In this paper we present Aino 3.0, the latest vintage of the dynamic stochastic general equilibrium (DSGE) model used at the Bank of Finland for policy analysis. Aino 3.0 is a small-open economy DSGE model at the intersection of the recent literatures on so-called TANK (“Two-Agent New...

Bayesian approaches to the estimation of DSGE models are becoming increasingly popular. Prior knowledge is normally formalized either be information concerning deep parameters’ values (‘microprior’) or some macroeconomic indicator, e.g. moments of observable variables (‘macroprior’)....

The number of variables related to long-run economic growth is large compared with the number of countries. Bayesian model averaging is often used to impose parsimony in the cross-country growth regression. The underlying prior is that many of the considered variables need to be excluded from...

This paper provides new evidence on the effects of government spending shocks and the fiscal transmission mechanism in the euro area for the period 1980-2008. Our contribution is two-fold. First, we investigate changes in the macroeconomic impact of government spending shocks using time-varying...

Using a two-sector endogenous growth model, this paper explores how productivity shocks in the goods and human capital producing sectors contribute to explaining aggregate cycles in output, consumption, investment and hours. To contextualize our findings, we also assess whether the human capital...

Many factors inhibiting and facilitating economic growth have been suggested. Will international income data tell which matter when all are treated symmetrically a priori? We find that growth determinants emerging from agnostic Bayesian model averaging and classical model selection procedures...

We use a Bayesian time-varying parameters structural VAR with stochastic volatility for GDP deflator inflation, real GDP growth, a 3-month nominal rate, and the rate of growth of M4 to investigate the underlying causes of the Great Moderation in the United Kingdom. Our evidence points towards a...

This paper introduces a new measure of dependence or jointness among explanatory variables. Jointness is based on the joint posterior distribution of variables over the model space, thereby taking model uncertainty into account. By looking beyond marginal measures of variable importance,...

This paper considers Bayesian regression with normal and double-exponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range...

The paper considers a Bayesian approach to the cointegrated VAR model with a uniform prior on the cointegration space. Building on earlier work by Villani (2005b), where the posterior probability of the cointegration rank can be calculated conditional on the lag order, the current paper also...