Bayesian techniques. While asymptotic identification tests show that some of the parameters are weakly identified in the model … with the Bayesian posterior median parameters, but at the expense of some the ML estimates being implausible from a … problems with model misspecification, and not primarily weak identification, is the main challenge ahead in developing …

We consider a Bayesian Model Averaging approach for the purpose of forecasting Swedish consumer price index inflation … these indicators in combination can give rise to. The results, in terms of out-of-sample-performance, suggest that Bayesian …

We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast … standard Bayesian model averaging using the marginal likelihood. …

compared with two less structural approaches for identification of monetary policy shocks. The first assumes that shocks can be …

, based on microeconomic deposit market data. We impose identification restrictions both in the cross-section (across insured …

variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable … problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the …

analysis of cointegration. Villani (2000) and Strachan and van Dijk (2003) have recently proposed finite sample Bayesian … robustness of the results to variations in the prior. A simulation study shows that the Bayesian approach performs remarkably …