Linear programming-based estimators in nonnegative autoregression

• Main Author: PREVE, Daniel P. A.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Robust estimation; Linear programming estimator; Strong convergence; Nonlinear nonnegative autoregression; Dependent non-identically distributed errors; Heavy-tailed errors; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2330; https://ink.library.smu.edu.sg/context/soe_research/article/3329/viewcontent/AMES2014_330.pdf
• Institution: Singapore Management University

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.