This paper explores a semiparametric version of a time-varying regression, where a subset of the regressors have a fixed coefficient and the rest a time-varying one. We provide an estimation method and establish associated theoretical properties of the estimates and standard errors in extended...

We analyze the interaction between monetary policy in the US and the global economy proposing a new class of Bayesian global vector autoregressive models that accounts for time-varying parameters and stochastic volatility (TVP-SV-GVAR). We find that a contractionary US monetary policy shock...

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...

We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural...

We study the time-varying effects of Tobin's q and cash flow on investment dynamics in the USA using a vector autoregression model with drifting parameters and stochastic volatilities estimated via Bayesian methods. We find significant variation over time of the response of investment to shocks...

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...

Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism. This requires the VAR parameters to be stable over the evaluation and forecast sample, or to explicitly consider parameter time variation. The earlier...

We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We use this approach in the context of stock return predictability and...