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Sample Moments and Weak Convergence to Multivariate Stochastic Power Integrals
Author(s) -
Sandberg Rickard
Publication year - 2017
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.12250
Subject(s) - mathematics , multivariate statistics , univariate , estimator , generalization , sample (material) , convergence (economics) , statistics , nonlinear system , mathematical analysis , chemistry , physics , chromatography , economic growth , quantum mechanics , economics
This work considers sample moments arising from least squares, least absolute deviation, and extremum estimators of linear and nonlinear multivariate systems with I(1) regressors. The sample moments are shown to converge weakly to multivariate stochastic power integrals, and these results can be considered as a multivariate generalization of the univariate results reported earlier.