Python-Granger因果关系F测试理解
我正在尝试平稳时间序列的格兰杰因果关系。我很难理解它的自信程度 对于例如1:Python-Granger因果关系F测试理解,python,python-3.x,statistics,time-series,statsmodels,Python,Python 3.x,Statistics,Time Series,Statsmodels,我正在尝试平稳时间序列的格兰杰因果关系。我很难理解它的自信程度 对于例如1: grangercausalitytests(filter_df[['transform_y_x', 'transform_y_y']], maxlag=15) gives result: Granger Causality number of lags (no zero) 1 ssr based F test: F=3.7764 , p=0.0530 , df_denom=286, df_num
grangercausalitytests(filter_df[['transform_y_x', 'transform_y_y']], maxlag=15)
gives result:
Granger Causality
number of lags (no zero) 1
ssr based F test: F=3.7764 , p=0.0530 , df_denom=286, df_num=1
ssr based chi2 test: chi2=3.8161 , p=0.0508 , df=1
likelihood ratio test: chi2=3.7911 , p=0.0515 , df=1
parameter F test: F=3.7764 , p=0.0530 , df_denom=286, df_num=1
Granger Causality
number of lags (no zero) 2
ssr based F test: F=2.1949 , p=0.1133 , df_denom=283, df_num=2
ssr based chi2 test: chi2=4.4673 , p=0.1071 , df=2
likelihood ratio test: chi2=4.4330 , p=0.1090 , df=2
parameter F test: F=2.1949 , p=0.1133 , df_denom=283, df_num=2
Granger Causality
number of lags (no zero) 3
ssr based F test: F=7.5713 , p=0.0001 , df_denom=280, df_num=3
ssr based chi2 test: chi2=23.2818 , p=0.0000 , df=3
likelihood ratio test: chi2=22.3856 , p=0.0001 , df=3
parameter F test: F=7.5713 , p=0.0001 , df_denom=280, df_num=3
Granger Causality
number of lags (no zero) 4
ssr based F test: F=2.3756 , p=0.0523 , df_denom=277, df_num=4
ssr based chi2 test: chi2=9.8113 , p=0.0437 , df=4
likelihood ratio test: chi2=9.6467 , p=0.0468 , df=4
parameter F test: F=2.3756 , p=0.0523 , df_denom=277, df_num=4
Granger Causality
number of lags (no zero) 5
ssr based F test: F=1.4871 , p=0.1941 , df_denom=274, df_num=5
ssr based chi2 test: chi2=7.7338 , p=0.1715 , df=5
likelihood ratio test: chi2=7.6307 , p=0.1778 , df=5
parameter F test: F=1.4871 , p=0.1941 , df_denom=274, df_num=5
Granger Causality
number of lags (no zero) 6
ssr based F test: F=1.2781 , p=0.2675 , df_denom=271, df_num=6
ssr based chi2 test: chi2=8.0363 , p=0.2355 , df=6
likelihood ratio test: chi2=7.9247 , p=0.2437 , df=6
parameter F test: F=1.2781 , p=0.2675 , df_denom=271, df_num=6
Granger Causality
number of lags (no zero) 7
ssr based F test: F=1.7097 , p=0.1067 , df_denom=268, df_num=7
ssr based chi2 test: chi2=12.6378 , p=0.0814 , df=7
likelihood ratio test: chi2=12.3637 , p=0.0892 , df=7
parameter F test: F=1.7097 , p=0.1067 , df_denom=268, df_num=7
Granger Causality
number of lags (no zero) 8
ssr based F test: F=1.4672 , p=0.1692 , df_denom=265, df_num=8
ssr based chi2 test: chi2=12.4909 , p=0.1306 , df=8
likelihood ratio test: chi2=12.2222 , p=0.1416 , df=8
parameter F test: F=1.4672 , p=0.1692 , df_denom=265, df_num=8
Granger Causality
number of lags (no zero) 9
ssr based F test: F=2.0761 , p=0.0320 , df_denom=262, df_num=9
ssr based chi2 test: chi2=20.0400 , p=0.0177 , df=9
likelihood ratio test: chi2=19.3576 , p=0.0223 , df=9
parameter F test: F=2.0761 , p=0.0320 , df_denom=262, df_num=9
Granger Causality
number of lags (no zero) 10
ssr based F test: F=1.8313 , p=0.0556 , df_denom=259, df_num=10
ssr based chi2 test: chi2=19.7977 , p=0.0312 , df=10
likelihood ratio test: chi2=19.1291 , p=0.0387 , df=10
parameter F test: F=1.8313 , p=0.0556 , df_denom=259, df_num=10
Granger Causality
number of lags (no zero) 11
ssr based F test: F=1.8893 , p=0.0410 , df_denom=256, df_num=11
ssr based chi2 test: chi2=22.6493 , p=0.0198 , df=11
likelihood ratio test: chi2=21.7769 , p=0.0262 , df=11
parameter F test: F=1.8893 , p=0.0410 , df_denom=256, df_num=11
Granger Causality
number of lags (no zero) 12
ssr based F test: F=2.0157 , p=0.0234 , df_denom=253, df_num=12
ssr based chi2 test: chi2=26.5779 , p=0.0089 , df=12
likelihood ratio test: chi2=25.3830 , p=0.0131 , df=12
parameter F test: F=2.0157 , p=0.0234 , df_denom=253, df_num=12
Granger Causality
number of lags (no zero) 13
ssr based F test: F=1.8636 , p=0.0347 , df_denom=250, df_num=13
ssr based chi2 test: chi2=26.8434 , p=0.0131 , df=13
likelihood ratio test: chi2=25.6211 , p=0.0191 , df=13
parameter F test: F=1.8636 , p=0.0347 , df_denom=250, df_num=13
Granger Causality
number of lags (no zero) 14
ssr based F test: F=1.5283 , p=0.1013 , df_denom=247, df_num=14
ssr based chi2 test: chi2=23.9090 , p=0.0470 , df=14
likelihood ratio test: chi2=22.9296 , p=0.0614 , df=14
parameter F test: F=1.5283 , p=0.1013 , df_denom=247, df_num=14
Granger Causality
number of lags (no zero) 15
ssr based F test: F=0.9749 , p=0.4823 , df_denom=244, df_num=15
ssr based chi2 test: chi2=16.4815 , p=0.3508 , df=15
likelihood ratio test: chi2=16.0065 , p=0.3816 , df=15
parameter F test: F=0.9749 , p=0.4823 , df_denom=244, df_num=15
grangercausalitytests(filter_df[['transform_y_y', 'transform_y_x']], maxlag=15)
it says:
Granger Causality
number of lags (no zero) 1
ssr based F test: F=70.4932 , p=0.0000 , df_denom=286, df_num=1
ssr based chi2 test: chi2=71.2326 , p=0.0000 , df=1
likelihood ratio test: chi2=63.6734 , p=0.0000 , df=1
parameter F test: F=70.4932 , p=0.0000 , df_denom=286, df_num=1
Granger Causality
number of lags (no zero) 2
ssr based F test: F=47.3519 , p=0.0000 , df_denom=283, df_num=2
ssr based chi2 test: chi2=96.3771 , p=0.0000 , df=2
likelihood ratio test: chi2=83.1351 , p=0.0000 , df=2
parameter F test: F=47.3519 , p=0.0000 , df_denom=283, df_num=2
Granger Causality
number of lags (no zero) 3
ssr based F test: F=33.6081 , p=0.0000 , df_denom=280, df_num=3
ssr based chi2 test: chi2=103.3450, p=0.0000 , df=3
likelihood ratio test: chi2=88.2665 , p=0.0000 , df=3
parameter F test: F=33.6081 , p=0.0000 , df_denom=280, df_num=3
Granger Causality
number of lags (no zero) 4
ssr based F test: F=24.1709 , p=0.0000 , df_denom=277, df_num=4
ssr based chi2 test: chi2=99.8248 , p=0.0000 , df=4
likelihood ratio test: chi2=85.6260 , p=0.0000 , df=4
parameter F test: F=24.1709 , p=0.0000 , df_denom=277, df_num=4
Granger Causality
number of lags (no zero) 5
ssr based F test: F=15.6663 , p=0.0000 , df_denom=274, df_num=5
ssr based chi2 test: chi2=81.4760 , p=0.0000 , df=5
likelihood ratio test: chi2=71.6615 , p=0.0000 , df=5
parameter F test: F=15.6663 , p=0.0000 , df_denom=274, df_num=5
Granger Causality
number of lags (no zero) 6
ssr based F test: F=11.5874 , p=0.0000 , df_denom=271, df_num=6
ssr based chi2 test: chi2=72.8595 , p=0.0000 , df=6
likelihood ratio test: chi2=64.8565 , p=0.0000 , df=6
parameter F test: F=11.5874 , p=0.0000 , df_denom=271, df_num=6
Granger Causality
number of lags (no zero) 7
ssr based F test: F=9.7282 , p=0.0000 , df_denom=268, df_num=7
ssr based chi2 test: chi2=71.9090 , p=0.0000 , df=7
likelihood ratio test: chi2=64.0753 , p=0.0000 , df=7
parameter F test: F=9.7282 , p=0.0000 , df_denom=268, df_num=7
Granger Causality
number of lags (no zero) 8
ssr based F test: F=8.3121 , p=0.0000 , df_denom=265, df_num=8
ssr based chi2 test: chi2=70.7626 , p=0.0000 , df=8
likelihood ratio test: chi2=63.1365 , p=0.0000 , df=8
parameter F test: F=8.3121 , p=0.0000 , df_denom=265, df_num=8
Granger Causality
number of lags (no zero) 9
ssr based F test: F=7.7863 , p=0.0000 , df_denom=262, df_num=9
ssr based chi2 test: chi2=75.1583 , p=0.0000 , df=9
likelihood ratio test: chi2=66.6028 , p=0.0000 , df=9
parameter F test: F=7.7863 , p=0.0000 , df_denom=262, df_num=9
Granger Causality
number of lags (no zero) 10
ssr based F test: F=6.9230 , p=0.0000 , df_denom=259, df_num=10
ssr based chi2 test: chi2=74.8427 , p=0.0000 , df=10
likelihood ratio test: chi2=66.3278 , p=0.0000 , df=10
parameter F test: F=6.9230 , p=0.0000 , df_denom=259, df_num=10
Granger Causality
number of lags (no zero) 11
ssr based F test: F=6.7168 , p=0.0000 , df_denom=256, df_num=11
ssr based chi2 test: chi2=80.5233 , p=0.0000 , df=11
likelihood ratio test: chi2=70.7452 , p=0.0000 , df=11
parameter F test: F=6.7168 , p=0.0000 , df_denom=256, df_num=11
Granger Causality
number of lags (no zero) 12
ssr based F test: F=6.8729 , p=0.0000 , df_denom=253, df_num=12
ssr based chi2 test: chi2=90.6239 , p=0.0000 , df=12
likelihood ratio test: chi2=78.4393 , p=0.0000 , df=12
parameter F test: F=6.8729 , p=0.0000 , df_denom=253, df_num=12
Granger Causality
number of lags (no zero) 13
ssr based F test: F=6.0868 , p=0.0000 , df_denom=250, df_num=13
ssr based chi2 test: chi2=87.6748 , p=0.0000 , df=13
likelihood ratio test: chi2=76.1718 , p=0.0000 , df=13
parameter F test: F=6.0868 , p=0.0000 , df_denom=250, df_num=13
Granger Causality
number of lags (no zero) 14
ssr based F test: F=5.6246 , p=0.0000 , df_denom=247, df_num=14
ssr based chi2 test: chi2=87.9896 , p=0.0000 , df=14
likelihood ratio test: chi2=76.3759 , p=0.0000 , df=14
parameter F test: F=5.6246 , p=0.0000 , df_denom=247, df_num=14
Granger Causality
number of lags (no zero) 15
ssr based F test: F=5.3775 , p=0.0000 , df_denom=244, df_num=15
ssr based chi2 test: chi2=90.9098 , p=0.0000 , df=15
likelihood ratio test: chi2=78.5443 , p=0.0000 , df=15
parameter F test: F=5.3775 , p=0.0000 , df_denom=244, df_num=15
grangercausalitytests(filter_df[['transform_y_x', 'transform_y_y']], maxlag=15)
gives result:
Granger Causality
number of lags (no zero) 1
ssr based F test: F=3.7764 , p=0.0530 , df_denom=286, df_num=1
ssr based chi2 test: chi2=3.8161 , p=0.0508 , df=1
likelihood ratio test: chi2=3.7911 , p=0.0515 , df=1
parameter F test: F=3.7764 , p=0.0530 , df_denom=286, df_num=1
Granger Causality
number of lags (no zero) 2
ssr based F test: F=2.1949 , p=0.1133 , df_denom=283, df_num=2
ssr based chi2 test: chi2=4.4673 , p=0.1071 , df=2
likelihood ratio test: chi2=4.4330 , p=0.1090 , df=2
parameter F test: F=2.1949 , p=0.1133 , df_denom=283, df_num=2
Granger Causality
number of lags (no zero) 3
ssr based F test: F=7.5713 , p=0.0001 , df_denom=280, df_num=3
ssr based chi2 test: chi2=23.2818 , p=0.0000 , df=3
likelihood ratio test: chi2=22.3856 , p=0.0001 , df=3
parameter F test: F=7.5713 , p=0.0001 , df_denom=280, df_num=3
Granger Causality
number of lags (no zero) 4
ssr based F test: F=2.3756 , p=0.0523 , df_denom=277, df_num=4
ssr based chi2 test: chi2=9.8113 , p=0.0437 , df=4
likelihood ratio test: chi2=9.6467 , p=0.0468 , df=4
parameter F test: F=2.3756 , p=0.0523 , df_denom=277, df_num=4
Granger Causality
number of lags (no zero) 5
ssr based F test: F=1.4871 , p=0.1941 , df_denom=274, df_num=5
ssr based chi2 test: chi2=7.7338 , p=0.1715 , df=5
likelihood ratio test: chi2=7.6307 , p=0.1778 , df=5
parameter F test: F=1.4871 , p=0.1941 , df_denom=274, df_num=5
Granger Causality
number of lags (no zero) 6
ssr based F test: F=1.2781 , p=0.2675 , df_denom=271, df_num=6
ssr based chi2 test: chi2=8.0363 , p=0.2355 , df=6
likelihood ratio test: chi2=7.9247 , p=0.2437 , df=6
parameter F test: F=1.2781 , p=0.2675 , df_denom=271, df_num=6
Granger Causality
number of lags (no zero) 7
ssr based F test: F=1.7097 , p=0.1067 , df_denom=268, df_num=7
ssr based chi2 test: chi2=12.6378 , p=0.0814 , df=7
likelihood ratio test: chi2=12.3637 , p=0.0892 , df=7
parameter F test: F=1.7097 , p=0.1067 , df_denom=268, df_num=7
Granger Causality
number of lags (no zero) 8
ssr based F test: F=1.4672 , p=0.1692 , df_denom=265, df_num=8
ssr based chi2 test: chi2=12.4909 , p=0.1306 , df=8
likelihood ratio test: chi2=12.2222 , p=0.1416 , df=8
parameter F test: F=1.4672 , p=0.1692 , df_denom=265, df_num=8
Granger Causality
number of lags (no zero) 9
ssr based F test: F=2.0761 , p=0.0320 , df_denom=262, df_num=9
ssr based chi2 test: chi2=20.0400 , p=0.0177 , df=9
likelihood ratio test: chi2=19.3576 , p=0.0223 , df=9
parameter F test: F=2.0761 , p=0.0320 , df_denom=262, df_num=9
Granger Causality
number of lags (no zero) 10
ssr based F test: F=1.8313 , p=0.0556 , df_denom=259, df_num=10
ssr based chi2 test: chi2=19.7977 , p=0.0312 , df=10
likelihood ratio test: chi2=19.1291 , p=0.0387 , df=10
parameter F test: F=1.8313 , p=0.0556 , df_denom=259, df_num=10
Granger Causality
number of lags (no zero) 11
ssr based F test: F=1.8893 , p=0.0410 , df_denom=256, df_num=11
ssr based chi2 test: chi2=22.6493 , p=0.0198 , df=11
likelihood ratio test: chi2=21.7769 , p=0.0262 , df=11
parameter F test: F=1.8893 , p=0.0410 , df_denom=256, df_num=11
Granger Causality
number of lags (no zero) 12
ssr based F test: F=2.0157 , p=0.0234 , df_denom=253, df_num=12
ssr based chi2 test: chi2=26.5779 , p=0.0089 , df=12
likelihood ratio test: chi2=25.3830 , p=0.0131 , df=12
parameter F test: F=2.0157 , p=0.0234 , df_denom=253, df_num=12
Granger Causality
number of lags (no zero) 13
ssr based F test: F=1.8636 , p=0.0347 , df_denom=250, df_num=13
ssr based chi2 test: chi2=26.8434 , p=0.0131 , df=13
likelihood ratio test: chi2=25.6211 , p=0.0191 , df=13
parameter F test: F=1.8636 , p=0.0347 , df_denom=250, df_num=13
Granger Causality
number of lags (no zero) 14
ssr based F test: F=1.5283 , p=0.1013 , df_denom=247, df_num=14
ssr based chi2 test: chi2=23.9090 , p=0.0470 , df=14
likelihood ratio test: chi2=22.9296 , p=0.0614 , df=14
parameter F test: F=1.5283 , p=0.1013 , df_denom=247, df_num=14
Granger Causality
number of lags (no zero) 15
ssr based F test: F=0.9749 , p=0.4823 , df_denom=244, df_num=15
ssr based chi2 test: chi2=16.4815 , p=0.3508 , df=15
likelihood ratio test: chi2=16.0065 , p=0.3816 , df=15
parameter F test: F=0.9749 , p=0.4823 , df_denom=244, df_num=15
grangercausalitytests(filter_df[['transform_y_y', 'transform_y_x']], maxlag=15)
it says:
Granger Causality
number of lags (no zero) 1
ssr based F test: F=70.4932 , p=0.0000 , df_denom=286, df_num=1
ssr based chi2 test: chi2=71.2326 , p=0.0000 , df=1
likelihood ratio test: chi2=63.6734 , p=0.0000 , df=1
parameter F test: F=70.4932 , p=0.0000 , df_denom=286, df_num=1
Granger Causality
number of lags (no zero) 2
ssr based F test: F=47.3519 , p=0.0000 , df_denom=283, df_num=2
ssr based chi2 test: chi2=96.3771 , p=0.0000 , df=2
likelihood ratio test: chi2=83.1351 , p=0.0000 , df=2
parameter F test: F=47.3519 , p=0.0000 , df_denom=283, df_num=2
Granger Causality
number of lags (no zero) 3
ssr based F test: F=33.6081 , p=0.0000 , df_denom=280, df_num=3
ssr based chi2 test: chi2=103.3450, p=0.0000 , df=3
likelihood ratio test: chi2=88.2665 , p=0.0000 , df=3
parameter F test: F=33.6081 , p=0.0000 , df_denom=280, df_num=3
Granger Causality
number of lags (no zero) 4
ssr based F test: F=24.1709 , p=0.0000 , df_denom=277, df_num=4
ssr based chi2 test: chi2=99.8248 , p=0.0000 , df=4
likelihood ratio test: chi2=85.6260 , p=0.0000 , df=4
parameter F test: F=24.1709 , p=0.0000 , df_denom=277, df_num=4
Granger Causality
number of lags (no zero) 5
ssr based F test: F=15.6663 , p=0.0000 , df_denom=274, df_num=5
ssr based chi2 test: chi2=81.4760 , p=0.0000 , df=5
likelihood ratio test: chi2=71.6615 , p=0.0000 , df=5
parameter F test: F=15.6663 , p=0.0000 , df_denom=274, df_num=5
Granger Causality
number of lags (no zero) 6
ssr based F test: F=11.5874 , p=0.0000 , df_denom=271, df_num=6
ssr based chi2 test: chi2=72.8595 , p=0.0000 , df=6
likelihood ratio test: chi2=64.8565 , p=0.0000 , df=6
parameter F test: F=11.5874 , p=0.0000 , df_denom=271, df_num=6
Granger Causality
number of lags (no zero) 7
ssr based F test: F=9.7282 , p=0.0000 , df_denom=268, df_num=7
ssr based chi2 test: chi2=71.9090 , p=0.0000 , df=7
likelihood ratio test: chi2=64.0753 , p=0.0000 , df=7
parameter F test: F=9.7282 , p=0.0000 , df_denom=268, df_num=7
Granger Causality
number of lags (no zero) 8
ssr based F test: F=8.3121 , p=0.0000 , df_denom=265, df_num=8
ssr based chi2 test: chi2=70.7626 , p=0.0000 , df=8
likelihood ratio test: chi2=63.1365 , p=0.0000 , df=8
parameter F test: F=8.3121 , p=0.0000 , df_denom=265, df_num=8
Granger Causality
number of lags (no zero) 9
ssr based F test: F=7.7863 , p=0.0000 , df_denom=262, df_num=9
ssr based chi2 test: chi2=75.1583 , p=0.0000 , df=9
likelihood ratio test: chi2=66.6028 , p=0.0000 , df=9
parameter F test: F=7.7863 , p=0.0000 , df_denom=262, df_num=9
Granger Causality
number of lags (no zero) 10
ssr based F test: F=6.9230 , p=0.0000 , df_denom=259, df_num=10
ssr based chi2 test: chi2=74.8427 , p=0.0000 , df=10
likelihood ratio test: chi2=66.3278 , p=0.0000 , df=10
parameter F test: F=6.9230 , p=0.0000 , df_denom=259, df_num=10
Granger Causality
number of lags (no zero) 11
ssr based F test: F=6.7168 , p=0.0000 , df_denom=256, df_num=11
ssr based chi2 test: chi2=80.5233 , p=0.0000 , df=11
likelihood ratio test: chi2=70.7452 , p=0.0000 , df=11
parameter F test: F=6.7168 , p=0.0000 , df_denom=256, df_num=11
Granger Causality
number of lags (no zero) 12
ssr based F test: F=6.8729 , p=0.0000 , df_denom=253, df_num=12
ssr based chi2 test: chi2=90.6239 , p=0.0000 , df=12
likelihood ratio test: chi2=78.4393 , p=0.0000 , df=12
parameter F test: F=6.8729 , p=0.0000 , df_denom=253, df_num=12
Granger Causality
number of lags (no zero) 13
ssr based F test: F=6.0868 , p=0.0000 , df_denom=250, df_num=13
ssr based chi2 test: chi2=87.6748 , p=0.0000 , df=13
likelihood ratio test: chi2=76.1718 , p=0.0000 , df=13
parameter F test: F=6.0868 , p=0.0000 , df_denom=250, df_num=13
Granger Causality
number of lags (no zero) 14
ssr based F test: F=5.6246 , p=0.0000 , df_denom=247, df_num=14
ssr based chi2 test: chi2=87.9896 , p=0.0000 , df=14
likelihood ratio test: chi2=76.3759 , p=0.0000 , df=14
parameter F test: F=5.6246 , p=0.0000 , df_denom=247, df_num=14
Granger Causality
number of lags (no zero) 15
ssr based F test: F=5.3775 , p=0.0000 , df_denom=244, df_num=15
ssr based chi2 test: chi2=90.9098 , p=0.0000 , df=15
likelihood ratio test: chi2=78.5443 , p=0.0000 , df=15
parameter F test: F=5.3775 , p=0.0000 , df_denom=244, df_num=15
那么我可以说是y_x Granger导致y_吗？
根据eg.2，所有p值均为0.0000， 那么你认为格兰杰导致了x 这意味着因果关系是双向的吗？
如何给出格兰杰因果关系的置信度分数？
F-test值在这里起作用吗？
在eg.1中，所有的f测试值都非常低，而eg.2中所有的f测试值都非常高。 在这种情况下，我可以考虑F检验值来得出结论吗？ 如果是这样的话，那么f检验的重要价值是什么？ 短暂性脑缺血发作 从eg.1的几个滞后来看，p值低于0.05， 那么我能说y_x Granger导致y_y吗 根据您的问题，我假设您希望将p值阈值设置为0.05。在示例1中，当p值显示为
p=0.0530滞后数（非零）1，这意味着y_y（第二列）的过去1个值（滞后1）对y_x（第一列）的当前值没有统计上的显著影响。对于p值显示为p=0.0001时的滞后数（无零）3
，这意味着y_y（第二列）的过去3个值（共同）对y_x（第一列）的当前值具有统计显著影响
根据eg.2，所有的p值都是0.0000，所以y_y Granger导致y_x
与上述答案类似，在所有情况下，例如例2，p值均<0.05，这意味着y_x（第二列）的过去值对y_y（第一列）的当前值具有统计显著影响
这意味着因果关系是双向的
这取决于你试图解决的问题，典型的假设是因果关系是单向的。从您的结果来看，您似乎最有可能从y_x预测y_y的值，而不是相反。如果两个输入信号都是周期性相似的循环信号，则可以看到过去的y_y值和当前的y_x值之间的弱相关性
如何给出格兰杰因果关系的置信度分数？
F-test值在这里起作用吗？
在eg.1中，所有的f测试值都非常低，而eg.2中所有的f测试值都非常高。在这种情况下，我能考虑F检验值得出结论吗？
如果是这样的话，那么f检验的重要价值是什么？
基于自由度，F值和p值相互关联，因为您使用的是p值阈值，这意味着您正在设置F值阈值
参考资料：