Using interindustry input-output relations as a Bayesian prior in employment forecasting models

Abstract

Listen

Translate article

This paper presents the results of using input-output tables as a source of Bayesian prior information in a national employment forecasting model. A Bayesian vector autoregressive (BVAR) estimation technique is used to incorporate the interindustry input-output table relationships into the labor market forecasting model. This technique requires that a simple translation of the direct use coefficients from the input-output table be used as prior weighting elements to depict the interindustry relations. The Bayesian model provides out-of-sample forecasts superior to those from unconstrained vector autoregressive, univariate autoregressive, a block recursive bvar model and a naive BVAR model based on the Minnesota random walk prior. This suggests that interindustry input-output table linkages provide useful information that can be effectively incorporated into labor market forecasting models.