Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
GDP Trend-cycle Decompositions Using State-level Data
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.
Cite this item
Manuel Gonzalez-Astudillo, GDP Trend-cycle Decompositions Using State-level Data, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2017-051, May 2017.
- 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
Keywords: Unobserved components model ; State-level GDP data ; Trend-cycle decomposition
This item with handle RePEc:fip:fedgfe:2017-51
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