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We conclude with an application comparing the performance of density forecasts produced by a linear Phillips curve model of inflation versus a Markov-switching Phillips curve,
Our results show the following: (a) Multivariate normality is re- jected; (b) multivariate residual checks suggest temporal insta- bilities for both the unconditional and
In particular, on the basis of the conditional probabilities of quantile exceedances (Kendall’s tau), it generates the tail-dependence tests (the concor- dance test) that can be used
Whereas sampling schemes for multiple change-point models draw the break dates conditional on all parameters, in a mixture innovation approach most parameters can be treated as
Conventional random-coefficients models of conditional brand choice using panel data ignore the de- pendence of the random-coefficients distribution on the purchase frequencies..
Again, unknown conditional densities in the efcient score function, in this case (15), are estimated using kernel techniques, and this estimation does not affect the as-
Progress in Bayesian posterior computation due to Markov chain Monte Carlo (MCMC) methods has made it possible to t increasingly complex statistical models and entailed the wish
We provide numerically reliable analytical expressions for the score, Hessian, and information matrix of conditionally heteroscedastic dynamic regression models when the
