We introduce a new class of autoregressive models for integer-valued time series using the rounding operator. Compared with classical INAR models based on the thinning operator, the new models have several advantages: simple innovation structure, autoregressive coefficients with arbitrary signs,...

Let (εj)j≥0 be a sequence of independent p-dimensional random vectors and τ≥1 a given integer. From a sample ε1,…,εT+τ of the sequence, the so-called lag-τ auto-covariance matrix is Cτ=T−1∑j=1Tετ+jεjt. When the dimension p is large compared to the sample size T, this paper...

Microreactors offer excellent mass and heat transfer performance for extraction and multiphase reactions. They provide a powerful tool for process intensification and micro scale processing. This paper reviews the structures of microreactors and units, and their applications on the synthesis of...

Using panel data analyses, this paper examines the relation between human capital formation and Foreign Direct Investment (FDI) in China. It shows that FDI has a significant effect on human capital formation, at least for the period 1995-2001. When we estimate the relationship between FDI and...

In the spiked population model introduced by Johnstone (2001) [11], the population covariance matrix has all its eigenvalues equal to unit except for a few fixed eigenvalues (spikes). The question is to quantify the effect of the perturbation caused by the spike eigenvalues. Baik and Silverstein...

In this note we develop an extension of the Marčenko–Pastur theorem to time series model with temporal correlations. The limiting spectral distribution (LSD) of the sample covariance matrix is characterised by an explicit equation for its Stieltjes transform depending on the spectral density...

This paper discusses the problem of estimating the population spectral distribution from high-dimensional data. We present a general estimation procedure that covers situations where the moments of this distribution fail to identify the model parameters. The main idea is to use generalized...

Correlation stress testing is motivated by a well-known phenomenon: correlations change under financial crises. The adjustment of correlation matrices may be required to evaluate the potential impact of these changes. Very often, some correlations are explicitly adjusted (core correlations),...