Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP

43 Pages Posted: 10 Jun 2008

See all articles by Massimiliano Giuseppe Marcellino

Massimiliano Giuseppe Marcellino

Bocconi University - Department of Economics; Centre for Economic Policy Research (CEPR)

Christian Schumacher

Deutsche Bundesbank

Multiple version iconThere are 3 versions of this paper

Date Written: February 2008

Abstract

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the 'ragged edge' of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the 'nowcast', using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections with respect to nowcast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data.

Keywords: business cycle, large factor models, MIDAS, missing values, mixed-frequency data, nowcasting

JEL Classification: C53, E37

Suggested Citation

Marcellino, Massimiliano and Schumacher, Christian, Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP (February 2008). CEPR Discussion Paper No. DP6708, Available at SSRN: https://ssrn.com/abstract=1141614

Massimiliano Marcellino (Contact Author)

Bocconi University - Department of Economics ( email )

Via Gobbi 5
Milan, 20136
Italy

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

Christian Schumacher

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

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