English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 possible forecast performance with respect to the core variables. We apply the statistical methodology to model real Gross Domestic Product and the national Unemployment Rate, two time series widely monitored by economists and policy-makers, based on a large set of Quarterly Workforce Indicators comprising various major sectors of the economy and different measures of labor market conditions.

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