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Steady-state priors for vector autoregressions (replication data)
Bayesian priors are often used to restrain the otherwise highly over-parametrized vector autoregressive (VAR) models. The currently available Bayesian VAR methodology does not... -
Risk of catastrophic terrorism: an extreme value approach (replication data)
This paper models the stochastic behavior of large-scale terrorism using extreme value methods. We utilize a unique dataset composed of roughly 26,000 observations. These data... -
On portfolio optimization: How and when do we benefit from high-frequency dat...
We examine how the use of high-frequency data impacts the portfolio optimization decision. Prior research has documented that an estimate of realized volatility is more precise... -
On the effect of prior assumptions in Bayesian model averaging with applicati...
We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of... -
Assessing the prudence of economic forecasts in the EU (replication data)
We estimate the EU Commission loss preferences for major economic forecasts of 12 Member States. Based on a recently proposed method by Elliott, Komunjer and Timmermann (2005)... -
Feasible estimation of firm-specific allocative inefficiency through Bayesian...
Both the theoretical and empirical literature on the estimation of allocative and technical inefficiency has grown enormously. To minimize aggregation bias, ideally one should... -
Boosting diffusion indices (replication data)
In forecasting and regression analysis, it is often necessary to select predictors from a large feasible set. When the predictors have no natural ordering, an exhaustive...
