This paper focuses on Bayesian shrinkage methods for covariance matrix estimation. We examine posterior properties and frequentist risks of Bayesian estimators based on new hierarchical inverse-Wishart priors. More precisely, we give the conditions for the existence of the posterior...

Working with micro-founded loss functions to derive and analyse optimal policy ensures consistency with the model used and overcomes the misleading prescriptions that result from using exogenous ad hoc loss functions. However, when allowance is made for the fact that different theories of...

We propose a new approach to evaluating the usefulness of a set of forecasts, based on the use of a discrete loss function de…ned on the space of data and forecasts. Exist- ing procedures for such an evaluation either do not allow for formal testing, or use tests statistics based just on the...

Evaluating Value at Risk (VaR) methods of predictive accuracy in an objective and effective framework is important for both efficient capital allocation and loss prediction. From this reasons, finding an adequate method of estimating and backtesting is crucial for both the regulators and the...

This chapter reviews the theory of optimal monetary stabilization policy in New Keynesian models, with particular emphasis on developments since the treatment of this topic in Woodford (2003). The primary emphasis of the chapter is on methods of analysis that are useful in this area, rather than...

A large number of parameterizations have been proposed to model conditional variance dynamics in a multivariate framework. However, little is known about the ranking of multivariate volatility models in terms of their forecasting ability. The ranking of multivariate volatility models is...

A large number of parameterizations have been proposed to model conditional variance dynamics in a multivariate framework. However, little is known about the ranking of multivariate volatility models in terms of their forecasting ability. The ranking of multivariate volatility models is...

Choice-based conjoint analysis is a popular marketing research technique to learn about consumers' preferences and to make market share forecasts under various scenarios for product offerings. Managers expect these forecasts to be “realistic” in terms of being able to replicate market shares...

According to the Statistical Learning Theory, the support vectors represent the most informative data points and compress the information contained in training set. However, a basic problem in the standard support vector machine is that when the data is noisy, there exists no guaranteed scheme...

Accurate forecasts of incoming calls are crucial to optimal staffing decisions in call centers. This paper evaluates a wide range of models and forecast combination techniques by means of statistical and economic criteria. Relative to the previous literature, this paper is novel in several...