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/

Global: Zendy Tools annual

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

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper investigates the quasi-maximum likelihood inference includingestimation, model selection and diagnostic checking for linear doubleautoregressive (DAR) models, where all asymptotic properties are establishedunder only fractional moment of the observed process. We propose a Gaussianquasi-maximum likelihood estimator (G-QMLE) and an exponential quasi-maximumlikelihood estimator (E-QMLE) for the linear DAR model, and establish theconsistency and asymptotic normality for both estimators. Based on the G-QMLEand E-QMLE, two Bayesian information criteria are proposed for model selection,and two mixed portmanteau tests are constructed to check the adequacy of fittedmodels. Moreover, we compare the proposed G-QMLE and E-QMLE with the existingdoubly weighted quantile regression estimator in terms of the asymptoticefficiency and numerical performance. Simulation studies illustrate thefinite-sample performance of the proposed inference tools, and a real exampleon the Bitcoin return series shows the usefulness of the proposed inferencetools.

Quasi-maximum Likelihood Inference for Linear Double Autoregressive Models