The relationship between economic characteristics and alternative annual earnings persistence measures

Accounting researchers (and potentially others) generally select rather simple, lower-order, time-series models to develop proxies for earnings persistence. However, measures of persistence produced by such models are not related to characteristics of the firm's economic environment that are expected to influence earnings persistence. Using a sample of 162 calendar year-end New York Stock Exchange firms, we document the cross-sectional relations between a set of relatively constant, firm-specific, economic characteristics that are theoretical determinants of persistence and measures of earnings persistence derived from both lower-order and higher-order Auto-regressive, Integrated, Moving-Average (ARIMA) models. When lower-order ARIMA models are used to generate measures of earnings persistence, the cross-sectional regression models measuring the association between persistence and economic determinants of persistence yield very low adjusted R(2)s. In sharp contrast, when differenced, higher-order ARIMA models are used to measure earnings persistence, adjusted R(2)s are in the 10-12 percent range. Moreover, independent variables such as capital intensity, barriers-to-entry, and product-type are all significant in the directions suggested by economic theory. Our results are consistent with Lipe and Kormendi (1994) who argue that higher-order ARIMA models do a better job of capturing the value-relevance of current period earnings than lower-order models.