DECIDING BETWEEN I(1) AND I(0)

A class of procedures that consistently classify the stochastic compon ent of a time series as being integrated either of order zero [I(0)] o r one [I(1)] are proposed for general I(0) or I(1) processes and polyn omial or piecewise linear detrending. Large-sample Bayesian inference is free of nuisance parameters describing short-run dynamics and requi res specifying priors only on the point hypotheses 'I(0)' and 'I(1)' t hereby avoiding problematic choices of parametric priors over roots an d nuisance parameters. Applied to the Nelson-Plosser (1982) data with linear detrending, these procedures largely support Nelson and Plosser 's original inferences. With piecewise-linear detrending these data ar e typically uninformative, producing Bayes ratios close to one.