Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 19, 2022 erstellt
Description:
A methodology for high dimensional causal inference in a time series context is introduced. It is assumed that there is a monotonic transformation of the data such that the dynamics of the transformed variables are described by a Gaussian vector autoregressive process. This is tantamount to assume that the dynamics are captured by a Gaussian copula. No knowledge or estimation of the marginal distribution of the data is required. The procedure consistently identifies the parameters that describe the dynamics of the process and the conditional causal relations among the possibly high dimensional variables under sparsity conditions. The methodology allows us to identify such causal relations in the form of a directed acyclic graph. As an application we estimate the directed acyclic graph for the order book on one-minute aggregated data on four stock constituents of the S&P500