Denoised least squars forecasting of GDP changes using indexes of consumer and business sentiment
In: Proceedings of the IFC Conference on "Initiatives to address data gaps revealed by the financial crisis", Basel, 25-26 August 2010
Indexes of consumer and business sentiment are frequently characterized by measurement errors and short-term cyclical fluctuations that can distort their predictive accuracy for GDP changes. While measurement errors arise due to the survey sampling procedures that characterize these surveys, short-term cyclical fluctuations are generally linked with various exogenous and irregular factors that are not necessarily related to the economy. This paper shows, using data on the US economy, that applying wavelet denoising on indexes of consumer and business sentiment in the context of the linear regression model can overcome these limitations and can provide: (a) efficient coefficient estimates in models that explain consumer sentiment index variation; and (b) consistent coefficient estimates and predictions in models for GDP changes when using consumer and business sentiment indexes as predictors.
(This abstract was borrowed from another version of this item.)
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