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...

This paper estimates the path of inflation persistence in the United States over the last 50 years and draws implications about the evolution of the Federal Reserve's monetary-policy preferences. Standard models of central bank optimization predict that the central bank's preference for output...

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...

Recently, it has been suggested that the effect of government expenditure on private consumption is dependent on the level of public debt. More specifically, a higher public debt implies a less Keynesian response in private consumption. In this paper we investigate if this theory is supported by...

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling...

Prepared for the Handbook of Economic Forecasting, vol 2 This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by extension, forecasting depends on numerical methods for simulating from the posterior distribution of the parameters and spe-...

Inflation did not fall as much as many economists expected as the Great Recession hit the US economy. One explanation suggested for this phenomenon is that the Phillips curve has become flatter. In this paper we investigate the stability of the US Phillips curve, employing Bayesian VARs to...

We use Bayesian techniques to estimate bivariate VAR models for Swedish unemployment rate and inflation. Employing quarterly data from 1995Q1 to 2017Q3 and new tools for model selection, we compare a model with time-varying parameters and stochastic volatility to a specification with constant...

In this paper we assess whether the relation between the corporate bond-yield spread and the real economy has been stable over time. Using quarterly US data from 1953Q1 to 2018Q2, we estimate Bayesian VAR models which allow for drifting parameters and/or stochastic volatility and conduct formal...

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...