Variable targeting and reduction in large vector autoregressions with applications to workforce indicators.

ABSTRACT: We develop statistical tools for time series analysis of large multivariate datasets, when a few core series are of principal interest and there are many potential auxiliary predictive variables. The methodology, based on Vector Autoregressions (VAR), handles the case where unrestricted fitting is precluded by a large number of series and a huge parameter space. In particular, we adopt a forecast error criterion and use Granger-causality tests in a sequential manner to build a VAR model that targets the main variables. This approach affects variable reduction (or equivalently, sparsity restrictions) in a computationally fast way that remains feasible for large dimensions. The search for the best model results in a VAR, fitted with a selection of supporting series, that has the best possibl

SUBMITTER: McElroy TS 

PROVIDER: S-EPMC10184600 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature