This paper aims to provide a comprehensive overview of the estimation methodology for the
time-varying parameter structural vector autoregression (TVP-VAR) with stochastic volatility …

This chapter reviews Bayesian methods for inference and forecasting with VAR models.
Bayesian inference and, by extension, forecasting depends on numerical methods for …

With the acceleration of global energy transition and financialization, intense climate policy
uncertainty and financial speculation have significant impacts on the global energy market …

We study the conditional distribution of GDP growth as a function of economic and financial
conditions. Deteriorating financial conditions are associated with an increase in the …

The COVID-19 pandemic has led to enormous data movements that strongly affect
parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To …

This paper studies the time-varying market linkages between Bitcoin and green assets
before and during the COVID-19 pandemic through a TVP-VAR model with stochastic …

We use factor augmented vector autoregressive models with time-varying coefficients and
stochastic volatility to construct a financial conditions index that can accurately track …

This note shows how to apply the procedure of to the estimation of VAR, DSGE, factor, and
unobserved components models with stochastic volatility. In particular, it revisits the …

We evaluate alternative indicators of global economic activity and other market
fundamentals in terms of their usefulness for forecasting real oil prices and global petroleum …

In this paper, we develop methods for estimation and forecasting in large time-varying
parameter vector autoregressive models (TVP-VARs). To overcome computational …