Publication Type

Journal Article

Version

submittedVersion

Publication Date

12-2015

Abstract

This note studies robust estimation of the autoregressive (AR) parameter in a nonlinear, nonnegative AR model. It is shown that a linear programming estimator (LPE), considered by Nielsen and Shephard (2003) among others, remains consistent under severe model misspecification. Consequently, the LPE can be used to seek sources of misspecification and to isolate certain trend, seasonal or cyclical components. Simple and quite general conditions under which the LPE is strongly consistent in the presence of heavy-tailed, serially correlated, heteroskedastic disturbances are given, and a brief review of the literature on LP-based estimators in nonnegative autoregression is presented. Finite-sample properties of the LPE are investigated in a small scale simulation study.

Keywords

Robust estimation, Linear programming estimator, Strong convergence, Nonlinear nonnegative autoregression, Dependent non-identically distributed errors, Heavy-tailed errors

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Banking and Finance

Volume

61

Issue

2

First Page

S225

Last Page

S234

ISSN

0378-4266

Identifier

10.1016/j.jbankfin.2015.08.010

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.jbankfin.2015.08.010

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