Nonlinear Adventures at the Zero Lower Bound

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Macroeconomic Dynamics Near the ZLB: A Tale of Two Countries (2016, methodology): “Unfortunately, model properties tend to be very sensitive to the approximation technique and to implicit or explicit assumptions about the probability of leaving the ZLB, see Braun et al. (2012) and Fernández-Villaverde et al. (2015).”
OccBin: A Toolkit for Solving Dynamic Models With Occasionally Binding Constraints Easily (2015, unknown): “Another departure from the calibration in Fern´andez-Villaverde et al. (2012) pertains θ , the probability that an intermediate ﬁrm will have to keep its price unchanged.”
Log-linear approximation versus an exact solution at the ZLB in the New Keynesian model (2016, background): “3 Fernández-Villaverde et al. (2015) and Miao and Ngo (2016), however, assume a common labor market from which all firms must hire, in which case price dispersion is a state variable of the non-linear model. Consequently a simple two equation characterization of the form presented in this paper is n”
Bond Risk Premia with Machine Learning∗ (2018, unknown): “Consistent with studies that show that non-linearities matter in the zero-lower bound period (see Fernández-Villaverde et al., 2015,Bauer and Rudebusch, 2016, and Gust et al., 2017, among others), we find evidence that a deeper network is needed to better proxy for the non linear mapping between…”