We show that confidence regions covering the identified set may be preferable to confidence regions covering each of its points in robust control applications.

We show that confidence regions covering the identified set may be preferable to confidence regions covering each of its points in robust control applications.

We show that con¯dence regions covering the identified set may be preferable to con¯dence regions covering each of its points in robust control applications.

In this paper we consider the policy implications of a fully specified dynamic general equilibrium model, developed by Smets and Wouters (2003a). This is a relatively large-scale forward looking model, which was shown to provide a good fit to the data. However there has been little previous work...

The paper uses functional auto-regression to predict the dynamics of interest rate curve. It estimates the auto-regressive operator by extending methods of the reduced-rank auto-regression to the functional data. Such an estimation technique is better suited for prediction purposes as opposed to...

We show that the empirical distribution of the roots of the vector autoregression (VAR) of order <italic>p</italic> fitted to <italic>T</italic> observations of a general stationary or nonstationary process converges to the uniform distribution over the unit circle on the complex plane, when both <italic>T</italic> and <italic>p</italic> tend to infinity so that...

We study the workings of the factor analysis of high-dimensional data using artificial series generated from a large, multi-sector dynamic stochastic general equilibrium (DSGE) model. The objective is to use the DSGE model as a laboratory that allows us to shed some light on the practical...

This paper deals with the local asymptotic structure, in the sense ofLe Cam’s asymptotic theory of statistical experiments, of the signal detectionproblem in high dimension. More precisely, we consider the problemof testing the null hypothesis of sphericity of a high-dimensional...

This paper introduces a drifting-parameter asymptotic framework to derive accurate approximations to the finite sample distribution of the principal components (PC) estimator in situations when the factors’ explanatory power does not strongly dominate the explanatory power of the...