An international study of persistence in output: Parametric estimates using the data dependent systems approach

This paper parametrically obtains estimates of persistence in output using Pandit's (1977, 1982) Data Dependent Systems approach for modelling autoregressive and moving average processes. The estimates are based on over a century of annual data for the rate of change of output in nine industrialized countries. The sensitivity of estimates to various model selection criteria is examined. While persistence in output is found to be sensitive to model selection criteria, the output of all countries including the United States is found to have a substantial degree of persistence if the ARMA models are chosen according to the Schwarz Bayesian Criterion, but excluding the ARMA models whose moving average roots are near the unit root (which involves pile-up phenomenon). Moreover, the parametric estimates of persistence are shown not to have the known upward bias problem commonly associated with parametric estimates of persistence relative to nonparametric estimates.