Inverse Gaussian Autoregressive Models
Copyright policy )Inverse Gaussian Autoregressive Models
A first‐order autoregressive process with one‐dimensional inverse Gaussian marginals is introduced. The innovation distributions are obtained in certain special cases. The unknown parameters are estimated using different methods and these estimators are shown to be consistent and asymptotically normal. Performance of the estimators is discussed using simulation experiments.
- University of Waterloo Canada
Time series, auto-correlation, regression, etc. in statistics (GARCH), inverse Gaussian distribution, asymptotic normality, empirical Laplace transform, Asymptotic properties of parametric estimators, first order autoregression, heavy tails
Time series, auto-correlation, regression, etc. in statistics (GARCH), inverse Gaussian distribution, asymptotic normality, empirical Laplace transform, Asymptotic properties of parametric estimators, first order autoregression, heavy tails
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