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Lag Length criteria
VAR Lag Order Selection Criteria
Endogenous variables: OIL EXCH R RPI LUNEMP GDP
Exogenous variables: C
Date: 04/10/12 Time: 09:40
Sample: 1980Q1 2011Q4
Included observations: 90
Lag
LogL
LR
FPE
AIC
SC
HQ
0
-1297.398
NA
152838.1
28.96439
29.13105
29.03160
1
-629.2589
1232.345
0.121419
14.91686
16.08344
15.38730
2
-546.9702
140.8050
0.043857
13.88823
16.05473*
14.76189*
3
-502.2073
70.62590
0.037065
13.69350
16.85992
14.97039
4
-469.5335
47.19560
0.042007
13.76741
17.93376
15.44753
5
-424.0268
59.66430
0.037071
13.55615
18.72242
15.63950
6
-377.6019
54.67826
0.033616
13.32449
19.49068
15.81106
7
-312.0832
68.43057
0.021261
12.66852
19.83464
15.55832
8
-247.3143
59.01173*
0.014906*
12.02921*
20.19525
15.32223
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
Fig - Table of Lag Order Selection Criteria from Eviews
From Fig it can be seen that the AIC statistic minimises at a lag length of 8. Therefore, according to Akaike, this should be the lag length which is used in the VAR model. However it can also be seen that the SC and HQC both minimise at the lag length 2. As the lag length 2 satisfies more than one criterion, and therefore the majority of criteria assessed in this paper, this length will be chosen.This length should produce the best goodness of fit for the VAR model.
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