Modelling Methodology and Forecast Failure.
We analyse by simulation the impact of model-selection strategies (sometimes called pre-testing) on forecast performance in both constant- and non-constant-parameter processes. Restricted, unrestricted and selected models are compared when either of the first two might generate the data. We find lit...
| 主要な著者: | , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
2002
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| 要約: | We analyse by simulation the impact of model-selection strategies (sometimes called pre-testing) on forecast performance in both constant- and non-constant-parameter processes. Restricted, unrestricted and selected models are compared when either of the first two might generate the data. We find little evidence that strategies such as general-to-specific induce significant over-fitting, or thereby cause forecast-failure rejection rates to greatly exceed nominal sizes. Parameter non-constancies put a premium on correct specification, but in general, model-selection effects appear to be relatively small, and progressive research is able to detect the mis-specifications. |
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