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EFFICIENT METHOD OF MOMENTS ESTIMATORS FOR INTEGER TIME SERIES MODELS
Author(s) -
Martin Vance L.,
Tremayne Andrew R.,
Jung Robert C.
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.12078
Subject(s) - mathematics , estimator , series (stratigraphy) , moment (physics) , autoregressive model , negative binomial distribution , likelihood function , monte carlo method , statistics , integer (computer science) , poisson distribution , convolution (computer science) , efficient estimator , maximum likelihood , minimum variance unbiased estimator , computer science , paleontology , biology , programming language , physics , classical mechanics , machine learning , artificial neural network
The parameters of integer autoregressive models with Poisson, or negative binomial innovations can be estimated by maximum likelihood where the prediction error decomposition, together with convolution methods, is used to write down the likelihood function. When a moving average component is introduced this is not the case. To address this problem an efficient method of moment estimator is proposed where the estimated standard errors for the parameters are obtained using subsampling methods. The small sample properties of the estimator are investigated using Monte Carlo methods, while the approach is demonstrated using two well‐known examples from the time series literature.