Statistical Inference for High-Dimensional Vector Autoregression with Measurement Error

High-dimensional vector autoregression with measurement error is frequently encountered in a large variety of scientific and business applications. In this article, we study statistical inference the transition matrix under model. While there has been body literature studying sparse estimation matrix, paucity solutions, especially high-dimensional scenario. We develop inferential procedures for both global simultaneous testing matrix. first new expectation-maximization algorithm to estimate model parameters, carefully characterize their precisions. then construct Gaussian after proper bias variance corrections, from which derive test statistics. Finally, establish asymptotic guarantees. finite-sample performance our tests through intensive simulations, illustrate brain connectivity analysis example.