Estimating and Forecasting the Euro Area Monthly National Accounts from a Dynamic Factor Model

By E. Angelini, Marta Bańbura, Gerhard Rünstler. Social Science Research Network, 2010

119 citations recorded by the source corpus.

We estimate and forecast growth in euro area monthly GDP and its components from the dynamic factor model of Doz et al. (2006), which handles unbalanced data sets in an efficient way. We extend the model to integrate interpolation and forecasting with cross-equation accounting identities. A pseudo real-time forecasting exercise indicates that the model outperforms various benchmarks, such as quarterly time-series models and bridge equations, in forecasting growth in quarterly GDP and its components.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/5c3c4ab4e3355dd9f2ae8d82c2905953488c8b6d

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In Economics and Econometrics

1. Haoyang Liu
2. Debashis Paul
3. Alexander Aue
4. Lajos Horv'ath
5. Gregory Rice

Citations in the corpus

• On the Marčenko-Pastur law for linear time series (2013, methodology): “DFMs have, for example, been used for describing the stock returns in [28], forecasting national accounts in [3], modeling portfolio allocation in [1] and modeling psychological development in [27], as well as in many other applications.”
• Now-Casting and the Real-time Data Flow (2013, unknown): “The coefficients of the transition equation are time-varying: I2r 0 0 0 0 W kq ,f t 0 I2r 0 0 0 Wkm,ft 0 0 Ir 0 0 Wkm,st 0 0 0 I2r 0 Wkw,ft 0 0 0 0 Ir Wkw,st 0 0 0 0 0 Ir F̃ kq ,f t F̃ km,ft F km,st F̃ kw,ft F kw,st Ft =”