Multivariate Filter Estimation Of Potential Output For The United States
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Multivariate Filter Estimation of Potential Output for the United States
Author: Ali Alichi
language: en
Publisher: International Monetary Fund
Release Date: 2017-05-04
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.
Multivariate Filter Estimation of Potential Output for the Euro Area and the United States
Author: Ali Alichi
language: en
Publisher: International Monetary Fund
Release Date: 2015-12-01
The estimates of potential output and the output gap presented in this paper are not official IMF estimates. The programs and potential output estimates in this paper can be downloaded from www.douglaslaxton.org.The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. The authors would like to thank the European Department of the IMF for helpful comments. All errors and omissions are our own.
Multivariate Filter Estimation of Potential Output for the United States
Author: Ali Alichi
language: en
Publisher: International Monetary Fund
Release Date: 2017-05-04
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.