We examine finite sample properties of estimators for approximate factor models when N is small. Contrary to the “rule-of-thumb”, we find that the principal component analysis estimator and the quasi-maximum likelihood estimator perform well even when N is small.

Is maximum likelihood suitable for factor models in large cross-sections of time series? We answer this question from both an asymptotic and an empirical perspective. We show that estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and...

Functional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal...

Functional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal...

This paper shows consistency of a two step estimator of the parameters of a dynamic approximate factor model when the panel of time series is large (n large). In the first step, the parameters are first estimated from an OLS on principal components. In the second step, the factors are estimated...

Estimating and assessing the risk of a large portfolio is an important topic in financial econometrics and risk management. The risk is often estimated by a substitution of a good estimator of the volatility matrix. However, the accuracy of such a risk estimator for large portfolios is largely...

This paper deals with estimation of high-dimensional covariance with a conditional sparsity structure, which is the composition of a low-rank matrix plus a sparse matrix. By assuming sparse error covariance matrix in a multi-factor model, we allow the presence of the cross-sectional correlation...

In the context of Dynamic Factor Models (DFM), we compare point and interval estimates of the underlying unobserved factors extracted using small and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather...

The estimation of regressions models with two-way error component disurbances, is considered for the case where both the random effects are non-spherically distributed. The usual approach that first transforms the effects into uncorrelated ones and then applies within and between...

This paper develops a novel approach for estimating latent state variables of Dynamic Stochastic General Equilibrium (DSGE) models that are solved using a second-order accurate approximation. I apply the Kalman filter to a state-space representation of the second-order solution based on the...