Moving average stochastic volatility models with application to inflation forecast

TL;DR: In this article, the authors generalize the stochastic volatility in mean model to allow for time-varying parameters in the conditional mean and develop an efficient Markov chain Monte Carlo algorithm based on band and sparse matrix algorithms instead of the Kalman filter.

TL;DR: This paper developed a bivariate model of inflation and long-run forecasts of inflation, which allows for the estimation of the link between trend inflation and the long run forecast, and found that long run forecasts can provide substantial help in refining estimates and fitting and forecasting inflation.

TL;DR: In an application that involves 20 macroeconomic variables, it is found that these BVARs with more flexible covariance structures outperform the standard variant with independent, homoscedastic Gaussian innovations in both in-sample model-fit and out-of-sample forecast performance.

TL;DR: In this article, the authors propose a new class of dynamic shrinkage processes for Bayesian time series and regression analysis, which inherit the desirable shrinkage behavior of popular global-local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modelling time series data or regression functions with local features.

TL;DR: This work considers approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables and can directly compute very accurate approximations to the posterior marginals.

TL;DR: In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models, and a highly effective method is developed that samples all the unobserved volatilities at once using an approximate offset mixture model, followed by an importance reweighting procedure.

TL;DR: In this paper, monetary policy and the private sector behavior of the US economy are modeled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations.

TL;DR: For a VAR with drifting coefficients and stochastic volatilities, the authors present posterior densities for several objects that are pertinent for designing and evaluating monetary policy, including measures of inflation persistence, the natural rate of unemployment, a core rate of inflation, and ‘activism coefficients' for monetary policy rules.