New Developments in Econometrics and Time Series

Friday, 11:45 Brendan K. Beare, University of California Vine Copula Specifications for Stationary Multivariate Markov Chains Vine copulae provide a graphical framework in which multiple bivariate copulae may be combined in a consistent fashion to yield a more complex multivariate copula. In this article, we discuss the use of vine copulae to build flexible semiparametric models for stationary multivariate higherorder Markov chains. We propose a new vine structure, the M-vine, that is particularly well suited to this purpose. Stationarity may be imposed by requiring the equality of certain copulae in the M-vine, while the Markov property may be imposed by requiring certain copulae to be independence copulae. Thursday, 10:15 Haeran Cho, University of Bristol Detecting Multiple Change-Points in Panel Data In this paper, we propose a method for detecting multiple change-points in the mean of (possibly) high-dimensional panel data. CUSUM statistics have been widely adopted for change-point detection in both univariate and multivariate data. For the latter, it is of particular interest to exploit the cross-sectional structure and achieve simultaneous change-point detection across the panels, by searching for change-points from the aggregation of multiple series of CUSUM statistics, each of which is computed on a single panel. For panel data of high dimensions, the detectability of a changepoint is influenced by several factors, such as its sparsity across the panels, the magnitude of jumps at the change-point and the unbalancedness of its location, and having a method that handles a wide range of change-point configurations without any prior knowledge is vital in panel data analysis. The Sparsified Binary Segmentation and the Double CUSUM Binary Segmentation represent determined efforts in this direction. We investigate under which conditions the two binary segmentation methods attain con-