Sparse Trend Estimation

RePEc Short ID: pcr107
ORCID iD: 0000-0002-7958-1818

Crump, Richard K.; Gospodinov, Nikolay; Wieman, Hunter

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Sparse Trend Estimation

Abstract: The low-frequency movements of economic variables play a prominent role in policy analysis and decision-making. We develop a robust estimation approach for these slow-moving trend processes that is guided by a judicious choice of priors and characterized by sparsity. We present novel stylized facts from longer-run survey expectations that inform the structure of the estimation procedure. The general version of the proposed Bayesian estimator with a spike-and-slab prior accounts explicitly for cyclical dynamics. We show that it performs well in simulations against relevant benchmarks and report empirical estimates of trend growth for U.S. output and annual mean temperature.

JEL Classification: C13; C30; C33; E27; E32;