Consistent Underestimation Bias, the Asymmetrical Loss Function, and Homogeneous Sources of Bias in State Revenue Forecasts

By Voorhees, William R. | Journal of Public Budgeting, Accounting & Financial Management, Spring 2006 | Go to article overview

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


ABSTRACT.

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.

INTRODUCTION

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. …

The rest of this article is only available to active members of Questia

Sign up now for a free, 1-day trial and receive full access to:

  • Questia's entire collection
  • Automatic bibliography creation
  • More helpful research tools like notes, citations, and highlights
  • Ad-free environment

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items

Items saved from this article

This article has been saved
Highlights (0)
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

Citations (0)
Some of your citations are legacy items.

Any citation created before July 30, 2012 will labeled as a “Cited page.” New citations will be saved as cited passages, pages or articles.

We also added the ability to view new citations from your projects or the book or article where you created them.

Notes (0)
Bookmarks (0)

You have no saved items from this article

Project items include:
  • Saved book/article
  • Highlights
  • Quotes/citations
  • Notes
  • Bookmarks
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited article

Consistent Underestimation Bias, the Asymmetrical Loss Function, and Homogeneous Sources of Bias in State Revenue Forecasts
Settings

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Full screen

matching results for page

Cited passage

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited passage

Welcome to the new Questia Reader

The Questia Reader has been updated to provide you with an even better online reading experience.  It is now 100% Responsive, which means you can read our books and articles on any sized device you wish.  All of your favorite tools like notes, highlights, and citations are still here, but the way you select text has been updated to be easier to use, especially on touchscreen devices.  Here's how:

1. Click or tap the first word you want to select.
2. Click or tap the last word you want to select.

OK, got it!

Thanks for trying Questia!

Please continue trying out our research tools, but please note, full functionality is available only to our active members.

Your work will be lost once you leave this Web page.

For full access in an ad-free environment, sign up now for a FREE, 1-day trial.

Already a member? Log in now.