Clustered Panel Data Models: An Efficient Approach for Nowcasting from Poor Data

CORE Discussion Paper No. 2003/90

30 Pages Posted: 23 Apr 2007

See all articles by Michel Mouchart

Michel Mouchart

Catholic University of Louvain (UCL) - School of Statistics

J. V. K. Rombouts

HEC Montreal; Catholic University of Louvain (UCL) - Center for Operations Research and Econometrics (CORE); Centre interuniversitaire sur le risque, les politiques économiques et l'emploi (CIRPÉE); Center for Interuniversity Research and Analysis on Organization (CIRANO)

Date Written: December 2003

Abstract

Nowcasting regards the inference on the present realization of random variables, on the basis of information available until a recent past. This paper proposes a modelling strategy aimed at a best use of the data for nowcasting based on panel data with severe deficiencies, namely short times series and many missing data. The basic idea consists of introducing a clustering approach into the usual panel data model specification. A case study in the field of R&D variables illustrates the proposed modelling strategy.

Keywords: panel data, forecast, nowcast, missing data, clustering, R&D data

JEL Classification: C23, C51, C53

Suggested Citation

Mouchart, Michel and Rombouts, Jeroen, Clustered Panel Data Models: An Efficient Approach for Nowcasting from Poor Data (December 2003). CORE Discussion Paper No. 2003/90, Available at SSRN: https://ssrn.com/abstract=981400 or http://dx.doi.org/10.2139/ssrn.981400

Michel Mouchart (Contact Author)

Catholic University of Louvain (UCL) - School of Statistics ( email )

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Jeroen Rombouts

HEC Montreal ( email )

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Catholic University of Louvain (UCL) - Center for Operations Research and Econometrics (CORE) ( email )

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Belgium

Centre interuniversitaire sur le risque, les politiques économiques et l'emploi (CIRPÉE) ( email )

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Canada

Center for Interuniversity Research and Analysis on Organization (CIRANO) ( email )

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