Lean' versus 'rich' data sets: Forecasting during the great moderation and the great recession - Marco J. Lombardi; Philipp Maier (Bank of Canada, 2010)
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 the 'Great Moderation' (2000-2007) and the 'Great Recession' (2008-2009) separately. All models consistently beat naive AR benchmarks. More data does not necessarily improve forecasting accuracy: For the factor model, adding monthly indicators from national economies can lead to more uneven forecasting accuracy, notably when forecasting components of euro area GDP during the Great Recession. This suggests that the merits of national data may reside in better estimation of heterogeneity across GDP components, rather than in improving headline GDP forecasts for individual euro area countries. Comparing factor models to the much simpler PMI model, we find that the dynamic factor model dominates the latter during the Great Moderation. However, during the Great Recession, the PMI model has the advantage that survey-based measures respond faster to changes in the outlook, whereas factor models are more sluggish in adjusting. Consequently, the dynamic factor model has relatively more difficulties beating the PMI model, with relatively large errors in forecasting some countries or components of euro area GDP.
Keywords: C50; C53; E37; E47; 330; Econometric and statistical methods; International topics; Prognose; Prognoseverfahren; Sozialprodukt; Eurozone