{"id":2472,"date":"2023-07-22T03:17:44","date_gmt":"2023-07-22T03:17:44","guid":{"rendered":"https:\/\/statorials.org\/cn\/python-%e4%b8%ad%e7%9a%84%e6%a0%bc%e5%85%b0%e6%9d%b0%e5%9b%a0%e6%9e%9c%e5%85%b3%e7%b3%bb%e6%a3%80%e9%aa%8c\/"},"modified":"2023-07-22T03:17:44","modified_gmt":"2023-07-22T03:17:44","slug":"python-%e4%b8%ad%e7%9a%84%e6%a0%bc%e5%85%b0%e6%9d%b0%e5%9b%a0%e6%9e%9c%e5%85%b3%e7%b3%bb%e6%a3%80%e9%aa%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python-%e4%b8%ad%e7%9a%84%e6%a0%bc%e5%85%b0%e6%9d%b0%e5%9b%a0%e6%9e%9c%e5%85%b3%e7%b3%bb%e6%a3%80%e9%aa%8c\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c\u683c\u5170\u6770\u56e0\u679c\u5173\u7cfb\u68c0\u9a8c"},"content":{"rendered":"

<\/p>\n

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\u683c\u5170\u6770\u56e0\u679c\u68c0\u9a8c<\/strong>\u7528\u4e8e\u786e\u5b9a\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u662f\u5426\u6709\u52a9\u4e8e\u9884\u6d4b\u53e6\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u3002<\/span><\/p>\n

\u8be5\u68c0\u9a8c\u4f7f\u7528\u4ee5\u4e0b\u539f\u5047\u8bbe\u548c\u5907\u62e9\u5047\u8bbe\uff1a<\/span><\/p>\n

\u539f\u5047\u8bbe (H 0<\/sub> )\uff1a<\/strong>\u65f6\u95f4\u5e8f\u5217x<\/em>\u4e0d\u4f1a\u5bfc\u81f4\u65f6\u95f4\u5e8f\u5217y<\/em>\u53d1\u751f\u683c\u5170\u6770\u5206\u5e03<\/span><\/p>\n

\u5907\u62e9\u5047\u8bbe ( HA<\/sub> )\uff1a<\/strong>\u65f6\u95f4\u5e8f\u5217x<\/em>\u683c\u5170\u6770\u539f\u56e0\u65f6\u95f4\u5e8f\u5217y<\/em><\/span><\/p>\n

\u672f\u8bed\u201c\u683c\u5170\u6770\u539f\u56e0\u201d\u662f\u6307\u77e5\u9053\u5177\u6709\u4e00\u5b9a\u6ede\u540e\u6027\u7684\u65f6\u95f4\u5e8f\u5217x<\/em>\u7684\u503c\u5bf9\u4e8e\u9884\u6d4b\u7a0d\u540e\u65f6\u95f4\u5e8f\u5217y<\/em>\u7684\u503c\u662f\u6709\u7528\u7684\u3002<\/span><\/p>\n

\u6b64\u68c0\u9a8c\u751f\u6210\u5177\u6709\u76f8\u5e94p \u503c<\/a>\u7684 F \u68c0\u9a8c\u7edf\u8ba1\u91cf\u3002\u5982\u679c p \u503c\u4f4e\u4e8e<\/span>\u4e00\u5b9a\u7684\u663e\u7740\u6027\u6c34\u5e73\uff08\u5373 \u03b1 = 0.05\uff09\uff0c\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u62d2\u7edd\u539f\u5047\u8bbe\u5e76\u5f97\u51fa\u7ed3\u8bba\uff1a\u6211\u4eec\u6709\u8db3\u591f\u7684\u8bc1\u636e\u6765\u65ad\u8a00\u65f6\u95f4\u5e8f\u5217x<\/em> Granger \u5bfc\u81f4\u65f6\u95f4\u5e8f\u5217 y\u3002<\/span><\/p>\n

\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 statsmodels \u5305\u4e2d\u7684grangercausalitytests()<\/a>\u51fd\u6570\u5728 Python \u4e2d\u6267\u884c\u683c\u5170\u6770\u56e0\u679c\u68c0\u9a8c\uff1a<\/span><\/p>\n

 from<\/span> statsmodels. tsa<\/span> . stattools<\/span> import<\/span> grangercausalitytests\n\n#perform Granger-Causality test<\/span>\ngrangercausalitytests(df[[' column1<\/span> ', ' column2<\/span> ']], maxlag=[ 3<\/span> ])\n<\/strong><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c maxlag<\/strong>\u6307\u5b9a\u7b2c\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u4e2d\u4f7f\u7528\u7684\u6ede\u540e\u6570\u3002<\/span><\/span><\/p>\n
\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u6b64\u529f\u80fd\u3002<\/span><\/p>\n
\u7b2c 1 \u6b65\uff1a\u52a0\u8f7d\u6570\u636e<\/strong><\/span><\/h3>\n
\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u4e00\u4e2a\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542b 1930 \u5e74\u81f3 1983 \u5e74\u7f8e\u56fd\u7684\u9e21\u86cb\u6570\u91cf\u548c\u9e21\u6570\u91cf\u503c\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#define URL where dataset is located\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Miscellaneous\/main\/chicken_egg.txt\"\n\n#read in dataset as pandas DataFrame\n<\/span>df = pd. read_csv<\/span> (url, sep=\" \")\n\n#view first five rows of DataFrame\n<\/span>df. head<\/span> ()\n\n\tyear chicken egg\n0 1930 468491 3581\n1 1931 449743 3532\n2 1932 436815 3327\n3 1933 444523 3255\n4 1934 433937 3156\n<\/strong><\/pre>\n
\u6b65\u9aa4 2\uff1a\u6267\u884c\u683c\u5170\u6770\u56e0\u679c\u5173\u7cfb\u68c0\u9a8c<\/strong><\/span><\/h3>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528grangercausalitytests<\/strong> ()<\/strong>\u51fd\u6570\u8fdb\u884c\u683c\u5170\u6770\u56e0\u679c\u5173\u7cfb\u68c0\u9a8c\uff0c\u770b\u770b\u6240\u4ea7\u9e21\u86cb\u7684\u6570\u91cf\u662f\u5426\u53ef\u4ee5\u9884\u6d4b\u672a\u6765\u6bcd\u9e21\u7684\u6570\u91cf\u3002\u6211\u4eec\u5c06\u4f7f\u7528\u4e09\u4e2a\u504f\u79fb\u91cf\u8fd0\u884c\u6d4b\u8bd5\uff1a<\/span><\/p>\n
 from<\/span> statsmodels. tsa<\/span> . stattools<\/span> import<\/span> grangercausalitytests<\/span>\n\n#perform Granger-Causality test<\/span>\ngrangercausalitytests(df[[' chicken<\/span> ', ' egg<\/span> ']], maxlag=[ 3<\/span> ])\n\nGranger Causality\nnumber of lags (no zero) 3\nssr based F test: F=5.4050, p=0.0030, df_denom=44, df_num=3\nssr based chi2 test: chi2=18.7946, p=0.0003, df=3\nlikelihood ratio test: chi2=16.0003, p=0.0011, df=3\nparameter F test: F=5.4050, p=0.0030, df_denom=44, df_num=3<\/strong><\/pre>\n
 F \u68c0\u9a8c\u7edf\u8ba1\u91cf\u7ed3\u679c\u4e3a5.405<\/strong> \uff0c\u76f8\u5e94\u7684 p \u503c\u4e3a0.0030<\/strong> \u3002<\/span><\/p>\n
\u7531\u4e8e p \u503c\u5c0f\u4e8e 0.05\uff0c\u6211\u4eec\u53ef\u4ee5\u62d2\u7edd\u68c0\u9a8c\u7684\u539f\u5047\u8bbe\uff0c\u5e76\u5f97\u51fa\u7ed3\u8bba\uff1a\u77e5\u9053\u9e21\u86cb\u7684\u6570\u91cf\u6709\u52a9\u4e8e\u9884\u6d4b\u672a\u6765\u6bcd\u9e21\u7684\u6570\u91cf\u3002<\/span><\/p>\n
\u6b65\u9aa4 3\uff1a\u53cd\u5411\u6267\u884c\u683c\u5170\u6770\u56e0\u679c\u5173\u7cfb\u68c0\u9a8c<\/strong><\/span><\/h3>\n
\u5c3d\u7ba1\u6211\u4eec\u62d2\u7edd\u4e86\u68c0\u9a8c\u7684\u539f\u5047\u8bbe\uff0c\u4f46\u5b9e\u9645\u4e0a\u6709\u53ef\u80fd\u53d1\u751f\u53cd\u5411\u56e0\u679c\u5173\u7cfb\u7684\u60c5\u51b5\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u6bcd\u9e21\u7684\u6570\u91cf\u6709\u53ef\u80fd\u5bfc\u81f4\u9e21\u86cb\u6570\u91cf\u7684\u53d8\u5316\u3002<\/span><\/p>\n
\u4e3a\u4e86\u6392\u9664\u8fd9\u79cd\u53ef\u80fd\u6027\uff0c\u6211\u4eec\u9700\u8981\u53cd\u5411\u6267\u884c\u683c\u5170\u6770\u56e0\u679c\u68c0\u9a8c\uff0c\u4f7f\u7528\u9e21\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c\u9e21\u86cb\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf<\/a>\uff1a<\/span><\/p>\n
 from<\/span> statsmodels. tsa<\/span> . stattools<\/span> import<\/span> grangercausalitytests<\/span>\n\n#perform Granger-Causality test<\/span>\ngrangercausalitytests(df[[' egg<\/span> ', ' chicken<\/span> ']], maxlag=[ 3<\/span> ])\n\nGranger Causality\nnumber of lags (no zero) 3\nssr based F test: F=0.5916, p=0.6238, df_denom=44, df_num=3\nssr based chi2 test: chi2=2.0572, p=0.5606, df=3\nlikelihood ratio test: chi2=2.0168, p=0.5689, df=3\nparameter F test: F=0.5916, p=0.6238, df_denom=44, df_num=3\n<\/strong><\/pre>\n
 F \u68c0\u9a8c\u7edf\u8ba1\u91cf\u7ed3\u679c\u4e3a0.5916<\/strong> \uff0c\u76f8\u5e94\u7684 p \u503c\u4e3a0.6238<\/strong> \u3002<\/span><\/p>\n
\u7531\u4e8e p \u503c\u4e0d\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u6211\u4eec\u4e0d\u80fd\u62d2\u7edd\u539f\u5047\u8bbe\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u6bcd\u9e21\u7684\u6570\u91cf\u5e76\u4e0d\u80fd\u9884\u6d4b\u672a\u6765\u9e21\u86cb\u7684\u6570\u91cf\u3002<\/span><\/p>\n
\u56e0\u6b64\uff0c\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u77e5\u9053\u9e21\u86cb\u7684\u6570\u91cf\u6709\u52a9\u4e8e\u9884\u6d4b\u672a\u6765\u6bcd\u9e21\u7684\u6570\u91cf\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u4f7f\u7528\u65f6\u95f4\u5e8f\u5217\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n
\u5982\u4f55\u5728 Seaborn \u4e2d\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u56fe<\/a>
\u5982\u4f55\u5728 Matplotlib \u4e2d\u521b\u5efa\u65f6\u95f4\u5e8f\u5217\u56fe<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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