Sparse Trend Estimation

Richard Crump, Nikolay Gospodinov and Hunter Wieman ()

No 1049, Staff Reports from Federal Reserve Bank of New York

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.

Keywords: sparsity; Bayesian inference; latent variable models; trend output growth; slow-moving trends (search for similar items in EconPapers)
JEL-codes: C13 C30 C33 E27 E32 (search for similar items in EconPapers)
Pages: 61
Date: 2023-02-01
New Economics Papers: this item is included in nep-ecm and nep-ets
Note: Revised March 2024.
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