Estimators for persistent and possibly non-stationary data with classical properties
By: Yuriy Gorodnichenko, Anna Mikusheva, Serena Ng
Contributors: Mikusheva, Anna; Ng, Serena; National Bureau of Economic Research
Series: NBER working paper series -- working paper 17424; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 17424.
Publisher: National Bureau of Economic Research
Published: 2011
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Description: "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.
Notes: Title from PDF file as viewed on 12/1/2011.

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