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ESTIMATION FOR REGRESSIVE AND AUTOREGRESSIVE MODELS WITH NON‐NEGATIVE RESIDUAL ERRORS
Author(s) -
HongZhi An,
Fuchun Huang
Publication year - 1993
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/j.1467-9892.1993.tb00136.x
Subject(s) - autoregressive model , mathematics , estimator , setar , residual , star model , rate of convergence , least squares function approximation , convergence (economics) , statistics , econometrics , generalized least squares , estimation , autoregressive integrated moving average , algorithm , computer science , time series , economics , computer network , channel (broadcasting) , economic growth , management
. The parameter estimation problems for regressive and autoregressive models are investigated. A new procedure is proposed which differs from the least squares method. Theorems relating to the rate of almost sure convergence of the new estimators are formulated. Some simulation results are also shown. With these convergent rates and simulation results a clear comparison of the new estimator with the least squares estimator is obtained.