Virginia's Revenue Forecasting Process and Models

Abstract

In Virginia, major differences between revenue forecasts and actual revenue collections raised questions from legislators about the adequacy of the state's econometric forecasting models and the forecasting process itself. Consequently, Virginia's process and models were examined by the Joint Legislative Audit and Review Commission (JLARC), a legislative watchdog agency. The review found most of the differences could be attributed to declining economic factors and not poor forecasting. However, a surprisingly large component of the forecast was based on undocumented judgmental inputs that required greater oversight. Other states in similar circumstances may wish to evaluate their own forecasting process and econometric models using the JLARC approach.

State Revenue Forecasts and Political Acceptance: The Value of Consensus Forecasting in the Budget Process

“…Shkurti and Winefordner () describe an instance in Ohio in the late 1980s in which the competing political parties produced their own forecast estimates and accused one another of producing manipulated numbers that served their own political interests. Jonas, Rest, and Atkinson () find that forecast errors in Virginia were more attributable to economic factors than poor forecasting, although much of the forecast was based on undocumented judgmental inputs. Krause and Douglas () find that forecast error carries a quadratic effect with respect to political diversity on the consensus forecasting panel, suggesting that there can be both too much or too little comprehensive political inclusion involved with forecast accuracy.…”

Rules, participants, and executive politics in state tax revenue forecasting

“…After studying revenue forecast error in the State of Virginia, Jonas, Rest, and Atkinson (1992) reported the findings of a Joint Legislative Review and Audit Commission (JLARC) that was created to investigate the source of forecast error in the state. JLARC found that most of the forecast error in Virginia was a result of declining economic conditions rather than poor forecasting.…”

The dynamics of government revenue forecasting from an organizational perspective: a review of the literature

“…Concerning forecast accuracy in particular, studies found when there were independent forecasts by competing agencies in a state, forecast accuracy increased (Bretschneider & Gorr, 1987;Bretschneider et al, 1989). Other studies that lent support to the desirable attributes of consensus forecasting include the study of Virginia's forecasting experience by Jonas et al (1992) and the study of revenue forecasting in Michigan by Hass and Knittel (1998). Some studies, however, were not consistent with the positive relationship between forecast accuracy and the use of outside advisors or consultants, and between forecast accuracy and competitive forecasts (Forrester, 1993;Mocan & Azad, 1995;Schroeder, 1982).…”

“…A number of studies have emphasized the importance of quality information in forecasts, which relies on cooperation among all the agencies involved. For instance, Jonas et al (1992) found Virginia's state revenue forecasting process and forecast accuracy could be improved by providing more timely information and sharing the information more consistently between the executive branch, the legislature, and the public. Over-dependence on information from outside sources, on the other hand, was found to be detrimental to state budget officials' learning (Klay, 1985).…”

“…A review of the literature on government revenue forecasting in the two directions suggested by Bretschneider and Gorr indicates that the attention devoted to forecasting methodologies far outweighs the attention given to revenue forecasting as an organizational activity. Even on the organizational dimension, forecasting methodology occupies an ------------------ Copyright © 2005 by PrAcademics Press important position and accuracy has been the primary, if not sole, criterion in selecting forecasting methods (Auerbach, 1999;Bretschneider & Gorr, 1987;Hass & Knittel, 1998;Jonas, Rest, & Atkinson, 1992;Rubin, Mantell & Peters, 1999;Shkurti, 1990;Shkurti & Winefordner, 1989;Yokum & Armstrong, 1995). Only a limited number of studies looked at other aspects, such as organizational learning (Klay, 1985;, being informative to forecast users, and being relatively inexpensive and easy to use (Agostini, 1991).…”