Which Lag Length Selection Criteria Should We Employ?
Estimating the lag length of autoregressive process for a time series is a crucial econometric exercise in most economic studies. This study attempts to provide helpfully guidelines regarding the use of lag length selection criteria in determining the autoregressive lag length. The most interesting...
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| Main Author: | |
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| Format: | E-Article |
| Language: | English |
| Published: |
Economics Bulletin
2004
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| Online Access: | http://ir.unimas.my/id/eprint/1865/1/Which%2BLag%2BLength%2BSelection%2BCriteria%2BShould%2BWe%2BEmploy%2B%2528abstract%2529%20%281%29%20%281%29.pdf http://ir.unimas.my/id/eprint/1865/ |
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| Summary: | Estimating the lag length of autoregressive process for a time series is a crucial econometric exercise in most economic studies. This study attempts to provide helpfully guidelines regarding the use of lag length selection criteria in determining the autoregressive lag length. The most interesting finding of this study is that Akaike’s information criterion (AIC) and final prediction error (FPE) are superior than the other criteria under study in the case of small sample (60 observations and below), in the manners that they minimize the chance of under estimation while maximizing the chance of recovering the true lag length. One immediate econometric implication of this study is that as most economic sample data can seldom be considered “large” in size, AIC and FPE are recommended for the estimation the autoregressive lag length. |
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