Multivariate Time Series Analysis of U. S. Army Recruiting

The United States Army Recruiting Command requires tools to quantify the impact of factors in the recruiting environment, identify differences in the recruiting processes across its five regional subordinate units, and measure the effectiveness of its policies and resource expenditures. This thesis examines recruiting data for the "high-quality" male demographic from July 1992 to September 1997. It uses multivariate time series analysis to predict the number of enlistment contracts signed in a month as a function of fifteen exogenous and endogenous factors plus monthly indicators. A stepwise recursion using bootstrap simulation is developed to identify significant factors in the multivariate time series. The significant factors in the reduced models are compared to those contained in models developed in previous studies. The models are also used to create nine- month projections of recruiting production, which are compared to known production figures from test set data to determine forecast accuracy. The results of this research support the intuition that the influential factors differ by region. The stepwise model reduction recursion using bootstrap simulation offers potential for further refinement and application.Naval Postgraduate School Monterey CA is the author of 'Multivariate Time Series Analysis of U. S. Army Recruiting', published 2000 under ISBN 9781423537052 and ISBN 142353705X.