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Forecasting economic growth in the euro area during the Great Moderation and the Great Recession

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  • Lombardi, Marco J.
  • Maier, Philipp

Abstract

We evaluate forecasts for the euro area in data-rich and ‘data-lean’ environments by comparing three different approaches: a simple PMI model based on Purchasing Managers’ Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data from national economies (pseudo-real time data). We estimate backcasts, nowcasts and forecasts for GDP, components of GDP, and GDP of all individual euro area members, and examine forecasts for periods of low and high economic volatility (more specifically, we consider 2002-2007, which falls into the ‘Great Moderation’, and the ‘Great Recession’ 2008-2009). We find that all models consistently beat naive AR benchmarks, and overall, the dynamic factor model tends to outperform the PMI model (at times by a wide margin). However, accuracy of the dynamic factor model can be uneven (forecasts for some countries have large errors), with the PMI model dominating clearly for some countries or over some horizons. This is particularly pronounced over the Great Recession, where the dynamic factor model dominates the PMI model for backcasts, but has considerable difficulties beating the PMI model for nowcasts. This suggests that survey-based measures can have considerable advantages in responding to changes during very volatile periods, whereas factor models tend to be more sluggish to adjust. JEL Classification: C50, C53, E37, E47

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 1379.

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Date of creation: Sep 2011
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Handle: RePEc:ecb:ecbwps:20111379

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Keywords: dynamic factor model; forecasting; PMI model;

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Cited by:
  1. Stratford, Kate, 2013. "Nowcasting world GDP and trade using global indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 53(3), pages 233-242.
  2. Daragh Clancy, 2013. "Output Gap Estimation Uncertainty: Extracting the TFP Cycle Using an Aggregated PMI Series," The Economic and Social Review, Economic and Social Studies, vol. 44(1), pages 1-18.
  3. Keeney, Mary & Kennedy, Bernard & Liebermann, Joelle, 2012. "The value of hard and soft data for short-term forecasting of GDP," Economic Letters 11/EL/12, Central Bank of Ireland.
  4. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
  5. Ergun Ermisoglu & Yasin Akcelik & Arif Oduncu, 2013. "GDP Growth and Credit Data," Working Papers 1327, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

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