In this paper we treat the issue of forecasting with DSGE and DSGE-VAR models, with particular attention to Bayesian estimation of the predictive distribution and its mean and covariance. As a novel contribution to the forecasting literature, which extends beyond (log-linearized) DSGE models and DSGE-VARs, we show how the value of the h-step-ahead marginal and joint predictive likelihood for a ...

Studies that seek to evaluate the e¤ects of technology shocks often employ structural VARs identi…ed with long-run restrictions. In the presence of a mismatch between the number of lags the data-generating process involves and the number of lags included in the VAR, estimates based on long-run restrictions can be largely biased. In this paper, I o¤er an alternative method that substantially red...

Central banks and other forecasters have become increasingly interested in various aspects of density forecasts. However, recent sharp changes in macroeconomic volatility — such as the Great Moderation and the more recent sharp rise in volatility associated with greater variation in energy prices and the deep global recession — pose significant challenges to density forecasting. Accordingly, th...

We show how security type systems from the literature of languagebased noninterference can be represented more directly as predicates defined by structural recursion on the programs. In this context, we show how our uniform syntactic criteria from [7,8] cover several previous type-system soundness results. 1 Security type systems As in Example 2 from [7, 8], we assume that atomic statements and...

This study examines the out-of-sample predictability of market risks measured as tail for stock returns eight advanced countries using a long-range monthly data over century. We follow Conditional Autoregressive Value at Risk (CAViaR) Engle and Manganelli (2004) to measure consequently, we produce results both 1% 5% VaRs across four variants (Adaptive, Symmetric absolute value, Asymmetric slope...