Lag length selection in panel autoregression
Model selection by BIC is well known to be inconsistent in the presence of incidental parameters. This article shows that, somewhat surprisingly, even without fixed effects in dynamic panels BIC is inconsistent and overestimates the true lag length with considerable probability. The reason for the i...
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sg-smu-ink.soe_research-28932017-07-17T08:17:15Z Lag length selection in panel autoregression HAN, Chirok Peter C. B. PHILLIPS, SUL, Donggyu Model selection by BIC is well known to be inconsistent in the presence of incidental parameters. This article shows that, somewhat surprisingly, even without fixed effects in dynamic panels BIC is inconsistent and overestimates the true lag length with considerable probability. The reason for the inconsistency is explained, and the probability of overestimation is found to be 50% asymptotically. Three alternative consistent lag selection methods are considered. Two of these modify BIC, and the third involves sequential testing. Simulations evaluate the performance of these alternative lag selection methods in finite samples. 2017-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1893 info:doi/10.1080/07474938.2015.1114313 https://ink.library.smu.edu.sg/context/soe_research/article/2893/viewcontent/LagLengthSelectionPanelAutoReg_2017_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University BIC dynamic panel lag selection sequential testing X-differencing C33 Econometrics |
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BIC dynamic panel lag selection sequential testing X-differencing C33 Econometrics HAN, Chirok Peter C. B. PHILLIPS, SUL, Donggyu Lag length selection in panel autoregression |
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Model selection by BIC is well known to be inconsistent in the presence of incidental parameters. This article shows that, somewhat surprisingly, even without fixed effects in dynamic panels BIC is inconsistent and overestimates the true lag length with considerable probability. The reason for the inconsistency is explained, and the probability of overestimation is found to be 50% asymptotically. Three alternative consistent lag selection methods are considered. Two of these modify BIC, and the third involves sequential testing. Simulations evaluate the performance of these alternative lag selection methods in finite samples. |
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HAN, Chirok Peter C. B. PHILLIPS, SUL, Donggyu |
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HAN, Chirok Peter C. B. PHILLIPS, SUL, Donggyu |
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HAN, Chirok |
title |
Lag length selection in panel autoregression |
title_short |
Lag length selection in panel autoregression |
title_full |
Lag length selection in panel autoregression |
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Lag length selection in panel autoregression |
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Lag length selection in panel autoregression |
title_sort |
lag length selection in panel autoregression |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/soe_research/1893 https://ink.library.smu.edu.sg/context/soe_research/article/2893/viewcontent/LagLengthSelectionPanelAutoReg_2017_afv.pdf |
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