Forecasting GDP: Do Revisions Matter?

Check, Adam J. and Nolan, Anna K. and Schipper, Tyler C. (2018): Forecasting GDP: Do Revisions Matter?

Abstract

This paper investigates the informational content of regular revisions to real GDP growth and its components. We perform a real-time forecasting exercise for the advance estimate of real GDP growth using dynamic regression models that include GDP and GDP component revisions. Echoing other work in the literature, we find little evidence that including aggregate GDP growth revisions improves forecast accuracy relative to an AR(1) baseline model; however, when we include revisions to components of GDP (i.e. C, I, G, X, and M) we find improvements in forecast accuracy. Overall, nearly 68\% of all models that contain subsets of component revisions outperform our baseline model. The "best" component-augmented model forecasts roughly 0.2 percentage points better, and a large subset of models improve RMSFE by more than 5%. Finally, we use Bayesian model comparison to demonstrate that differences in forecast performance are unlikely to be the result of statistical noise. Our results imply that component revisions, in particular to consumption, contain important information for forecasting GDP growth.

• Item Type: MPRA Paper
• Language: English
• Keywords: Data revisions, real-time data, forecasting, GDP
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C82 - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data ; Data Access; E - Macroeconomics and Monetary Economics > E0 - General > E01 - Measurement and Data on National Income and Product Accounts and Wealth ; Environmental Accounts

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