Forecasting accuracy of behavioural models for participation in the arts
This paper assesses the forecasting performance of count data models applied to arts attendance. We estimate participation models for two artistic activities that differ in their degree of popularity  museums and jazz concerts  with data derived from the 2002 release of the Survey of Public Participation in the Arts for the United States. We estimate a finite mixture model  a zero-inflated negative binomial model  that allows us to distinguish between true non-attendants and goers and their respective behaviour regarding participation in the arts. We evaluate the predictive (in-sample) and forecasting (out-of-sample) accuracy of the estimated model using bootstrapping techniques to compute the Brier score. Overall, the results indicate the model performs well in terms of forecasting. Finally, we draw certain policy implications from the models forecasting capacity, thereby allowing the identification of target populations.
forecasting count data bootstrapping brier scores cultural participation