The Demand for Higher Education and Institutional Enrollment Forecasting

Hoenack, Stephen A.; Weiler, William C.

doi:10.1111/j.1465-7295.1979.tb00297.x

Cited by 57 publications

(18 citation statements)

References 18 publications

(3 reference statements)

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“…We used the variance-covariance matrix of the estimated coefficients and the standard error of estimate for the linear regressions (for each enrollment category) corresponding to the last iteration of the estimation algorithm. It is often employed to produce estimates of forecast errors in multiequation econometric models as, for example, in [6,9]. Each set of forecasts was computed by drawing random values of the estimated parameters consistent with the estimated variance-covariance matrix of the coefficients and a random value of the residual consistent with the standard error of estimate.…”

Section: Resultsmentioning

confidence: 99%

“…Compare the percentage errors in Part B of Table 4 in [6] to those in Table 2 of this paper. Compare the percentage errors in Part B of Table 4 in [6] to those in Table 2 of this paper.…”

Section: Forecasts and Forecast Errorsmentioning

confidence: 92%

“…3See Part B of Table 4 in Hoenack and Weiler [6] for the forecast errors in their model. influence enrollments such as attendance costs and labor market conditions do not vary across the sample period (from spring to the beginning of the fall term).…”

Section: Jfe William C Weilermentioning

confidence: 99%

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A Model for Short-Term Institutional Enrollment Forecasting

Weiler¹

1980

The Journal of Higher Education

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Ohio State University Pressis collaborating with JSTOR to digitize, preserve and extend access to The Journal of Higher Education This content downloaded from 150.135.239.97 on Tue, 07 Jun 2016 18:45:19 UTC All use subject to http://about.jstor.org/terms I wish to thank David J. Berg, Colleen T. Davidson, Stephen A. Hoenack, William S. Reece, and two anonymous referees for comments on earlier drafts of this paper. 'Studies of enrollment demand behavior over the past fifteen years have provided estimates of the effects of short-term labor market conditions, financial aid policies, and attendance costs on enrollments. Many of the studies analyzing the effects of costs are reviewed in [8] and [12]. Richard Freeman has produced seminal research on the effects of labor market conditions on attendance, much of which is summarized in [4]. Hoenack and Weiler apply a model of enrollment demand to enrollment forecasting at the University of Minnesota in [6]. William C. Weiler is associate director, Management

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Section: Resultsmentioning

confidence: 99%

“…Compare the percentage errors in Part B of Table 4 in [6] to those in Table 2 of this paper. Compare the percentage errors in Part B of Table 4 in [6] to those in Table 2 of this paper.…”

Section: Forecasts and Forecast Errorsmentioning

confidence: 92%

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A Model for Short-Term Institutional Enrollment Forecasting

Weiler¹

1980

The Journal of Higher Education

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“…Weiler (1987a) demonstrates how to design and use enrollment demand models, that is, models that incorporate the variables thought to influence the demand for enrollment systematically (see also Weiler, 1984). In a similar vein, Hoenack and Weiler (1979) develop a complex causal model for long-term enrollment forecasts. They focus on price and labor-market variables that influ-ence demand for higher education and demonstrate procedures for calculating confidence intervals for an enrollment forecast.…”

Section: Alternative Methodologies For Enrollment Forecastingmentioning

confidence: 99%

Methods and Techniques of Enrollment Forecasting

Brinkman¹,

McIntyre²

1997

New Drctns for Instit Rsrch

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Enrollment forecasters have much to consider. A combination of quantitative and qualitative methods can sometimes be the best approach. Methods and Techniques of Enrollment Forecasting Paul T. Brinkman, Chuck McIntyreInstitutional researchers are often called on to conduct or assist with enrollment forecasts. Occasionally, personnel in institutional-planning offices, in state agencies, or in the marketing offices of continuing education divisions will be called on to produce such forecasts. Enrollment forecasts are fundamental elements of planning and budgeting at any higher education institution that depends on student enrollments or at any agency or organization that has responsibilities for supporting those institutions. Numerous institutional policy issues are related to enrollment forecasts-tuition policy, budget forecasting, faculty staffing, institutional closure or consolidation, and optimizing objectives related to the size and composition of enrollment (Weiler, 1987a).There is no one right way to forecast enrollment. In this chapter, we discuss a variety of approaches and associated issues. We relate choices of method or technique to specific circumstances and situations in an effort to provide some guidance to readers in choosing their own approaches. We focus on forecasting institutional enrollments. Factors Affecting EnrollmentNo one factor determines enrollments at a college or university. For the economist, enrollment will be determined by the intersection of measured supplyand-demand curves. For the demographer, enrollments are related to numbers of people and where they are located. For the higher education administrator, enrollment is determined by the combined effects of many manageable and unmanageable factors, categories that are roughly, though not completely, equivalent to supply and demand. In addition, there is always the possibility that a

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“…For example, enrollment managers and admissions offices often rely on theoretical and empirical research on student price responsiveness to design admission and financial aid policies (Breneman, Doti, and Lapovski, 2001;DesJardins, 2002;DesJardins, Dundar, and Hendel, 1999;Hoenack and Weiler, 1979;Hossler, 1984;St. John, Asker, and Hu, 2001;Thomas, Dawes, and Reznik, 2001).…”

Section: Introductionmentioning

confidence: 99%

What Can Institutional Research do to Help Colleges Meet the Workforce Needs of States and Nations?

Toutkoushian

2005

Res High Educ

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The Demand for Higher Education and Institutional Enrollment Forecasting

Cited by 57 publications

References 18 publications

A Model for Short-Term Institutional Enrollment Forecasting

A Model for Short-Term Institutional Enrollment Forecasting

Methods and Techniques of Enrollment Forecasting

What Can Institutional Research do to Help Colleges Meet the Workforce Needs of States and Nations?

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