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 develop a new consistent and simple to compute estimator of the number of factors in the approximate factor models of Chamberlain and Rothchild (1983). Our setting requires both time series and cross-sectional dimensions of the data to be large. The main theoretical advantage of our estimator...

We provide a test for the specification of a structural model without identifying assumptions. We show the equivalence of several natural formulations of correct specification, which we take as our null hypothesis. From a natural empirical version of the latter, we derive a Kolmogorov-Smirnov...

Time series parametric models generally cater to a particular objective, such as forecasting, and it is therefore desirable to judge such models solely on the basis of their performance in the fullfillment of that objective. We propose a specification testing procedure which concentrates power...

The long range dependence paradigm appears to be a suitable description of the data generating process for many observed economic time series. This is mainly due to the fact that it naturally characterizes time series displaying a high degree of persistence, in the form of a long lasting effect...

Mixtures of distributions are present in many econometric models, such as models with unobserved heterogeneity. It is thus crucial to have a general approach to identify them nonparametrically. Yet the literature so far only contains isolated examples, applied to specific models. We derive the...

A formalization of the Precautionary Principle is given here: We formalize scientific knowledge on the likelihood of events in the state space and the concepts of scientifically unambiguous events and acts. We give a definition of a non-precautionary social planner as a Savage Expected Utility...

We propose a measure of the degree of ambiguity associated with a belief function and a nonparametric method to estimate it. The degree of ambiguity associated with a belief function is measured by the Kullback-Leibler diameter of the set of probability measures compatible with it. It is shown...