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Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
When Can Trend-Cycle Decompositions Be Trusted?
In this paper, we examine the results of GDP trend-cycle decompositions from the estimation of bivariate unobserved components models that allow for correlated trend and cycle innovations. Three competing variables are considered in the bivariate setup along with GDP: the unemployment rate, the inflation rate, and gross domestic income. We find that the unemployment rate is the best variable to accompany GDP in the bivariate setup to obtain accurate estimates of its trend-cycle correlation coefficient and the cycle. We show that the key feature of unemployment that allows for precise estimates of the cycle of GDP is that its nonstationary component is "small" relative to its cyclical component. Using quarterly GDP and unemployment rate data from 1948:Q1 to 2015:Q4, we obtain the trend-cycle decomposition of GDP and find evidence of correlated trend and cycle components and an estimated cycle that is about 2 percent below its trend at the end of the sample.
Cite this item
Manuel Gonzalez-Astudillo & John M. Roberts, When Can Trend-Cycle Decompositions Be Trusted?, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2016-099, 19 Dec 2016.
- 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 ; Trend-cycle decomposition ; Trend-cycle correlation
This item with handle RePEc:fip:fedgfe:2016-99
is also listed on EconPapers
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