BayVAR_R is an R package designed to estimate and analyze Vec-tor Autoregressive (VAR) models from both a classical (UVAR) andBayesian (BVAR) perspective. The package includes functionalities forthe speci cation, estimation and diagnosis of such a models. It alsoprovides procedures for...

This paper examines whether the presence of parameter instabilities in dynamic stochastic general equilibrium (DSGE) models affects their forecasting performance. We apply this analysis to medium-scale DSGE models with and without financial frictions for the US economy. Over the forecast period...

Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While...

We propose a new Bayesian VAR model for forecasting household loan stocks in Finland. The model is designed to work as a satellite model of a larger DSGE model for the Finnish economy, the Aino 2.0 model. The forecasts produced with the BVAR model can be conditioned on projections of several...

Prediction markets for future events are increasingly common and they often trade several contracts for the same event. This paper considers the distribution of a normative risk-neutral trader who, given any portfolio of contracts traded on the event, would choose not to reallocate that...

Inflation has been persistently weak in the euro area despite the economic recovery since 2013. We investigate the sources behind this protracted low inflation by building a time-varying parameter model that jointly explains the dynamics of inflation and inflation expectations from the ECB's...

This paper proposes an international collaboration between researchers in academia and policymaking institutions to stimulate and coordinate research on probability forecasting in macroeconomics, developing a toolbox for short-term prediction. The toolbox should include time series models,...

This paper proposes an accurate, parsimonious and fast-to-estimate forecasting model for integer-valued time series with long memory and seasonality. The modelling is achieved through an autoregressive Poisson process with a predictable stochastic intensity that is determined by two factors: a...

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of...

By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies:...