Methods of Improving Recreation Projections

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Abstract
Previous efforts to project participation in outdoor recreation activities assume to be constant the rates of participation over time by various socioeconomic and demographic segments of society. Participatory data is becoming more available however, particularly at the state and federal levels. There will soon be sufficient data for several recreation activities to base projections on time series data, which has the advantage of allowing one to incorporate change in participation levels over time into projection models. Time series models also allow the researcher to draw causal inferences with much more certainty than single-point models. The knowledge of causal relationships to participation are important to recreation management agencies, which have some control over such causal factors as level of supply, access, and participation costs, and can attempt to modify the level of participation from the level forecasted by modifying one or more causally related variables. The authors develop and illustrate time series models by using state-wide data on hunting and fishing license sales for the period from 1950 to 1970, and also illustrate how a management agency might manipulate exogenous variables over which it has control to modify projected sales levels.

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