Sparse trend estimation - Richard K. Crump; Nikolaj Gospodinov; Hunter Wieman (Federal Reserve Bank of New York, 2023)
The low-frequency movements of many 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 some 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 slab-and-spike prior accounts explicitly for cyclical dynamics. The practical implementation of the method is discussed in detail, and we show that it performs well in simulations against some relevant benchmarks. We report empirical estimates of trend growth for U.S. output (and its components), productivity, and annual mean temperature. These estimates allow policymakers to assess shortfalls and overshoots in these variables from their economic and ecological targets.
Keywords: C13; C30; C33; E27; E32; 330; slow-moving trends; sparsity; Bayesian inference; latent variable models; trend output growth