A Simple Multivariate Filter for Estimating Potential Output
Patrick Blagrave, Roberto Garcia-Saltos, Douglas Laxton, Fan Zhang
Abstract
Patrick Blagrave, Roberto Garcia-Saltos, Douglas Laxton, Fan Zhang
Abstract
Estimates of potential output are an important ingredient of structured forecasting and policy analysis. Using information on consensus forecasts, this paper extends the multivariate filter developed by Benes and others (2010). Although the estimates in real time are more robust relative to those of naïve statistical filters, there is still significant uncertainty surrounding the estimates. The paper presents estimates for 16 countries and provides an example of how the filtered estimates at the end of the sample period can be improved with additional information.
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Estimates of potential output are an important ingredient of structured forecasting and policy analysis. Using information on consensus forecasts, this paper extends the multivariate filter developed by Benes and others (2010). Although the estimates in real time are more robust relative to those of naïve statistical filters, there is still significant uncertainty surrounding the estimates. The paper presents estimates for 16 countries and provides an example of how the filtered estimates at the end of the sample period can be improved with additional information.
Key concepts: Multivariate statistics, Filter (signal processing), Econometrics, Simple (philosophy), Computer science, Hodrick–Prescott filter, Statistics, Sample (material)