Speaker: Stephan Smeekes, Maastricht University

Title: Inference in Non-stationary High-Dimensional VARs (Joint work with Alain Hecq and Luca Margaritella)

Abstract:

In this paper we construct an inferential procedure for Granger causality in high-dimensional non-stationary vector autoregressive (VAR) models. Our method does not require knowledge of the order of integration of the time series under consideration. We augment the VAR with at least as many lags as the suspected maximum order of integration, an approach which has been proven to be robust against the presence of unit roots in low dimensions. We prove that we can restrict the augmentation to only the variables of interest for the testing, thereby making the approach suitable for high dimensions. We combine this lag augmentation with a post-double-selection procedure in which a set of initial penalized regressions is performed to select the relevant variables for both the Granger causing and caused variables. We then establish uniform asymptotic normality of a second-stage regression involving only the selected variables. Finite sample simulations show good performance, an application to investigate the (predictive) causes and effects of economic uncertainty illustrates the need to allow for unknown orders of integration.

His main research interests lie in the statistical analysis of time series data, combining techniques on the interface between econometrics, statistics and data science. Much of his research involves uncertainty quantification, often using the bootstrap, and the analysis of high-dimensional "Big Data" time series. Among the applications he considers are, long-run trends in macroeconomic and climatological time series, inference on risk measures for financial series, and the forecasting of macroeconomic and financial time series.

Host: Robert Adamek

Organisers: Luke Taylor and Leopoldo Catania.