Principal Components Method," BCRA Working Paper Series 200832, Central Bank of Argentina, Economic Research Department.
    • Handle: RePEc:bcr:wpaper:200832
    as

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    File URL: http://www.bcra.gov.ar/pdfs/investigaciones/WP%202008%2032.pdf
    File Function: Spanish version (versión en Español)
    Download Restriction: no
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    More about this item

    Keywords

    Argentina; output gap; Phillips Curve; principal components;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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