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SEMI‐PARAMETRIC ESTIMATION OF LINEAR COINTEGRATING MODELS WITH NONLINEAR CONTEMPORANEOUS ENDOGENEITY
Author(s) -
Sun Yiguo
Publication year - 2014
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12075
Subject(s) - mathematics , estimator , endogeneity , asymptotic distribution , monte carlo method , ordinary least squares , econometrics , instrumental variable , parametric statistics , linear model , statistics
This article considers linear cointegrating models with unknown nonlinear short‐run contemporaneous endogeneity. Two estimators are proposed to estimate the linear cointegrating parameter after the nonlinear endogenous component is estimated by local linear regression approach. Both the proposed estimators are shown to have the same mixed normal limiting distribution with zero mean and smaller asymptotic variance than the fully modified ordinary least squares and instrumental variables estimators. Monte Carlo simulations are used to evaluate the finite sample performance of our proposed estimators, and an empirical application is also included.