CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
New results on asymptotic properties of likelihood estimators with persistent data for small and large T
Authors
A Juodis
V Sarafidis
Publication date
3 August 2023
Publisher
Springer Nature on behalf of the Spanish Economic Association
Doi
Abstract
Data Availability Statement: Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.Copyright © The Author(s) 2023. This paper revisits the panel autoregressive model, with a primary emphasis on the unit-root case. We study a class of misspecified Random effects Maximum Likelihood (mRML) estimators when T is either fixed or large, and N tends to infinity. We show that in the unit-root case, for any fixed value of T, the log-likelihood function of the mRML estimator has a single mode at unity as N→ ∞ . Furthermore, the Hessian matrix of the corresponding log-likelihood function is non-singular, unless the scaled variance of the initial condition is exactly zero. As a result, mRML is consistent and asymptotically normally distributed as N tends to infinity. In the large-T setup, it is shown that mRML is asymptotically equivalent to the bias-corrected FE estimator of Hahn and Kuersteiner (Econometrica 70(4):1639–1657, 2002). Moreover, under certain conditions, its Hessian matrix remains non-singular
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Sustaining member
Brunel University Research Archive
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:bura.brunel.ac.uk:2438/271...
Last time updated on 14/09/2023