The maximum likelihood estimation of dynamic demand models has usually been based on the likelihood function conditional on the first observations of the dependent variables. However, this neglects information which may be necessary for identifying the long-run structure. We formulate the...

Logistic smooth transition and Markov switching autoregressive models of a logistic transform of the monthly US unemployment rate are estimated by Markov chain Monte Carlo methods. The Markov switching model is identified by constraining the first autoregression coefficient to differ across...

Efficient posterior simulators for two GARCH models with generalized hyperbolic disturbances are presented. The first model, GHt-GARCH, is a threshold GARCH with a skewed and heavy-tailed error distribution; in this model, the latent variables that account for skewness and heavy tails are...

The small sample critical values of three test statistics for vector autocorrelated errors are investigated in the context of several specific empirical models. Under the null hypothesis, two of the proposed statistics do not depend on nuisance parameters when the regressors are strongly...

Demographic effects and user costs in demand systems have usually been modelled explicitly. A more robust approach is a state space formulation of the demand system, where time-varying intercepts account for the effects of unobservable variables. The author embeds such a system in a vector...