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Sparse Trend Estimation

Richard K. Crump¹; Nikolay Gospodinov²; Hunter Wieman³

¹ Federal Reserve Bank of New York, 33 Liberty Street, New York, NY 10045 [email protected] · ² Federal Reserve Bank of Atlanta, 1000 Peachtree Street, N.E., Atlanta, GA 30309 [email protected] · ³ Princeton University, Julis Romo Rabinowitz Building Princeton, NJ 08544 [email protected]

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 which is guided by a judicious choice of priors and is 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.

DOI

10.1162/rest_a_01506

Pages

1-45

Language

en

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