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Aggregate Indicators of Economic Activity for the Argentine Case: The Principal Components Methodology

Author

Listed:
  • Pedro Elosegui

    (Central Bank of Argentina)

  • Lorena Garegnani

    () (Central Bank of Argentina)

  • Luis Lanteri

    () (Central Bank of Argentina)

  • Emilio Blanco

    () (Central Bank of Argentina)

Abstract

In order to comply with their main objective of price stability, monetary authorities rely on analytic tools to properly assess tendencies and inflationary pressures in the economy. Therefore Central Banks are interested in analyzing and monitoring changes in cyclical fluctuations of economic variables that may potentially result in an acceleration of inflation. Indeed, they use different methodologies such as estimations of non-inflationary potential output and the output gap in order to understand prices and wages dynamics. An alternative to such variables is the consideration of different indicators that anticipate inflationary pressures. The relevant information available increases with the number of variables included in the analysis, making it more difficult in practice. The Principal Components methodology partially resolves this problem, since it simplifies and consolidates relevant information extracted from a significant number of series. This methodology resumes information in a few autonomous components which also explain a higher proportion of the common variance and covariance of the series. Therefore Principal Components Analysis is a very useful tool for business cycle analysis. This paper shows an application to the argentine case, using a group of synthetic indicators that resume the information coming from a considerable number of quarterly economic series and indexes. These series can be grouped in aggregate demand or supply data, and in more detail: (a) output, activity and sectoral indicators, (b) industrial output, industrial survey and installed capacity, (c) consumption and investment, survey of perspectives and tendency of the demand and (d) international trade and others. The activity indicators resulting from the application of the Principal Components methodology are evaluated in terms of their usefulness as leading indicators of the business cycle and in relation to its forecast performance on the evolution of the inflation index. A synthetic indicator build up from series available in real time (with about a quarter lag) deserves particular interest, and may contribute to a more rigorous and regular monitoring of the economy by the monetary authority. A detailed analysis of cointegration, following the methodology of cointegration systems Johansen (1988) and Johansen and Juselius (1990) is performed to study the relationship between real GDP and the principal Component Indicator obtained. The procedure followed permits to determine whether there is a co-integration relationship and, at the same time, carry out a weak exogeneity analysis to validate the conditional model that corroborates a consistent and unidirectional relationship between the indicator constructed and real GDP. Results show that principal components indicators can be use to detect the presence of inflationary pressures in the economy, complementing the information obtained by other techniques and methodologies based on non-observable components. In this regard, an interesting result from this study is the possibility of generating a synthetic real time series that constitutes a leading and consistent indicator of the business cycle.

Suggested Citation

  • Pedro Elosegui & Lorena Garegnani & Luis Lanteri & Emilio Blanco, 2008. "Aggregate Indicators of Economic Activity for the Argentine Case: The Principal Components Methodology," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(51), pages 7-41, April - S.
  • Handle: RePEc:bcr:ensayo:v:1:y:2008:i:51:p:7-41
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    More about this item

    Keywords

    output gap; Phillips curve; principal components;

    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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