Webfor the study of non-linear models. First, if shocks have fat tails, linearization may sim-ply produce a poor approximation of the full model. Second, non-linearities may explain away the fat tails: what we capture as large rare shocks may in fact be Gaussian shocks whose e ect is ampli ed through a non-linear propagation mechanism. In fact ... WebAbstract. We introduce a Bayesian VAR model with non-Gaussian disturbances that are modelled with a finite mixture of normal distributions. Importantly, we allow for regime switching among the different components of the mixture of normals. Our model is highly flexible and can capture distributions that are fat-tailed, skewed and even multimodal.

simshocks: Underlying gaussian shocks for risk factors

WebThe tractability of discrete time affine term structure models (DTATSM) is fully preserved when adding squared Gaussian shocks (SGS) to factor processes. SGS guarantee non … WebJul 20, 2024 · Although using non-Gaussian distributions in economic models has become increasingly popular, currently there is no systematic way for calibrating a discrete distribution from the data without imposing parametric assumptions. This paper proposes a simple nonparametric calibration method based on the Golub-Welsch algorithm (Golub … greencastle pa to newark nj

Characterizing growth instability: new evidence on unit

WebMar 3, 2024 · Working Paper No. 417By Martin M Andreasen. This paper studies how non-Gaussian shocks affect risk premia in DSGE models approximated to second and third … WebFeb 22, 2024 · 2 Although no shock can follow a Gaussian distribution, a shock is allowed to be arbitrarily close to a Gaussian distrib ution. In the case that the shock process is close to Gaussian, the model ... WebThis paper proposes a new method to estimate the (possibly nonlinear) dynamic effects of structural shocks by using Gaussian basis functions to parametrize impulse response … greencastle pa to henrico va