Nonstationary discrete choice

This paper develops an asymptotic theory for time series discrete choice models with explanatory variables generated as integrated processes and with multiple choices and threshold parameters determining the choices. The theory extends recent work by Park and Phillips (Econometrica 68 (2000) 1249) on binary choice models. As in this earlier work, the maximum likelihood estimator is consistent and has a limit theory with multiple rates of convergence (n3/4 and n1/4) and mixture normal distributions where the mixing variates depend on Brownian local time as well as Brownian motion. An extended arc sine limit law is given for the sample proportions of the various choices. The new limit law exhibits a wider range of potential behavior that depends on the values taken by the threshold parameters. The approach is applied to model the empirical behavior of overnight target rate adjustments by the Bank of Canada over 1996–2002.