{"id":1638,"date":"2023-07-25T13:50:04","date_gmt":"2023-07-25T13:50:04","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%b8%83%e9%b2%81%e6%96%bd%e6%88%88%e5%bc%97%e9%9b%b7%e6%b5%8b%e8%af%95python\/"},"modified":"2023-07-25T13:50:04","modified_gmt":"2023-07-25T13:50:04","slug":"%e5%b8%83%e9%b2%81%e6%96%bd%e6%88%88%e5%bc%97%e9%9b%b7%e6%b5%8b%e8%af%95python","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%b8%83%e9%b2%81%e6%96%bd%e6%88%88%e5%bc%97%e9%9b%b7%e6%b5%8b%e8%af%95python\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u6267\u884c breusch-godfrey \u6d4b\u8bd5"},"content":{"rendered":"

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\u7ebf\u6027\u56de\u5f52\u7684\u5173\u952e\u5047\u8bbe<\/a>\u4e4b\u4e00\u662f\u6b8b\u5dee\u4e4b\u95f4\u4e0d\u5b58\u5728\u76f8\u5173\u6027\uff0c\u5373\u6b8b\u5dee\u662f\u72ec\u7acb\u7684\u3002<\/span><\/p>\n

\u4e3a\u4e86\u6d4b\u8bd5\u4e00\u9636\u81ea\u76f8\u5173\uff0c\u6211\u4eec\u53ef\u4ee5\u6267\u884cDurbin-Watson \u68c0\u9a8c<\/a>\u3002\u7136\u800c\uff0c\u5982\u679c\u6211\u4eec\u60f3\u6d4b\u8bd5\u66f4\u9ad8\u9636\u7684\u81ea\u76f8\u5173\u6027\uff0c\u6211\u4eec\u9700\u8981\u6267\u884cBreusch-Godfrey \u68c0\u9a8c<\/strong>\u3002<\/span><\/p>\n

\u8be5\u6d4b\u8bd5\u4f7f\u7528\u4ee5\u4e0b\u5047\u8bbe<\/a>\uff1a<\/span><\/p>\n

H 0<\/sub> \uff08\u96f6\u5047\u8bbe\uff09\uff1a<\/strong>\u4e0d\u5b58\u5728\u5c0f\u4e8e\u6216\u7b49\u4e8ep<\/em>\u7684\u9636\u6570\u7684\u81ea\u76f8\u5173\u3002<\/span><\/p>\n

H A<\/sub> \uff08\u66ff\u4ee3\u5047\u8bbe\uff09\uff1a<\/strong>\u5b58\u5728\u5c0f\u4e8e\u6216\u7b49\u4e8ep<\/em>\u7684\u67d0\u4e2a\u9636\u7684\u81ea\u76f8\u5173\u3002<\/span><\/p>\n

\u68c0\u9a8c\u7edf\u8ba1\u91cf\u9075\u5faa\u5177\u6709p \u4e2a<\/em>\u81ea\u7531\u5ea6\u7684\u5361\u65b9\u5206\u5e03\u3002<\/span><\/p>\n

\u5982\u679c\u4e0e\u8be5\u68c0\u9a8c\u7edf\u8ba1\u91cf\u76f8\u5bf9\u5e94\u7684p \u503c<\/a>\u4f4e\u4e8e\u4e00\u5b9a\u7684\u663e\u7740\u6027\u6c34\u5e73\uff08\u4f8b\u5982 0.05\uff09\uff0c\u90a3\u4e48\u6211\u4eec\u53ef\u4ee5\u62d2\u7edd\u96f6\u5047\u8bbe\u5e76\u5f97\u51fa\u7ed3\u8bba\uff1a\u5728\u67d0\u4e2a\u8f83\u4f4e\u9636\u6216\u7b49\u4e8ep<\/em>\u7684\u6b8b\u5dee\u4e4b\u95f4\u5b58\u5728\u81ea\u76f8\u5173\u3002<\/span><\/p>\n

\u8981\u5728 Python \u4e2d\u6267\u884c Breusch-Godfrey \u6d4b\u8bd5\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528statsmodels<\/b>\u5e93\u4e2d\u7684acorr_breusch_godfrey()<\/strong>\u51fd\u6570\u3002<\/span><\/p>\n

\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u8bf4\u660e\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c Breusch-Godfrey \u6d4b\u8bd5\u3002<\/span><\/p>\n

\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/h3>\n

\u9996\u5148\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u5305\u542b\u4e24\u4e2a\u9884\u6d4b\u53d8\u91cf\uff08x1 \u548c x2\uff09\u548c\u4e00\u4e2a\u54cd\u5e94\u53d8\u91cf (y) \u7684\u6570\u636e\u96c6\u3002<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd<\/span>\n\n#create dataset<\/span>\ndf = pd. DataFrame<\/span> ({' x1<\/span> ': [3, 4, 4, 5, 8, 9, 11, 13, 14, 16, 17, 20],\n                   ' x2<\/span> ': [7, 7, 8, 8, 12, 4, 5, 15, 9, 17, 19, 19],\n                    ' y<\/span> ': [24, 25, 25, 27, 29, 31, 34, 34, 39, 30, 40, 49]})\n\n#view first five rows of dataset\n<\/span>df. head<\/span> ()\n\n\tx1 x2 y\n0 3 7 24\n1 4 7 25\n2 4 8 25\n3 5 8 27\n4 8 12 29\n<\/strong><\/pre>\n
\u7b2c 2 \u6b65\uff1a\u62df\u5408\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n
\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 x1 \u548c x2 \u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0cy \u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf<\/a>\u6765\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\u3002<\/span><\/p>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define response variable\n<\/span>y = df[' y<\/span> ']\n\n#define predictor variables\n<\/span>x = df[[' x1<\/span> ', ' x2<\/span> ']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n<\/strong><\/span><\/pre>\n
\u7b2c 3 \u6b65\uff1a\u6267\u884c\u5e03\u6d1b\u65bd-\u6208\u5f17\u96f7\u6d4b\u8bd5<\/strong><\/span><\/h3>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u6267\u884c Breusch-Godfrey \u68c0\u9a8c\u6765\u68c0\u9a8cp<\/em>\u9636\u6b8b\u5dee\u4e4b\u95f4\u7684\u81ea\u76f8\u5173\u6027\u3002\u5bf9\u4e8e\u672c\u4f8b\uff0c\u6211\u4eec\u5c06\u9009\u62e9p<\/em> = 3\u3002<\/span><\/p>\n
 import<\/span> statsmodels. stats<\/span> . diagnosis<\/span> as<\/span> dg\n<\/span>\n#perform Breusch-Godfrey test at order p<\/em> = 3\nprint<\/span> (dg. acorr_breusch_godfrey<\/span> (model, nlags= 3<\/span> ))\n\n(8.70314827, 0.0335094873, 5.27967224, 0.0403980576)\n<\/span><\/span><\/strong><\/span><\/pre>\n
\u8f93\u51fa\u7684\u7b2c\u4e00\u4e2a\u503c\u8868\u793a\u68c0\u9a8c\u7edf\u8ba1\u91cf\uff0c\u7b2c\u4e8c\u4e2a\u503c\u8868\u793a\u76f8\u5e94\u7684 p \u503c\u3002<\/span><\/p>\n
\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u4ee5\u4e0b\u5185\u5bb9\uff1a<\/span><\/p>\n