Model_Comparison.pdf - Model Comparison November 2 2016...

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Model Comparison November 2, 2016 Model Comparison and Averaging In applications, there is no true model for a given time series. All statistical models are approximations used to describe the dynamic dependence of the data. It is then common to see that several models fit a given data set well, and the question of model comparison arises. In this section, we discuss two methods to compare time series models. These methods are statistical criteria. They are designed to provide guidances in selecting a model. It should also be kept in mind, however, that the objective of data analysis and substantive information of the problem at hand are also important in model selection. 1. In-sample Comparison. If the objective of data analysis is to gain insight into the dynamic structure of a time series, then one can use in-sample measurement to compare di ff erent models. By in-sample, we mean that all data are used in model estimation and comparison. In this case, information criteria, such as AIC and BIC, and the estimate of residual variance can be used for model comparison. For a selected criterion, the model with a smaller value is preferred.
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