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Ali Alichi, Olivier Bizimana, Mr. Douglas Laxton, Kadir Tanyeri, Hou Wang, Jiaxiong Yao, and Fan Zhang
Estimates of potential output are an important component of a structured forecasting and policy analysis system. Using information on capacity utilization, this paper extends the multivariate filter developed by Laxton and Tetlow (1992) and modified by Benes and others (2010), Blagrave and others (2015), and Alichi and others (2015). We show that, although still fairly uncertain, the real-time estimates from this approach are more accurate than estimates constructed from naïve univariate statistical filters. The paper presents illustrative estimates for the United States and discusses how the end-of-sample estimates can be improved with additional information.
Patrick Blagrave, Mr. Roberto Garcia-Saltos, Mr. Douglas Laxton, and Fan Zhang

. Second, the filter includes some simple economic theory—specifically the structure of the filter relates the output gap to slack in the labor market and changes in inflation. Third, the filter produces more robust real-time estimates of potential and the output gap relative to estimates from an HP filter, though a certain amount of uncertainty in real-time estimates is unavoidable. Fourth, due to the minimal data requirements (GDP, inflation, and unemployment), the filter can be applied to a broad range of countries. Finally, the results can be conditioned in a