IMF Working Papers

Multivariate Filter Estimation of Potential Output for the United States

By Ali Alichi, Olivier Bizimana, Douglas Laxton, Kadir Tanyeri, Hou Wang, Jiaxiong Yao, Fan Zhang

May 4, 2017

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Ali Alichi, Olivier Bizimana, Douglas Laxton, Kadir Tanyeri, Hou Wang, Jiaxiong Yao, and Fan Zhang. Multivariate Filter Estimation of Potential Output for the United States, (USA: International Monetary Fund, 2017) accessed November 23, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Capacity utilization, Financial crises, Global financial crisis of 2008-2009, Inflation, Output gap, Potential output, Prices, Production

Keywords: Capacity utilization, Coherence of output-gap estimate, Estimates of the output gap, Estimation result, Financial crisis, Global, Global financial crisis of 2008-2009, Inflation, Inflation expectation, Macroeconomic Modeling, Output gap, Output gap decomposition, Output gap estimate, Output tradeoff sense, Phillips curve, Potential Output, Potential output gap, Real-time estimate, WP

Publication Details

  • Pages:

    25

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2017/106

  • Stock No:

    WPIEA2017106

  • ISBN:

    9781475598384

  • ISSN:

    1018-5941

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