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GDP Trend-cycle Decompositions Using State-level Data

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Abstract

This paper develops a method for decomposing GDP into trend and cycle exploiting the cross-sectional variation of state-level real GDP and unemployment rate data. The model assumes that there are common output and unemployment rate trend and cycle components, and that each state?s output and unemployment rate are subject to idiosyncratic trend and cycle perturbations. The model is estimated with Bayesian methods using quarterly data from 2005:Q1 to 2016:Q1 for the 50 states and the District of Columbia. Results show that the U.S. output gap reached about -8% during the Great Recession and is about 0.6% in 2016:Q1.

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  • Manuel Gonzalez-Astudillo, 2017. "GDP Trend-cycle Decompositions Using State-level Data," Finance and Economics Discussion Series 2017-051, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2017-51
    DOI: 10.17016/FEDS.2017.051
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    Cited by:

    1. Travis J. Berge & Damjan Pfajfar, 2019. "Duration Dependence, Monetary Policy Asymmetries, and the Business Cycle," Finance and Economics Discussion Series 2019-020, Board of Governors of the Federal Reserve System (U.S.).
    2. González-Astudillo, Manuel, 2019. "An output gap measure for the euro area: Exploiting country-level and cross-sectional data heterogeneity," European Economic Review, Elsevier, vol. 120(C).

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    More about this item

    Keywords

    unobserved component model; State-level GDP data; Trend-cycle decomposition;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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