This paper proposes a novel and flexible framework to estimate autoregressive models with time-varying parameters. Our setup nests various adaptive algorithms that are commonly used in the macroeconometric literature, such as learning-expectations and forgetting-factor algorithms. These are...

We propose an extended time-varying parameter Vector Autoregression that allows for an evolving relationship between the variances of the shocks. Using this model, we show that the relationship between the conditional variance of GDP growth and the long-term interest rate has become weaker over...

We develop non-parametric instrumental variable estimation and inferential theory for econometric models with possibly endogenous regressors whose coefficients can vary over time either deterministically or stochastically, and the time-varying and uniform versions of the standard Hausman...

We develop a dynamic factor model with time-varying parameters and stochastic volatility, estimate it with several variables for a large number of countries and decompose the variance of each variable in terms of contributions from uncertainty common to all countries (global uncertainty),...

In this paper we extend the Bayesian Proxy VAR to incorporate time variation in the parameters. A Gibbs sampling algorithm is provided to approximate the posterior distributions of the model's parameters. Using the proposed algorithm, we estimate the time-varying effects of taxation shocks in...

In this paper, we add new evidence to a long-debated macroeconomic question, namely whether money growth has predictive power for inflation or, put differently, whether money growth Granger causes inflation. We use a historical dataset - consisting of annual Swedish data on money growth and...

We assess the bivariate relation between money growth and inflation in the euro area and the United States using hybrid time-varying parameter Bayesian VAR models. Model selection based on marginal likelihoods suggests that the relation is statistically unstable across time in both regions. The...

This paper develops a two-block Structural Vector Autoregression featuring time-varying parameters and stochastic volatility to estimate the changing spillover of global oil shocks into the Maltese economy during the period that goes from January 2008 to March 2022. The model is estimated by...

This paper evaluates the performance of structural VAR models in estimating the impact of credit supply shocks. In a simple Monte-Carlo experiment, we generate data from a DSGE model that features bank lending and credit supply shocks and use SVARs to try and recover the impulse responses to...