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

Alessandro Giovannelli, Tommaso Proietti, Ambra Citton (), Ottavio Ricchi (), Cristian Tegami () and Cristina Tinti ()
Additional contact information
Ambra Citton: Ministero dell'Economia e delle Finanze
Ottavio Ricchi: Ministero dell'Economia e delle Finanze
Cristian Tegami: Sogei SpA
Cristina Tinti: Ministero dell'Economia e delle Finanze

No 489, CEIS Research Paper from Tor Vergata University, CEIS

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. (search for similar items in EconPapers)
JEL-codes: C32 C52 C53 E37 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2020-05-30, Revised 2020-05-30
New Economics Papers: this item is included in nep-eec, nep-ets, nep-for and nep-mac
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Journal Article: Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach (2021)
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