In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors r , which is an unresolved issue in the rapidly growing… (More)

It is widely known that when there are errors with a moving-average root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information… (More)

This paper presents a toolkit for Panel Analysis of Non-stationarity in Idiosyncratic and Common components (PANIC). The point of departure is that the unobserved common factors can be consistently… (More)

This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the… (More)

A widely held but untested assumption underlying macroeconomic analysis is that the number of shocks driving economic fluctuations, q, is small. In this paper, we associate q with the number of… (More)

This paper uses a decomposition of the data into common and idiosyncratic components to develop procedures that test if these components satisfy the null hypothesis of stationarity. The decomposition… (More)

We consider the situation when there is a large number of series, N , each with T observations, and each series has some predictive ability for some variable of interest. A methodology of growing… (More)

Forecasting using “diffusion indices” has received a good deal of attention in recent years. The idea is to use the common factors estimated from a large panel of data to help forecast the series of… (More)

Existing empirical literature on the risk-return relation uses a relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market… (More)

Econometric analysis of large dimensional factor models has been a heavily researched topic in recent years. This review surveys the main theoretical results that relate to static factor models or… (More)