This paper considers Bayesian regression with normal and doubleexponential 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...

Monetary aggregates continue to play an important role in the ECB's policy strategy. This paper revisits the case for money, surveying the ongoing theoretical and empirical debate. The key conclusion is that an exclusive focus on non-monetary factors alone may leave the ECB with an incomplete...

This paper contributes to the debate on the role of money in monetary policy by analyzing the information content of money in forecasting euro-area inflation. We compare the predictive performance within and among various classes of structural and empirical models in a consistent framework using...

Distinguishing pure supply effects from other determinants of price and quantity in the market for loans is a notoriously difficult problem. Using German data, we employ Bayesian vector autoregressive models with sign restrictions on the impulse response functions in order to enquire the role of...

This paper presents a Structural Vector Autoregressive (SVAR) model with particular attention to the Hungarian labour market. The identification of structural shocks is based on sign restrictions. We identify four structural shocks: a labour supply, an aggregate supply, an aggregate demand and a...

A standard approach in measuring the effect of monetary policy on output and prices is to estimate a VAR model, characterise somehow the monetary policy shock and then plot impulse responses. In this paper I attempt to do this exercise with Hungarian data. I compare two identification...

This Paper describes a procedure for constructing theory restricted prior distributions for BVAR models. The Bayes Factor, which is obtained without any additional computational e?ort, can be used to assess the plausibility of the restrictions imposed on the VAR parameter vector by competing...

A Bayesian model averaging procedure is presented that makes use of a finite mixture of many model structures within the class of vector autoregressive (VAR) processes. It is applied to two empirical issues. First, stability of the Great Ratios in U.S. macro-economic time series is investigated,...

The empirical support for a real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure. This procedure makes use of a finite mixture of many models within the class ofvector autoregressive (VAR) processes. The linear VAR model is extendedto...

This paper considers Bayesian regression with normal and doubleexponential 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...