A “Dirty” Approach to Efficient Revenue Forecasting

Abstract

A “dirty forecast” refers to any forecast conducted where non-traditional, coincident indicators are included. These coincident indicators tell us about the behavior within an environment in the here and now rather than measure the environment itself. By focusing on behavior, dirty forecasts are able to pick up changes in the environment well before the changes become measurable outcomes. Dirty processes have been used for some time in economics, but their use in local government forecasting is relatively new. This paper explores the use by discussing what dirty forecasts are and how they can be used to obtain better, more efficient estimates of local government revenues and expenditures. This foundation is then demonstrated with a case study from the city of Seattle