Multivariate Filter Estimation of Potential Output for the United States / by Ali Alichi [and six others].

Print version record.

Cover; Table of Contents; Abstract; I. Introduction; II. Potential Output-A Brief Overview of Common Estimation Techniques; III. Methodology; IV. Estimating the Output Gap For the United States; V. Uncertainty in Estimating the Output Gap and Potential; VI. Conclusion; References; Figures; Figure 1. Shocks to the Level and Growth Rate of Potential Output, and to the Output Gap; Figure 2. Output Gap Decomposition; Figure 3. Output gap, Unemployment Gap, Capacity Utilization Gap, and Inflation; Figure 4. 95-Percent Confidence Bands for Estimates of Potential Growth.

Figure 5. 95-Percent Confidence Bands for Estimates of Output GapFigure 6. Potential Growth Estimates as the Sample is Extended; Tables; Table 1. Comparison of Results; Table 2. Standard Deviation of Supply and Demand Shocks; Appendix Tables; Table A1. Data Sources; Table A2. Estimated Parameters; Table A3. Calibrated Parameters.

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 naive 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.

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