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Estimation précoce de la croissance. De la régression LARS au modèle à facteurs

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  • Françoise Charpin

Abstract

In this paper, nowcasts are provided by a factor model, where factors are extracted from a small number of monthly series, selected using the LARS algorithm (Least Angle Regression). We follow the work of Bai and Ng (2008) which contrasts strongly with the traditional factor model based on a large information set. They recommend selecting only targeted predictors, i.e. the most informative series to forecast growth. A pseudo real time analysis is carried out to estimate French growth over the period 2001-2007. JEL Classification: C22, C53.

Suggested Citation

  • Françoise Charpin, 2009. "Estimation précoce de la croissance. De la régression LARS au modèle à facteurs," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(1), pages 31-48.
  • Handle: RePEc:cai:reofsp:reof_108_0031
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    Cited by:

    1. Françoise Charpin, 2011. "Réévaluation des modèles d’estimation précoce de la croissance," Sciences Po publications info:hdl:2441/eu4vqp9ompq, Sciences Po.

    More about this item

    Keywords

    forecasting; factor model; targeted predictors;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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