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Modelling non‐normal first‐order autoregressive time series
Author(s) -
Sim C. H.
Publication year - 1994
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980130403
Subject(s) - autoregressive model , star model , series (stratigraphy) , autoregressive integrated moving average , mathematics , bivariate analysis , laplace distribution , marginal distribution , laplace transform , econometrics , univariate , computer science , time series , statistics , multivariate statistics , random variable , mathematical analysis , paleontology , biology
We shall first review some non‐normal stationary first‐order autoregressive models. The models are constructed with a given marginal distribution (logistic, hyperbolic secant, exponential, Laplace, or gamma) and the requirement that the bivariate joint distribution of the generated process must be sufficiently simple so that the parameter estimation and forecasting problems of the models can be addressed. A model‐building approach that consists of model identification, estimation, diagnostic checking, and forecasting is then discussed for this class of models.