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"This paper considers a moments based non-linear estimator that is root-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, as well as certain non-linear dynamic models. Asymptotic normality is obtained because the moments are chosen so that the objective function is uniformly bounded in probability and 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"--National Bureau of Economic Research web site.
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Estimators for persistent and possibly non-stationary data with classical properties
2011, National Bureau of Economic Research
Electronic resource
in English
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Title from PDF file as viewed on 12/1/2011.
Includes bibliographical references.
Also available in print.
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.
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October 17, 2020 | Edited by MARC Bot | import existing book |
December 7, 2011 | Created by LC Bot | import new book |