English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We prove the asymptotic properties of the maximum likelihood estimator (MLE)in time-varying transition probability (TVTP) regime-switching models. Thisclass of models extends the constant regime transition probability inMarkov-switching models to a time-varying probability by including informationfrom observations. An important feature in this proof is the mixing rate of theregime process conditional on the observations, which is time varying owing tothe time-varying transition probabilities. Consistency and asymptotic normalityfollow from the almost deterministic geometrically decaying bound of the mixingrate. The assumptions are verified in regime-switching autoregressive modelswith widely-applied TVTP specifications. A simulation study examines thefinite-sample distributions of the MLE and compares the estimates of theasymptotic variance constructed from the Hessian matrix and the outer productof the score. The simulation results favour the latter. As an empiricalexample, we compare three leading economic indicators in terms of describingU.S. industrial production.

Asymptotic Properties of the Maximum Likelihood Estimator in Regime-Switching Models with Time-Varying Transition Probabilities