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This paper develops a method for decomposing GDP into trend and cycle exploiting the cross-sectional variation of state-level real GDP and unemployment rate data. The model assumes that there are common output and unemployment rate trend and cycle components, and that each state's output and unemployment rate are subject to idiosyncratic trend and cycle perturbations. The model is estimated with Bayesian methods using quarterly data from 2005:Q1 to 2016:Q1 for the 50 states and the District of Columbia. Results show that the U.S. output gap reached about -8% during the Great Recession and is about 0.6% in 2016:Q1. [ABSTRACT FROM AUTHOR]

In this paper, I propose an econometric technique to estimate a Markov-switching Taylor rule subject to the zero lower bound of interest rates. I show that incorporating a Tobit-like specification allows to obtain consistent estimators. More importantly, I show that linking the switching of the Taylor rule coefficients to the switching of the coefficients of an auxiliary uncensored Markov-switching regression improves the identification of an otherwise unidentifiable prevalent monetary regime. To illustrate the proposed estimation technique, I use U.S. quarterly data spanning 1960:1-2013:4. The chosen auxiliary Markov-switching regression is a fiscal policy rule where federal revenues react to debt and the output gap. Results show that there is evidence of policy co-movements with debt-stabilizing fiscal policy more likely accompanying active monetary policy, and vice versa. [ABSTRACT FROM AUTHOR]