Consistent Underestimation Bias, the Asymmetrical Loss Function, and Homogeneous Sources of Bias in State Revenue Forecasts
Voorhees, William R., Journal of Public Budgeting, Accounting & Financial Management
One component of revenue forecast error has been attributed to the phenomena of consistent underestimation bias due asymmetrical loss. Because underestimation of revenue forecast results in less loss to forecasters than overestimations, there appears to be a bias for forecasters to underestimate revenue forecasts. This paper confirms this hypothesis. Additionally, with the greater usage of national forecasting organizations that provide economic forecasts on which revenue forecasts are based, a secondary source of forecaster bias may be present in many state level forecasts. This hypothesis is supported by the increase in number of states using such organizations and a decrease in the standard deviation of the annual mean percentage state forecast error.
Revenue forecast error often is attributed to consistent underestimation bias due to an asymmetrical loss function. Because forecasters are subject to a greater loss when they overestimate revenue than when they underestimate revenue, there is an incentive for forecasters to under forecast revenues and thus avoid losses they may encounter with overestimated revenue. Forecaster loss is manifested in many forms including loss of potential salary increases, loss of reputation as a forecaster and loss of job responsibilities. Research to date has considered the revenue forecaster as the source of underestimation bias, but the recent usage of external economic forecasts may also be introducing bias into forecasts. Many states utilize a conditional forecasting process where a forecast is made for economic conditions and then the revenues are forecast from the economic forecast. If the economic forecast is underestimated, then an accurate revenue-forecasting model will underestimate revenue. Recent trends indicate that states are increasing their reliance on external forecasts generated by a very limited number of national forecasting consultants. In October of 2002, the two primary firms, Data Resources, Inc., (DRI) and Wharton Econometric Forecasting Associates (WEFA) merged into a single company called Global Insights, further reducing sources of economic forecasts. Companies providing economic forecasts on a fee basis may well be operating under a similar set of asymmetrical risk factors as are revenue forecasters. Renewal of contracts for economic forecasts depends on both the accuracy and the impact that the error of the forecast has on governmental disruption. Because an under forecast will always result in less disruption than an over forecast, third party economic forecasters are incented to underestimation bias. As more states utilize a limited number of economic forecasting services, the error for the economic forecast should become homogenized as indicated by a decreasing variance of error across state revenue forecasts.
This paper first considers the literature on underestimation bias and the effects on forecasts attributed to fiscal stress. Next state forecasts are examined for a consistent underestimation bias for the years 1979 thru 2002. Finally, aggregate state forecast error variances are examined for consistency across years, which would indicate the introduction of a homogenous error source.
SOURCES OF BIAS IN REVENUE FORECASTS
In addition to random error, bias also creates an opportunity for the forecast to be in error. Although all forecasts have error, an unbiased, strongly rational forecast has error that is attributable only to randomness. A forecast is said to be strongly rational if the forecast and the actual revenues, conditional upon the influences of a set of full information, are equal (Feenberg, Gentry, Gilroy & Rosen, 1989). In other words, given a set of full information available and taking into account the effects of full information, the difference between the forecast and the actual revenue should be zero. Weak rationality exists when the set of information is incomplete yet the forecasters achieve the correct answer. …