Working Paper: NBER ID: w17424
Authors: Yuriy Gorodnichenko; Anna Mikusheva; Serena Ng
Abstract: 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. \n \nCritical 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.
Keywords: quasidifferencing; nonlinear estimator; asymptotic normality; autoregressive models; persistent data
JEL Codes: C22; C32; E27; E37; G17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| QD estimators (C51) | t-consistent estimators (C51) |
| QD estimators (C51) | uniformly asymptotically normal estimators (C51) |
| uniformly bounded moments (C46) | QD estimators achieve asymptotic normality (C51) |
| QD estimators (C51) | reliable estimates (C13) |
| QD framework (C22) | accommodate DSGE models (E13) |