Detecting an innovative outlier in a set of time series
Tests for an innovative outlier affecting every member of a set of autoregressive time series at the same time point are developed. In one model, the outliers are represented as independent random effects; likelihood ratio tests are derived for this case and simulated critical values are tabulated. In a second model, assuming that the size of the outlier is the same in each series, a standard regression framework can be used and correlations between the series are introduced. Simulation studies show that approximate critical values obtained from the ?12 distribution work well for heteroscedastic independent series and for the case of equal correlations between each pair of series.
innovative outliers time series autoregressive model