Nowcasting GDP and its Components in a Data-Rich Environment: The Merits of the Indirect Approach

116 Pages Posted: 4 Jun 2020 Last revised: 7 Aug 2020

See all articles by Alessandro Giovannelli

Alessandro Giovannelli

University of Rome Tor Vergata

Tommaso Proietti

University of Rome II - Department of Economics and Finance

Ambra Citton

Ministry of Economy and Finance, Italy

Ottavio Ricchi

Government of the Italian Republic (Italy) - Ministry of Economy and Finance - RGS

Cristian Tegami

Sogei S.p.A.

Cristina Tinti

Ministry of Economy and Finance, Italy; University of Rome Tor Vergata

Date Written: May 29, 2020

Abstract

The national accounts provide a coherent and exaustive description of the current state of the economy, but are available at the quarterly frequency and are released with a nonignorable publication lag. The paper proposes and illustrates a method for nowcasting and forecasting the sixteen main components of Gross Domestic Product (GDP) by output and expenditure type at the monthly frequency, using a high-dimensional set of monthly economic indicators spanning the space of the common macroeconomic and financial factors. The projection on the common space is carried out by combining the individual nowcasts and forecasts arising from all possible bivariate models of the unobserved monthly GDP component and the observed monthly indicator. We discuss several pooling strategies and we select the one showing the best predictive performance according to a pseudo real time forecasting experiment. Monthly GDP can be indirectly estimated by the contemporaneous aggregation of the value added of the different industries and of the expenditure components. This enables the comparative assessment of the indirect nowcasts and forecasts vis-à-vis the direct approach and a growth accounting exercise. Our approach meets the challenges posed by the dimensionality, since it can handle a large number of time series with a complexity that increases linearly with the cross-sectional dimension, while retaining the essential heterogeneity of the information about the macroeconomy. The application to the Italian case leads to several interesting discoveries concerning the time-varying predictive content of the information carried by the monthly indicators.

Keywords: Mixed-Frequency Data, Dynamic Factor Models, Growth Accounting, Model Averaging, Ledoit-Wolf Shrinkage.

JEL Classification: C32, C52, C53, E37

Suggested Citation

Giovannelli, Alessandro and Proietti, Tommaso and Citton, Ambra and Ricchi, Ottavio and Tegami, Cristian and Tinti, Cristina, Nowcasting GDP and its Components in a Data-Rich Environment: The Merits of the Indirect Approach (May 29, 2020). CEIS Working Paper No. 489, Available at SSRN: https://ssrn.com/abstract=3614110 or http://dx.doi.org/10.2139/ssrn.3614110

Alessandro Giovannelli

University of Rome Tor Vergata ( email )

Via di Tor Vergata
Rome, Lazio 00133
Italy

Tommaso Proietti (Contact Author)

University of Rome II - Department of Economics and Finance ( email )

Via Columbia, 2
Rome, 00133
Italy

Ambra Citton

Ministry of Economy and Finance, Italy ( email )

Via XX Settembre 97
Rome, Rome 00187
Italy

Ottavio Ricchi

Government of the Italian Republic (Italy) - Ministry of Economy and Finance - RGS ( email )

Via XX Settembre 97
Rome
Italy
39-06-47614774 (Phone)

Cristian Tegami

Sogei S.p.A. ( email )

Via Mario Carucci n. 99 e 85
Rome, 00143
Italy

Cristina Tinti

Ministry of Economy and Finance, Italy ( email )

Via XX Settembre 97
Rome, Rome 00187
Italy

University of Rome Tor Vergata ( email )

Via di Tor Vergata
Rome, Lazio 00133
Italy

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