ESTIMATORS FOR PERSISTENT AND POSSIBLY NONSTATIONARY DATA WITH CLASSICAL PROPERTIES

Yuriy Gorodnichenko, Anna Mikusheva and Serena Ng ()

Econometric Theory, 2012, vol. 28, issue 5, 1003-1036

Abstract: This paper considers a moments-based nonlinear estimator that is $\root \of T $-consistent and uniformly asymptotically normal irrespective of the degree of persistence of the forcing process. These properties hold for linear autoregressive models, linear predictive regressions, and certain nonlinear dynamic models. Asymptotic normality is obtained because the moments are chosen so that the objective function is uniformly bounded in probability and so that a central limit theorem can be applied. Critical values from the normal distribution can be used irrespective of the treatment of the deterministic terms. Simulations show that the estimates are precise and the t-test has good size in the parameter region where the least squares estimates usually yield distorted inference.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
Working Paper: Estimators for Persistent and Possibly Non-Stationary Data with Classical Properties (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:28:y:2012:i:05:p:1003-1036_00

Access Statistics for this article

More articles in Econometric Theory from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().