In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where factors are unobserved and factor loadings are nonparametrically unknown smooth functions of individual characteristics variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with sample size. This is a very general framework and includes many existing linear and nonlinear panel data models as special cases.

Interactive fixed effects are a popular means to model unobserved heterogeneity in panel data. Models with interactive fixed effects are well studied in the low-dimensional case where the number of parameters to be estimated is small. However, they are largely unexplored in the high-dimensional case where the number of parameters is large, potentially much larger than the sample size itself. In this paper, we develop new econometric methods for the estimation of high-dimensional panel data models with interactive fixed effects.

In this paper, we consider a panel data model which allows for heterogeneous time trends at different locations. We propose a new estimation method for the panel data model before we establish an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation presents in both dimensions of the error terms. We examine the finite–sample properties of the proposed model and estimation method through extensive simulated studies.

Most stock markets are open for 6-8 hours per trading day. The Asian, European and American stock markets are separated in time by time-zone differences. We propose a statistical dynamic factor model for a large number of daily returns across multiple time zones. Our model has a common global factor as well as continental factors. Under a mild fixed-signs assumption, our model is identified and has a structural interpretation.

In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalised shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one.