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This paper proposes a model selection test statistic for the choice between an AR(1) and an MA(1) model. It is a function of the first two sample autocorrelations of a time series. This establishes that it can be compared directly with a statistic given in Burke, Godfrey and Tremayne (1990). From Mo
A recurring issue in modeling seasonal time series variables is the choice of the most adequate model for the seasonal movements. One selection method for quarterly data is proposed in Hylleberg et al. (1990). Market response models are often constructed for bimonthly variables, and hence the topic
This paper proposes a general-to-simple test procedure for the presence of seasonal patterns in time series, which is based on tests for parameter restrictions in a general periodic model. The method is illustrated for the U.K. stock price index and the U.S. CLI index.
The article discusses the use of some Monte Carlo experiments to investigate the effects of dynamic specification on the size and power of three cointegration tests. The first test, proposed by Engle and Granger (1987), is the residual augmented Dickey-Fuller unit root test. The second is a Wald tes
A common characteristic of diagnostic measures on influential observations is the assumption that all relevant regressors are included in the model, and that none of them can be deleted. We review and illustrate a method to detect data points which are influential enough to establish the empirical (
The article discusses some aspects of the error correction model for the Norwegian consumption function proposed in Brodin and Nymoen (1991). The main focus is on the pursued simplification process since the simplified model contains an error correcting variable that includes a contemporaneous varia
The central issue in the application of econometric and time series analysis (ETS) to market response models is the model-building process. The author proposes a specification strategy for ETS modeling and applies it to the primary demand for beer in The Netherlands.
Moving average filtering a stationary AR(1) time series yields higher valued first order autocorrelations. Its implications for unit root testing in seasonally (un-) adjusted time series are evaluated theoretically, via simulations, as well as with an empirical example.
Model selection can involve several variables and selection criteria. A simple method to detect observations possibly influential for model selection is proposed. The potentials of this method are illustrated with three examples, each of which is taken from related studies.
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