Optimal Macroeconomic Policy in Nonlinear Models : A Vstar Perspective

The paper derives a structural identification of a vector smooth transition autoregression that yields a low-dimension and certainty-equivalent nonlinear optimization problem. Optimal rules are calculated with the state dependent coefficients approach, which allows linear methods to solve nonlinear problems. The empirical application revisits interest rate and quantitative easing (QE) policy on United States data during 1979-2018. The actual size of QE and duration of the zero lower bound are found close to those prescribed by the optimal policy, but not the timing and composition of QE. The methodology compares favourably against alternative optimization approaches based on linearization or numerical search