Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, this paper establishes a new central limit theorem (CLT) for a linear spectral statistic (LSS) of high dimensional sample correlation matrices for the case where the...

Capturing dependence among a large number of high dimensional random vectors is a very important and challenging problem. By arranging n random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors are...

This paper proposes a new unit-root test for the case where a high-dimensional vector of nonstationary time series is considered. A new CLT is being established and studied both theoretically and numerically

This paper proposes a new near-unit root test for a class of high-dimensional nonstationary time series. A central limit theorem for the proposed test is proposed and then evaluated by an extensive simulation study

This study decomposes the bilateral trade flows using a three-dimensional panel data model. Under the scenario that all three dimensions diverge to infinity, we pro- pose an estimation approach to identify the number of global shocks and country- specific shocks sequentially, and establish the...

This paper proposes a new time-varying minimum variance portfolio (TV-MVP) in a large investment universe of assets. Our method extends the existing literature of minimum variance portfolio by allowing for time-varying factor loadings, which is the facilitator to capture the dynamics of asset...