Over the past decade there has been remarkable progress in developing empirical micro-founded macroeconomic models for monetary policy analysis that feature coherence both to economic theory and to the data. In this paper, we estimate using Bayesian methods a second-generation micro founded...

Data in which each observation is a curve occur in many applied problems. This paper explores prediction in time series in which the data is generated by a curve-valued autoregression process. It develops a novel technique, the predictive factor decomposition, for estimation of the...

Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We develop new methods to analyze different sources of uncertainty in one coherent structure, which is useful for policy decisions. We show how to estimate the size of the uncertainty based on...

We show that the empirical distribution of the roots of the vector auto-regression of order n fitted to T observations of a general stationary or non-stationary process, converges to the uniform distribution over the unit circle on the complex plane, when both T and n tend to infinity so that...

We develop a new estimator of the number of factors in the approximate factor models. The estimator works well even when the idiosyncratic terms are substantially correlated. It is based on the fact, established in the paper, that any finite number of the largest "idiosyncratic" eigenvalues of...

We study the workings of the factor analysis of high-dimensional data using arti…cial 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 allow us to shed some light on the practical...

This paper considers a linear panel data model with reduced rank regressors and interactive fixed effects. The leading example is a factor model where some of the factors are observed, some others not. Invariance considerations yield a maximal invariant statistic whose density does not depend on...

This paper studies the asymptotic power of tests of sphericity against perturbations in a single unknown direction as both the dimensionality of the data and the number of observations go to infinity. We establish the convergence, under the null hypothesis and the alternative, of the log ratio...