Denoised Least Squares Forecasting of GDP Changes Using Indexes of Consumer and Business Sentiment
AbstractIndexes 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.
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Bibliographic InfoPaper provided by Central Bank of Cyprus in its series Working Papers with number 2010-9.
Length: 15 pages
Date of creation: Oct 2010
Date of revision:
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Web page: http://www.centralbank.gov.cy/nqcontent.cfm?a_id=1
More information through EDIRC
Consumer sentiment index; denoised least squares; index of homebuilders’sentiment; index of manufacturing activity; measurement errors.;
Find related papers by JEL classification:
- C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
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- Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
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