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Construction of coincident indicators for euro area key macroeconomic variables. 28th International Symposium on Forecasting, Nice, June 23 2008

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Listed:
  • Françoise Charpin

    (Observatoire français des conjonctures économiques)

  • Catherine Mathieu

    (Observatoire français des conjonctures économiques)

  • Gian Luigi Mazzi

    (Eurostat)

Abstract

The availability of timely and reliable information on main macroeconomic variables is considered both by policy makers and analysts crucial for an effective process of decision making. Unfortunately official statistics cannot always meet adequately users' needs, especially concerning their timely availability. This is the reason why, using econometric techniques, analysts try to anticipate or estimate in real time short-term movements of main macroeconomic variables. In this paper we propose a strategy simple and easily replicable in production processes for the estimation of the period on period growth rates of the euro area Industrial Production Index and Gross Domestic Product (GDP). Our strategy is based on the classical multivariate regression model on growth rates with autoregressive error term which is widely used in anticipating economic movements. Concerning GDP three different equations were identified, while for Industrial Production Index we have identified only two suitable representations. Furthermore for both variables we also use a purely autoregressive representation as a benchmark.

Suggested Citation

  • Françoise Charpin & Catherine Mathieu & Gian Luigi Mazzi, 2008. "Construction of coincident indicators for euro area key macroeconomic variables. 28th International Symposium on Forecasting, Nice, June 23 2008," Sciences Po publications info:hdl:2441/9676, Sciences Po.
  • Handle: RePEc:spo:wpmain:info:hdl:2441/9676
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    References listed on IDEAS

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    1. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    2. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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