The article compares forecast quality from two atheoretical models. Neither method assumed a priori causality and forecasts were generated without additional assumptions about regressors. Tendency survey data was used within the Bayesian averaging of classical estimates (BACE) framework and...

The main goal of the article is to investigate forecasting quality of two approaches to modelling main macroeconomic variables without a priori assumptions concerning causality and generate forecasts without additional assumptions regarding regressors. With application of tendency survey data...

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’)....

I propose to estimate structural impulse responses from macroeconomic time series by doing Bayesian inference on the Structural Vector Moving Average representation of the data. This approach has two advantages over Structural Vector Autoregressions. First, it imposes prior information directly...

Since Russia's invasion of Ukraine, many countries have pledged to end or restrict their oil and gas imports to curtail Moscow's revenues and hinder its war effort. Thus, the European ministers agreed to trigger a cap on the gas price. To detect the importance of the price cap for gas, we...

Inference on the long-run properties of a Vector Autoregression (VAR) consisting wholly of I(1) variables are made using Bayesian methods. In particular, the implications on the forecast and impulse response function distributions of directly estimating and restricting the drift parameters of...

A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....

A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the...

In the aftermath of the 2008 Global Financial Crisis (GFC), scholars and policymakers turned their attention to the role of uncertainty in amplifying the effects of economic or financial shocks on economic activity. A growing literature has focused on addressing this question. Most works find...

We use a Bayesian dynamic factor model to measure Germany's pre World War I economic activity. The procedure makes better use of existing time series data than historical national accounting. To investigate industrialization we propose to look at comovement between sectors. We find that...