{"id":155559,"date":"2019-08-11T18:32:25","date_gmt":"2019-08-11T10:32:25","guid":{"rendered":"https:\/\/www.lixiangluntan.com\/155559.html"},"modified":"2019-08-11T18:32:25","modified_gmt":"2019-08-11T10:32:25","slug":"%e4%b8%ad%e5%9b%bd%e8%82%a1%e7%a5%a8%e5%86%85%e5%9c%a8%e4%bb%b7%e5%80%bc%e5%bd%b1%e5%93%8d%e5%9b%a0%e7%b4%a0%e7%9a%84%e5%ae%9e%e8%af%81%e5%88%86%e6%9e%90_%e9%a3%9e%e7%8b%90","status":"publish","type":"post","link":"https:\/\/www.lixiangluntan.com\/?p=155559","title":{"rendered":"\u4e2d\u56fd\u80a1\u7968\u5185\u5728\u4ef7\u503c\u5f71\u54cd\u56e0\u7d20\u7684\u5b9e\u8bc1\u5206\u6790_\u98de\u72d0"},"content":{"rendered":"<blockquote style=\"font-size:15px;background-color: #F5F5F5;border:none;padding: 5px;margin:15px;line-height: 1.6;text-indent: 2em;\">\n<p style=\"margin-bottom:5px;\">\u4ef7\u503c\u6295\u8d44\u8fd8\u884c\u4e0d\u884c<\/p>\n<p>\u3000\u3000\u4ef7\u503c\u6295\u8d44\u8fd8\u884c\u5417\uff1f<\/p>\n<\/blockquote>\n<blockquote style=\"font-size:15px;background-color: #F5F5F5;border:none;padding: 5px;margin:15px;line-height: 1.6;text-indent: 2em;\">\n<p style=\"margin-bottom:5px;\">\u3010\u80a1\u7968\u8d44\u6e90\u9986\u3011\u540c\u82b1\u987a\u5b9e\u65f6\u6e2f\u80a1\u7834\u89e3,\u540c\u82b1\u987a\u8d22\u5bcc\u4e3b\u529b\u7834\u89e3,\u5927\u667a\u6167\u8d22\u5bcc\u7834\u89e3\u7248,\u901a\u8fbe\u4fe1\u8d22\u5bcc\u7834\u89e3\u7248,\u901a\u8fbe\u4fe1\u4e13\u4e1a\u7248\u7834\u89e3,\u901a\u8fbe\u4fe1\u7814\u7a76\u7248\u7834\u89e3,\u901a\u8fbe\u4fe1\u8d85\u8d62\u7248\u7834\u89e3,\u80a1\u7968\u516c\u5f0f,\u80a1\u7968\u6307\u6807\u516c\u5f0f,\u80a1\u7968\u516c\u5f0f\u5927\u5168,\u6307\u5357\u9488\u8f6f\u4ef6\u7834\u89e3,\u7092\u80a1\u4e66\u7c4d,k\u7ebf\u56fe\u600e\u4e48\u770b,\u80a1\u7968k\u7ebf\u56fe,\u3002<\/p>\n<\/blockquote>\n<p>&nbsp;&nbsp;&nbsp;&nbsp; <\/p>\n<p> \u3000\u3000\u77e5\u8bc6\u7f51\u5c0f\u7f16\u4e3a\u60a8\u5206\u6790<strong>\u4e2d\u56fd\u80a1\u7968\u5185\u5728\u5f71\u54cd\u56e0\u7d20\u7684\u5b9e\u8bc1\u5206\u6790<\/strong>\uff1a<\/p>\n<p> \u3000\u3000 \u672c\u6587\u65e8\u5728\u5bf9\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\u3001\u516c\u53f8\u8d44\u4ea7\u51c0\u503c\u7b49\u5fae\u89c2\u56e0\u7d20\u5bf9\u80a1\u7968\u4ef7\u503c\u7684\u5f71\u54cd\u8fdb\u884c\u5b9e\u8bc1\u5206\u6790\uff0c\u4e3b\u8981\u8bc4\u4ef7\u7684\u662f\u516c\u53f8\u7684\u76c8\u5229\u6c34\u5e73\u548c\u6295\u8d44\u4ef7\u503c\u3002\u9996\u5148\uff0c\u5728\u8bc1\u5238\u6295\u8d44\u57fa\u672c\u5206\u6790\u6d41\u6d3e\u7684\u57fa\u7840\u4e0a\u6211\u4eec\u5efa\u7acb\u4e86\u8ba1\u91cf\u6a21\u578b\u3002\u7136\u540e\uff0c\u6536\u96c6\u4e86\u76f8\u5173\u7684\u6570\u636e\uff0c\u5229\u7528EVIEWS\u8f6f\u4ef6\u5bf9\u8ba1\u91cf\u6a21\u578b\u8fdb\u884c\u4e86\u53c2\u6570\u4f30\u8ba1\u548c\u68c0\u9a8c\uff0c\u5e76\u52a0\u4ee5\u4fee\u6b63\u3002\u6700\u540e\uff0c\u6211\u4eec\u5bf9\u6240\u5f97\u7684\u5206\u6790\u7ed3\u679c\u4f5c\u4e86\u7ecf\u6d4e\u610f\u4e49\u7684\u5206\u6790\uff0c\u5e76\u76f8\u5e94\u63d0\u51fa\u4e00\u4e9b\u5efa\u8bae\u3002<\/p>\n<p> \u3000\u3000\u4e00\u3001\u95ee\u9898\u7684\u63d0\u51fa<\/p>\n<p> \u3000\u3000\u7ecf\u8fc7\u5341\u591a\u5e74\u98ce\u96e8\u7684\u6d17\u793c\uff0c\u6211\u56fd\u7684\u53d6\u5f97\u4e86\u521d\u6b65\u7684\u53d1\u5c55\uff0c\u4f46\u662f\u548c\u53d1\u8fbe\u56fd\u5bb6\u8bc1\u5238\u5e02\u573a\u76f8\u6bd4\uff0c\u4ecd\u7136\u5904\u4e8e\u4e0d\u6210\u719f\u7684\u9636\u6bb5\uff0c\u5168\u6d41\u901a\u95ee\u9898\u5c1a\u672a\u89e3\u51b3\uff0c\u6295\u673a\u98ce\u6c14\u76db\u884c\uff0c\u5e84\u5bb6\u64cd\u7eb5\u80a1\u4ef7\u7684\u884c\u4e3a\u5927\u91cf\u5b58\u5728\uff0c\u6295\u8d44\u8005\u8ffd\u957f\u6740\u8dcc\u7684\u76f2\u76ee\u6295\u8d44\u884c\u4e3a\u6bd4\u6bd4\u7686\u662f\u3002\u4e3a\u4e86\u5f15\u5bfc\u6295\u8d44\u8005\u7406\u6027\u7684\u6295\u8d44\u884c\u4e3a\u548c\u4fdd\u62a4\u4e2d\u5c0f\u80a1\u4e1c\u7684\u5229\u76ca\uff0c\u4ee5\u53ca\u4fc3\u8fdb\u80a1\u7968\u5e02\u573a\u7684\u53d1\u5c55\u4e0e\u5b8c\u5584\uff0c\u8d8a\u6765\u8d8a\u591a\u7684\u4eba\u63d0\u5021\u4ef7\u503c\u6295\u8d44\uff0c\u516c\u53f8\u7684\u5185\u5728\u4ef7\u503c\u6210\u4e3a\u5f71\u54cd\u80a1\u4ef7\u7684\u91cd\u8981\u56e0\u7d20\u3002<\/p>\n<p> \u3000\u3000\u4e8c\u3001\u7ecf\u6d4e\u7406\u8bba\u9648\u8ff0<\/p>\n<p> \u3000\u3000\u8bc1\u5238\u6295\u8d44\u7684\u5206\u6790\u6d41\u6d3e\u6709\u57fa\u672c\u5206\u6790\u6d41\u6d3e\u548c\u6280\u672f\u5206\u6790\u6d41\u6d3e\u3002\u57fa\u672c\u5206\u6790\u6d41\u6d3e\u662f\u76ee\u524d\u897f\u65b9\u6295\u8d44\u754c\u7684\u4e3b\u6d41\u6d3e\u522b\uff0c\u5b83\u662f\u4ee5\u5b8f\u89c2\u7ecf\u6d4e\u3001\u884c\u4e1a\u7279\u5f81\u53ca\u4e0a\u5e02\u516c\u53f8\u7684\u57fa\u672c\u8d22\u52a1\u6570\u636e\u4f5c\u4e3a\u6295\u8d44\u5206\u6790\u5bf9\u8c61\uff0c\u5bf9\u8bc1\u5238\u7684\u6295\u8d44\u4ef7\u503c\u53ca\u5e02\u573a\u5b9a\u4ef7\u4f5c\u51fa\u8bc4\u4f30\u5224\u65ad\u7684\u4e00\u79cd\u5206\u6790\u65b9\u6cd5\u3002\u6b64\u6d41\u6d3e\u7684\u6295\u8d44\u8005\u5927\u591a\u662f\u4ef7\u503c\u6295\u8d44\u8005\uff0c\u4ed6\u4eec\u7684\u6295\u8d44\u884c\u4e3a\u6bd4\u8f83\u7406\u6027\u3002\u57fa\u672c\u5206\u6790\u7684\u7406\u8bba\u57fa\u7840\u5728\u4e8e\u8bc1\u5238\u7684\u5185\u5728\u4ef7\u503c\u7406\u8bba\u3002\u5373\uff1a\u4efb\u4f55\u4e00\u79cd\u6295\u8d44\u5bf9\u8c61\u90fd\u6709\u201c\u5185\u5728\u4ef7\u503c\u201d\uff0c\u4e14\u201c\u5185\u5728\u4ef7\u503c\u201d\u53ef\u4ee5\u901a\u8fc7\u5bf9\u8be5\u79cd\u6295\u8d44\u5bf9\u8c61\u7684\u73b0\u72b6\u548c\u672a\u6765\u524d\u666f\u7684\u5206\u6790\u800c\u83b7\u5f97\uff1b\u5e02\u573a\u4ef7\u683c\u548c\u201c\u5185\u5728\u4ef7\u503c\u201d\u4e4b\u95f4\u7684\u5dee\u8ddd\u6700\u7ec8\u4f1a\u88ab\u5e02\u573a\u7ea0\u6b63\u3002\u5b83\u6709\u4e24\u4e2a\u524d\u63d0\u5047\u8bbe\uff1a\u201c\u80a1\u7968\u7684\u4ef7\u503c\u51b3\u5b9a\u4ef7\u683c\u201d\u3001\u201c\u4ef7\u683c\u56f4\u7ed5\u4ef7\u503c\u4e0a\u4e0b\u6ce2\u52a8\u201d\u3002\u7531\u4e8e\u516c\u53f8\u7684\u5185\u5728\u4ef7\u503c\u4f53\u73b0\u5728\u76c8\u5229\u80fd\u529b\u548c\u6295\u8d44\u4ef7\u503c\u4e0a\uff0c\u6240\u4ee5\u6211\u4eec\u9009\u62e9\u4e86\u80fd\u591f\u53cd\u6620\u8fd9\u4e24\u4e2a\u56e0\u7d20\u7684\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\u548c\u6bcf\u80a1\u51c0\u8d44\u4ea7\u4f5c\u4e3a\u5206\u6790\u3002<\/p>\n<p> \u3000\u3000\u4e09\u3001\u76f8\u5173\u6570\u636e\u641c\u96c6<\/p>\n<p> \u3000\u3000\u9996\u5148\uff0c\u7531\u4e8e\u6211\u56fd\u80a1\u7968\u5e02\u573a\u624d\u6709\u5341\u591a\u5e74\u7684\u5386\u53f2\uff0c\u5f88\u591a\u6307\u6807\u53c8\u90fd\u662f\u6309\u5e74\u5ea6\u8ba1\u7b97\u7684\uff0c\u5982\u679c\u4ee5\u65f6\u95f4\u4e3a\u4f9d\u636e\u9009\u53d6\u6837\u672c\uff0c\u53ef\u80fd\u4e0d\u5177\u6709\u4ee3\u8868\u6027\uff0c\u6240\u4ee5\u6211\u4eec\u9009\u53d6\u622a\u9762\u6570\u636e\u4f5c\u4e3a\u6837\u672c\u3002\u5176\u6b21\uff0c\u7531\u4e8e\u4e0a\u5e02\u7684\u80a1\u7968\u5f88\u591a\uff0c\u6240\u4ee5\u6837\u672c\u80a1\u7684\u9009\u62e9\u5341\u5206\u5173\u952e\u3002\u6211\u4eec\u4ece\u4eca\u5e741\u67082\u65e5\u63a8\u51fa\u7684\u4e0a\u8bc150\u768450\u652f\u80a1\u7968\u4e2d\u968f\u673a\u62bd\u53d620\u652f\u4f5c\u4e3a\u6837\u672c\u3002\u636e\u4e13\u5bb6\u5206\u6790\uff0c\u4e0a\u8bc150\u6210\u5206\u80a12003\u5e743\u5b63\u5ea6\u7684\u51c0\u5229\u6da6\u4e0e\u5229\u6da6\u603b\u989d\u5360\u540c\u671f\u5168\u90e8A\u80a1\u7684\u6bd4\u4f8b\u5206\u522b\u8fbe\u523042.06%\u4e0e43.05%\uff0c\u662f\u4f18\u8d28\u84dd\u7b79\u80a1\u7684\u7a81\u51fa\u4ee3\u8868\uff0c\u800c\u4e14\u884c\u4e1a\u5206\u5e03\u4e5f\u5f88\u5408\u7406\uff0c\u56e0\u6b64\uff0c\u6211\u4eec\u9009\u53d6\u7684\u6570\u636e\u5177\u5907\u7814\u7a76\u6240\u8981\u6c42\u8fbe\u5230\u7684\u4ee3\u8868\u6027\u3002\u518d\u6b21\uff0c\u6211\u4eec\u9009\u62e9\u4e86\u62a5\u8868\u8ba1\u7b97\u671f\u540e\u768460\u65e5\u5747\u4ef7\u4f5c\u4e3a\u81ea\u53d8\u91cf\u3002\u56e0\u4e3a\u7ecf\u8fc760\u5929\u7684\u5e02\u573a\u8c03\u6574\uff0c\u8be5\u6307\u6807\u66f4\u8d34\u8fd1\u4e8e\u8ba1\u7b97\u671f\u65e5\u80a1\u7968\u7684\u5185\u5728\u4ef7\u503c\u3002<\/p>\n<p> \u3000\u3000\u6307\u6807<\/p>\n<p> \u3000\u3000\u5e8f\u53f7<\/p>\n<p> \u3000\u300060\u65e5\u5747\u4ef7<\/p>\n<p> \u3000\u3000\u6bcf\u80a1\u51c0\u8d44\u4ea7<\/p>\n<p> \u3000\u3000\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387<\/p>\n<p> \u3000\u30001 10.85 3.068 13.04<\/p>\n<p> \u3000\u30002 9.26 3.78 7.84<\/p>\n<p> \u3000\u30003 12.14 4.029 15.64<\/p>\n<p> \u3000\u30004 11.3 4.039 9.08<\/p>\n<p> \u3000\u30005 10.96 3.31 10.48<\/p>\n<p> \u3000\u30006 17.32 5.77 15.69<\/p>\n<p> \u3000\u30007 7.75 2.46 9.29<\/p>\n<p> \u3000\u30008 10.28 2.66 14.42<\/p>\n<p> \u3000\u30009 14.42 3.2954 16.796<\/p>\n<p> \u3000\u300010 7.24 2.83 19.67<\/p>\n<p> \u3000\u300011 8.38 2.14 13.77<\/p>\n<p> \u3000\u300012 4.9 1.879 11.667<\/p>\n<p> \u3000\u300013 5.46 2.46 -7.28<\/p>\n<p> \u3000\u300014 8.52 2.21 13<\/p>\n<p> \u3000\u300015 8.38 3.5034 10.65<\/p>\n<p> \u3000\u300016 11.26 3.2 12.21<\/p>\n<p> \u3000\u300017 14.29 4.09 16.44<\/p>\n<p> \u3000\u300018 4.41 1.99 0.12<\/p>\n<p> \u3000\u300019 14.48 4.835 13.29<\/p>\n<p> \u3000\u300020 16.23 5.03 10<\/p>\n<p> \u3000\u3000\u56db\u3001\u6a21\u578b\u7684\u5efa\u7acb<\/p>\n<p> \u3000\u3000\u6839\u636e\u4ee5\u4e0a\u5206\u6790\uff0c\u6211\u4eec\u5efa\u7acb\u4e86\u4ee5\u4e0b\u6a21\u578b\uff1a<\/p>\n<p> \u3000\u3000Y=C+ \u03b21X1+\u03b22X2+U<\/p>\n<p> \u3000\u3000\u5176\u4e2d\uff1a<\/p>\n<p> \u3000\u3000Y\u4ee3\u8868\u80a1\u796860\u65e5\u5747\u4ef7<\/p>\n<p> \u3000\u3000C\u4ee3\u8868\u5e38\u6570\u9879<\/p>\n<p> \u3000\u3000\u03b2\u4ee3\u8868\u53c2\u6570<\/p>\n<p> \u3000\u3000X1\u4ee3\u8868\u6bcf\u80a1\u51c0\u8d44\u4ea7<\/p>\n<p> \u3000\u3000X2\u4ee3\u8868\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387<\/p>\n<p> \u3000\u3000\u4e94\u3001\u6a21\u578b\u7684\u4f30\u8ba1\u548c\u68c0\u9a8c<\/p>\n<p> \u3000\u3000\u6211\u4eec\u5229\u7528EVIEWS\u8f6f\u4ef6\u548c\u6700\u5c0f\u4e8c\u4e58\u6cd5\u8fdb\u884c\u56de\u5f52\u5206\u6790\u53ca\u7edf\u8ba1\u68c0\u9a8c\u5f97\u51fa\u4ee5\u4e0b\u7ed3\u679c<\/p>\n<p> \u3000\u3000Dependent Variable\uff1a Y<\/p>\n<p> \u3000\u3000Method\uff1a Least Squares<\/p>\n<p> \u3000\u3000Date\uff1a 05\/12\/04 Time\uff1a 14:59<\/p>\n<p> \u3000\u3000Sample\uff1a 1 20<\/p>\n<p> \u3000\u3000Included observations\uff1a 20<\/p>\n<p> \u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.<\/p>\n<p> \u3000\u3000X1 2.722779 0.352973 7.713854 0.0000<\/p>\n<p> \u3000\u3000X2 0.167501 0.062923 2.661985 0.0164<\/p>\n<p> \u3000\u3000C -0.563666 1.233640 -0.456913 0.6535<\/p>\n<p> \u3000\u3000R-squared 0.834815 Mean dependent var 10.39150<\/p>\n<p> \u3000\u3000Adjusted R-squared 0.815381 S.D. dependent var 3.666757<\/p>\n<p> \u3000\u3000S.E. of regression 1.575506 Akaike info criterion 3.884511<\/p>\n<p> \u3000\u3000Sum squared resid 42.19771 Schwarz criterion 4.033871<\/p>\n<p> \u3000\u3000Log likelihood -35.84511 F-statistic 42.95741<\/p>\n<p> \u3000\u3000Durbin-Watson stat 2.659659 Prob\uff08F-statistic\uff09 0.000000<\/p>\n<p> \u3000\u3000\u56de\u5f52\u65b9\u7a0b\u5982\u4e0b\uff1a<\/p>\n<p> \u3000\u3000Y= -0.563666 + 2.722779X1 + 0.167501X2<\/p>\n<p> \u3000\u3000\uff081.233640\uff09 \uff080.352973\uff09 \uff080.062923\uff09<\/p>\n<p> \u3000\u3000t=\uff08-0.456913\uff09 \uff087.713854\uff09 \uff082.661985\uff09<\/p>\n<p> \u3000\u3000R2= 0.834815 F=42.95741 DW=2.659659<\/p>\n<p> \u3000\u3000\u7ecf\u6d4e\u610f\u4e49\u7684\u68c0\u9a8c<\/p>\n<p> \u3000\u3000\u4ece\u7ecf\u6d4e\u610f\u4e49\u4e0a\u6765\u8bf4\uff0c\u80a1\u7968\u4ef7\u683c\u968f\u80a1\u7968\u4e0e\u6bcf\u80a1\u51c0\u8d44\u4ea7\u53ca\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\u6210\u6b63\u6bd4\uff0cX1\u548cX2\u7684\u7cfb\u6570\u03b21\u548c\u03b22\u5747\u4e3a\u6b63\u6570\uff0c\u8868\u793a\u968f\u7740\u6bcf\u80a1\u51c0\u8d44\u4ea7\u548c\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\u7684\u589e\u52a0\uff0c\u80a1\u7968\u7684\u4ef7\u503c\u4f1a\u4e0a\u5347\uff0c\u8fd9\u662f\u7b26\u5408\u7ecf\u6d4e\u610f\u4e49\u7684\u3002\u800cC\u4e3a\u6837\u672c\u56de\u5f52\u65b9\u7a0b\u7684\u622a\u8ddd\uff0c\u8868\u793a\u5f53\u6bcf\u80a1\u51c0\u8d44\u4ea7\u548c\u6240\u6709\u7740\u6743\u76ca\u6536\u76ca\u7387\u5747\u4e3a\u96f6\u65f6\u7684\u80a1\u7968\u4ef7\u503c\uff0c\u5728\u4e0a\u8ff0\u56de\u5f52\u65b9\u7a0b\u4e2d\u4e3a\u8d1f\u6570\uff0c\u8fd9\u663e\u7136\u662f\u4e0d\u7b26\u5408\u7ecf\u6d4e\u610f\u4e49\u7684\u3002<\/p>\n<p> \u3000\u3000\u7edf\u8ba1\u63a8\u65ad\u7684\u68c0\u9a8c<\/p>\n<p> \u3000\u3000R2=0.834815 \u8bf4\u660e\u603b\u79bb\u5dee\u5e73\u65b9\u548c\u768483.4815%\u88ab\u6837\u672c\u56de\u5f52\u76f4\u7ebf\u89e3\u91ca\uff0c\u4ec5\u6709\u4e0d\u8db317%\u672a\u88ab\u89e3\u91ca\uff0c\u56e0\u6b64\u6837\u672c\u56de\u5f52\u76f4\u7ebf\u5bf9\u6837\u672c\u7684\u62df\u5408\u4f18\u5ea6\u662f\u5f88\u9ad8\u7684\u3002<\/p>\n<p> \u3000\u3000\u03b21\u7684t\u7edf\u8ba1\u91cf\u4e3a7.713854\uff0c\u5728\u7ed9\u5b9a\u663e\u8457\u6027\u6c34\u5e73\u4e3a0.05\u7684\u60c5\u51b5\u4e0b\uff0c\u67e5T\u5206\u5e03\u8868\u5728\u81ea\u7531\u5ea6\u4e3aN-2=18\u4e0b\u7684\u4e34\u754c\u503c\u4e3a2.101\uff0c\u56e0\u4e3a7.713854\u5927\u4e8e2.101\uff0c\u6240\u4ee5\u62d2\u7edd\u539f\u5047\u8bbe\u3002\u8868\u660e\u6bcf\u80a1\u51c0\u8d44\u4ea7\u5bf9\u80a1\u7968\u4ef7\u503c\u7684\u5f71\u54cd\u663e\u8457\u3002<\/p>\n<p> \u3000\u3000\u03b22\u7684t \u7edf\u8ba1\u91cf\u4e3a2.661985\uff0c\u5728\u7ed9\u5b9a\u663e\u8457\u6027\u6c34\u5e73\u4e3a0.05\u7684\u60c5\u51b5\u4e0b\uff0c\u67e5T\u5206\u5e03\u8868\u5728\u81ea\u7531\u5ea6\u4e3aN-2=18\u4e0b\u7684\u4e34\u754c\u503c\u4e3a2.101\uff0c\u56e0\u4e3a2.661985\u5927\u4e8e2.101\uff0c\u6240\u4ee5\u62d2\u7edd\u539f\u5047\u8bbe\u3002\u8868\u660e\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\u5bf9\u80a1\u7968\u4ef7\u503c\u7684\u5f71\u54cd\u663e\u8457\u3002<\/p>\n<p> \u3000\u3000\u800c\u5e38\u6570\u9879C\u7684t\u7edf\u8ba1\u91cf\u4e3a-0.456913\uff0c-2.101\u300a-0.456913\u300a2.101\uff0c\u63a5\u53d7\u539f\u5047\u8bbe\uff0c\u8868\u660e\u5e38\u6570\u9879C\u5bf9\u80a1\u7968\u4ef7\u503c\u7684\u5f71\u54cd\u4e0d\u663e\u8457\u3002<\/p>\n<p> \u3000\u3000\u7efc\u5408\u7ecf\u6d4e\u610f\u4e49\u68c0\u9a8c\u548c\u7edf\u8ba1\u63a8\u65ad\u68c0\u9a8c\uff0c\u6211\u4eec\u5254\u9664\u4e86\u56de\u5f52\u6a21\u578b\u4e2d\u7684\u5e38\u6570\u9879C\uff0c\u5373\u5f53\u80a1\u7968\u7684\u6bcf\u80a1\u51c0\u8d44\u4ea7\u53ca\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\u5747\u4e3a\u96f6\u65f6\uff0c\u80a1\u7968\u7684\u4ef7\u503c\u4e3a\u96f6\u3002\u8fd9\u663e\u7136\u662f\u7b26\u5408\u7ecf\u6d4e\u610f\u4e49\u7684\u3002<\/p>\n<p> \u3000\u3000\u4e8e\u662f\u6211\u4eec\u5f97\u5982\u4e0b\u6a21\u578b\uff1a<\/p>\n<p> \u3000\u3000Y=\u03b21X1+\u03b22X2<\/p>\n<p> \u3000\u3000\u6211\u4eec\u5229\u7528EVIEWS\u8f6f\u4ef6\uff0c\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5\u8fdb\u884c\u56de\u5f52\u5206\u6790\u548c\u7edf\u8ba1\u68c0\u9a8c\u5f97\u5982\u4e0b\u7ed3\u679c\uff1a<\/p>\n<p> \u3000\u3000Dependent Variable\uff1a Y<\/p>\n<p> \u3000\u3000Method\uff1a Least Squares<\/p>\n<p> \u3000\u3000Date\uff1a 05\/12\/04 Time\uff1a 15:00<\/p>\n<p> \u3000\u3000Sample\uff1a 1 20<\/p>\n<p> \u3000\u3000Included observations\uff1a 20<\/p>\n<p> \u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.<\/p>\n<p> \u3000\u3000X1 2.596293 0.214121 12.12533 0.0000<\/p>\n<p> \u3000\u3000X2 0.158943 0.058736 2.706069 0.0145<\/p>\n<p> \u3000\u3000R-squared 0.832786 Mean dependent var 10.39150<\/p>\n<p> \u3000\u3000Adjusted R-squared 0.823497 S.D. dependent var 3.666757<\/p>\n<p> \u3000\u3000S.E. of regression 1.540489 Akaike info criterion 3.796717<\/p>\n<p> \u3000\u3000Sum squared resid 42.71592 Schwarz criterion 3.896290<\/p>\n<p> \u3000\u3000Log likelihood -35.96717 Durbin-Watson stat 2.608111<\/p>\n<p> \u3000\u3000\u5f97\u56de\u5f52\u65b9\u7a0b\u5982\u4e0b\uff1a<\/p>\n<p> \u3000\u3000Y= 2.596293 X1 + 0.158943X2<\/p>\n<p> \u3000\u3000\uff080.214121\uff09 \uff080.058736\uff09<\/p>\n<p> \u3000\u3000t=\uff0812.12533\uff09 \uff082.706069\uff09<\/p>\n<p> \u3000\u3000R2= 0.832786 DW=2.608111<\/p>\n<p> \u3000\u3000R2=0.832786 \u8bf4\u660e\u603b\u79bb\u5dee\u5e73\u65b9\u548c\u768483.2786%\u88ab\u6837\u672c\u56de\u5f52\u76f4\u7ebf\u89e3\u91ca\uff0c\u4ec5\u6709\u4e0d\u8db317%\u672a\u88ab\u89e3\u91ca\uff0c\u56e0\u6b64\u6837\u672c\u56de\u5f52\u76f4\u7ebf\u5bf9\u6837\u672c\u7684\u62df\u5408\u4f18\u5ea6\u662f\u5f88\u9ad8\u7684\u3002<\/p>\n<p> \u3000\u3000\u03b21\u7684t\u7edf\u8ba1\u91cf\u4e3a12.12533\uff0c\u5728\u7ed9\u5b9a\u663e\u8457\u6027\u6c34\u5e73\u4e3a0.05\u7684\u60c5\u51b5\u4e0b\uff0c\u67e5T\u5206\u5e03\u8868\u5728\u81ea\u7531\u5ea6\u4e3aN-2=18\u4e0b\u7684\u4e34\u754c\u503c\u4e3a2.101\uff0c\u56e0\u4e3a12.12533\u5927\u4e8e2.101\uff0c\u6240\u4ee5\u62d2\u7edd\u539f\u5047\u8bbe\u3002\u8868\u660e\u8868\u660e\u6bcf\u80a1\u51c0\u8d44\u4ea7\u5bf9\u80a1\u7968\u4ef7\u503c\u7684\u5f71\u54cd\u663e\u8457\u3002<\/p>\n<p> \u3000\u3000\u03b22\u7684t \u7edf\u8ba1\u91cf\u4e3a2.706069\uff0c\u5728\u7ed9\u5b9a\u663e\u8457\u6027\u6c34\u5e73\u4e3a0.05\u7684\u60c5\u51b5\u4e0b\uff0c\u67e5T\u5206\u5e03\u8868\u5728\u81ea\u7531\u5ea6\u4e3aN-2=18\u4e0b\u7684\u4e34\u754c\u503c\u4e3a2.101\uff0c\u56e0\u4e3a2.706069\u5927\u4e8e2.101\uff0c\u6240\u4ee5\u62d2\u7edd\u539f\u5047\u8bbe\u3002\u8868\u660e\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\u5bf9\u80a1\u7968\u4ef7\u503c\u7684\u5f71\u54cd\u663e\u8457\u3002<\/p>\n<p> \u3000\u3000\u8ba1\u91cf\u7ecf\u6d4e\u7684\u68c0\u9a8c<\/p>\n<p> \u3000\u3000\u591a\u91cd\u5171\u7ebf\u6027\u7684\u68c0\u9a8c<\/p>\n<p> \u3000\u3000X1 X2<\/p>\n<p> \u3000\u3000X1 1 0.292084631717<\/p>\n<p> \u3000\u3000X2 0.292084631717 1<\/p>\n<p> \u3000\u3000\u7531\u8868\u53ef\u4ee5\u770b\u51fa\uff0cX1\u3001X2\u4e0d\u5b58\u5728\u591a\u91cd\u5171\u7ebf\u6027\u3002<\/p>\n<p> \u3000\u30002\uff0e\u5f02\u65b9\u5dee\u7684\u68c0\u9a8c<\/p>\n<p> \u3000\u3000\u56fe\u793a\u6cd5<\/p>\n<p> \u3000\u3000\u968fX1\u3001X2\u7684\u53d8\u5316e2\u6ca1\u6709\u660e\u663e\u7cfb\u7edf\u6027\u53d8\u5316\uff0c\u6240\u4ee5\u4ece\u56fe\u53ef\u4ee5\u770b\u51fa\u6a21\u578b\u4e0d\u5b58\u5728\u5f02\u65b9\u5dee\u3002<\/p>\n<p> \u3000\u3000\uff082\uff09Goldfele-Quandt\u68c0\u9a8c\uff1a<\/p>\n<p> \u3000\u3000Dependent Variable\uff1a Y<\/p>\n<p> \u3000\u3000Method\uff1a Least Squares<\/p>\n<p> \u3000\u3000Date\uff1a 06\/04\/04 Time\uff1a 09:09<\/p>\n<p> \u3000\u3000Sample\uff1a 1 8<\/p>\n<p> \u3000\u3000Included observations\uff1a 8<\/p>\n<p> \u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.<\/p>\n<p> \u3000\u3000X2 0.115917 0.068733 1.686478 0.1427<\/p>\n<p> \u3000\u3000X1 2.589111 0.362659 7.139240 0.0004<\/p>\n<p> \u3000\u3000R-squared 0.510444 Mean dependent var 7.117500<\/p>\n<p> \u3000\u3000Adjusted R-squared 0.428852 S.D. dependent var 2.034437<\/p>\n<p> \u3000\u3000S.E. of regression 1.537513 Akaike info criterion 3.910527<\/p>\n<p> \u3000\u3000Sum squared resid 14.18368 Schwarz criterion 3.930388<\/p>\n<p> \u3000\u3000Log likelihood -13.64211 Durbin-Watson stat 1.763852<\/p>\n<p> \u3000\u3000Dependent Variable\uff1a Y<\/p>\n<p> \u3000\u3000Method\uff1a Least Squares<\/p>\n<p> \u3000\u3000Date\uff1a 06\/04\/04 Time\uff1a 09:09<\/p>\n<p> \u3000\u3000Sample\uff1a 13 20<\/p>\n<p> \u3000\u3000Included observations\uff1a 8<\/p>\n<p> \u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.<\/p>\n<p> \u3000\u3000X2 0.152842 0.171517 0.891123 0.4072<\/p>\n<p> \u3000\u3000X1 2.546468 0.491337 5.182728 0.0020<\/p>\n<p> \u3000\u3000R-squared 0.818279 Mean dependent var 12.92500<\/p>\n<p> \u3000\u3000Adjusted R-squared 0.787992 S.D. dependent var 3.204158<\/p>\n<p> \u3000\u3000S.E. of regression 1.475331 Akaike info criterion 3.827960<\/p>\n<p> \u3000\u3000Sum squared resid 13.05962 Schwarz criterion 3.847821<\/p>\n<p> \u3000\u3000Log likelihood -13.31184 Durbin-Watson stat 0.908317<\/p>\n<p> \u3000\u3000\u4ee5X1\u6392\u5e8f\u540e\uff0c\u6c42\u5f97\u2211e12=14.8368\uff0c\u2211e22 =13.05962<\/p>\n<p> \u3000\u3000F=14.8368\/13.05962=1.0861<\/p>\n<p> \u3000\u3000\u5728\u7ed9\u5b9a\u663e\u8457\u6027\u6c34\u5e73\u4e3a0.05\u7684\u60c5\u51b5\u4e0b\uff0c\u67e5F\u5206\u5e03\u8868\u5728\u81ea\u7531\u5ea6\u4e3a\uff08n-c\uff09\/2-k=6\u4e0b\u7684\u4e34\u754c\u503c\u4e3a4.28\uff0c\u56e0\u4e3a4.28\u5927\u4e8e1.0861\uff0c\u6240\u4ee5\u63a5\u53d7H0\uff0c\u8868\u660e\u65e0\u5f02\u65b9\u5dee<\/p>\n<p> \u3000\u3000Dependent Variable\uff1a Y<\/p>\n<p> \u3000\u3000Method\uff1a Least Squares<\/p>\n<p> \u3000\u3000Date\uff1a 06\/04\/04 Time\uff1a 09:10<\/p>\n<p> \u3000\u3000Sample\uff1a 1 8<\/p>\n<p> \u3000\u3000Included observations\uff1a 8<\/p>\n<p> \u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.<\/p>\n<p> \u3000\u3000X2 0.124515 0.097287 1.279881 0.2478<\/p>\n<p> \u3000\u3000X1 2.574255 0.245557 10.48332 0.0000<\/p>\n<p> \u3000\u3000R-squared 0.875434 Mean dependent var 9.218750<\/p>\n<p> \u3000\u3000Adjusted R-squared 0.854673 S.D. dependent var 3.715525<\/p>\n<p> \u3000\u3000S.E. of regression 1.416426 Akaike info criterion 3.746468<\/p>\n<p> \u3000\u3000Sum squared resid 12.03757 Schwarz criterion 3.766329<\/p>\n<p> \u3000\u3000Log likelihood -12.98587 Durbin-Watson stat 1.361776<\/p>\n<p> \u3000\u3000Dependent Variable\uff1a Y<\/p>\n<p> \u3000\u3000Method\uff1a Least Squares<\/p>\n<p> \u3000\u3000Date\uff1a 06\/04\/04 Time\uff1a 09:10<\/p>\n<p> \u3000\u3000Sample\uff1a 13 20<\/p>\n<p> \u3000\u3000Included observations\uff1a 8<\/p>\n<p> \u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.<\/p>\n<p> \u3000\u3000X2 0.137181 0.136256 1.006783 0.3529<\/p>\n<p> \u3000\u3000X1 2.721127 0.556496 4.889749 0.0027<\/p>\n<p> \u3000\u3000R-squared 0.715329 Mean dependent var 12.31875<\/p>\n<p> \u3000\u3000Adjusted R-squared 0.667884 S.D. dependent var 3.453373<\/p>\n<p> \u3000\u3000S.E. of regression 1.990162 Akaike info criterion 4.426627<\/p>\n<p> \u3000\u3000Sum squared resid 23.76446 Schwarz criterion 4.446487<\/p>\n<p> \u3000\u3000Log likelihood -15.70651 Durbin-Watson stat 2.218959<\/p>\n<p> \u3000\u3000\u4ee5X2\u6392\u5e8f\u540e\uff0c\u6c42\u5f97\u2211e12=12.03757\uff0c\u2211e22 =23.76446<\/p>\n<p> \u3000\u3000F=23.76446\/12.03757=1.9742<\/p>\n<p> \u3000\u3000\u5728\u7ed9\u5b9a\u663e\u8457\u6027\u6c34\u5e73\u4e3a0.05\u7684\u60c5\u51b5\u4e0b\uff0c\u67e5F\u5206\u5e03\u8868\u5728\u81ea\u7531\u5ea6\u4e3a\uff08n-c\uff09\/2-k=6\u4e0b\u7684\u4e34\u754c\u503c\u4e3a4.28\uff0c\u56e0\u4e3a4.28\u5927\u4e8e1.9742\uff0c\u6240\u4ee5\u63a5\u53d7H0\uff0c\u8868\u660e\u65e0\u5f02\u65b9\u5dee<\/p>\n<p> \u3000\u3000\uff083\uff09White\u68c0\u9a8c\uff1a<\/p>\n<p> \u3000\u3000White Heteroskedasticity Test\uff1a<\/p>\n<p> \u3000\u3000F-statistic 1.883353 Probability 0.161203<\/p>\n<p> \u3000\u3000Obs*R-squared 8.042756 Probability 0.153895<\/p>\n<p> \u3000\u3000Test Equation\uff1a<\/p>\n<p> \u3000\u3000Dependent Variable\uff1a RESID^2<\/p>\n<p> \u3000\u3000Method\uff1a Least Squares<\/p>\n<p> \u3000\u3000Date\uff1a 05\/13\/04 Time\uff1a 14:07<\/p>\n<p> \u3000\u3000Sample\uff1a 1 20<\/p>\n<p> \u3000\u3000Included observations\uff1a 20<\/p>\n<p> \u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.<\/p>\n<p> \u3000\u3000C -10.37809 7.592657 -1.366859 0.1932<\/p>\n<p> \u3000\u3000X1 4.503260 3.935652 1.144222 0.2717<\/p>\n<p> \u3000\u3000X1^2 -0.250357 0.611059 -0.409711 0.6882<\/p>\n<p> \u3000\u3000X1*X2 -0.249335 0.219636 -1.135221 0.2753<\/p>\n<p> \u3000\u3000X2 0.480980 0.513953 0.935845 0.3652<\/p>\n<p> \u3000\u3000X2^2 0.030638 0.015260 2.007810 0.0644<\/p>\n<p> \u3000\u3000R-squared 0.402138 Mean dependent var 2.135796<\/p>\n<p> \u3000\u3000Adjusted R-squared 0.188616 S.D. dependent var 3.159756<\/p>\n<p> \u3000\u3000S.E. of regression 2.846210 Akaike info criterion 5.173178<\/p>\n<p> \u3000\u3000Sum squared resid 113.4127 Schwarz criterion 5.471898<\/p>\n<p> \u3000\u3000Log likelihood -45.73178 F-statistic 1.883353<\/p>\n<p> \u3000\u3000Durbin-Watson stat 2.610948 Prob\uff08F-statistic\uff09 0.161203<\/p>\n<p> \u3000\u3000\u7531\u62df\u5408\u7684\u6570\u636e\u53ef\u77e5\uff0cN *R^2=200.347103=6.94206\uff0c\u67e5\u8868\u5f970.05\uff085\uff09=9.48773\uff0cN*R^2\u300a0.05\uff085\uff09\uff0c\u63a5\u53d7H0\uff0c\u8868\u660e\u6a21\u578b\u65e0\u5f02\u65b9\u5dee\u3002<\/p>\n<p> \u3000\u3000\u7efc\u4e0a\u6240\u8ff0\uff0c\u6a21\u578b\u65e0\u5f02\u65b9\u5dee\u3002<\/p>\n<p> \u3000\u30003\u3001\u81ea\u76f8\u5173\u68c0\u9a8c<\/p>\n<p> \u3000\u3000\u7528DW\u6cd5\u68c0\u9a8c\u65b9\u7a0b\u7684\u81ea\u76f8\u5173\u6027\uff0c\u65b9\u7a0bDW\u503c\u4e3a2.608111<\/p>\n<p> \u3000\u3000\u67e5\u8868\u5f97Dl=1.100 Du=1.537 4-Du=2.463<\/p>\n<p> \u3000\u3000Du\u300ad\u300a4-Du \u8868\u660e\u6240\u5efa\u6a21\u578b\u65e0\u81ea\u76f8\u5173\u3002<\/p>\n<p> \u3000\u3000\u7efc\u4e0a\u6240\u8ff0\uff0c\u6a21\u578b\u7684\u62df\u5408\u4f18\u5ea6\u8f83\u597d\uff0c\u4e14\u65e0\u591a\u91cd\u5171\u7ebf\u6027\u3001\u5f02\u65b9\u5dee\u3001\u81ea\u76f8\u5173\u7b49\u95ee\u9898\uff0c\u6709\u8f83\u597d\u7684\u5b9e\u7528\u6027\uff0c\u53ef\u7528\u4e8e\u6307\u5bfc\u5b9e\u8df5\u3002\u56de\u5f52\u65b9\u7a0b\u5982\u4e0b\uff1a<\/p>\n<p> \u3000\u3000Y= 2.596293 X1 + 0.158943X2<\/p>\n<p> \u3000\u3000\uff080.214121\uff09 \uff080.058736\uff09<\/p>\n<p> \u3000\u3000t=\uff0812.12533\uff09 \uff082.706069\uff09<\/p>\n<p> \u3000\u3000R2= 0.832786 DW=2.608111<\/p>\n<p> \u3000\u3000\u516d\u3001\u6a21\u578b\u603b\u7ed3<\/p>\n<p> \u3000\u3000\u7531\u6211\u4eec\u7684\u6a21\u578b\u53ef\u77e5\u5f53\u6bcf\u80a1\u51c0\u8d44\u4ea7\u589e\u52a0\u4e00\u4e2a\u5355\u4f4d\u65f6\u80a1\u7968\u4ef7\u503c\u4e0a\u53472.596293\u4e2a\u5355\u4f4d\uff0c\u5f53\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\u63d0\u9ad8\u4e00\u4e2a\u5355\u4f4d\u65f6\u80a1\u7968\u4ef7\u503c\u4e0a\u53470.158943\u4e2a\u5355\u4f4d\u3002\u5728\u5b9e\u9645\u6295\u8d44\u4e2d\uff0c\u5df2\u77e5\u4e00\u4e2a\u516c\u53f8\u80a1\u7968\u7684\u6bcf\u80a1\u51c0\u8d44\u4ea7\u548c\u6240\u6709\u8005\u6743\u76ca\u6536\u76ca\u7387\uff0c\u7528\u6211\u4eec\u7684\u65b9\u7a0b\u8ba1\u7b97\u51fa\u8be5\u516c\u53f8\u80a1\u7968\u7684\u5185\u5728\u4ef7\u503c\uff0c\u4e0e\u5f53\u524d\u5e02\u573a\u4ef7\u683c\u8fdb\u884c\u6bd4\u8f83\uff0c\u5f53\u5e02\u573a\u4ef7\u683c\u4f4e\u4e8e\u8ba1\u7b97\u6240\u5f97\u7684\u5185\u5728\u4ef7\u503c\u65f6\uff0c\u5219\u8be5\u80a1\u7968\u6709\u6295\u8d44\u4ef7\u503c\uff0c\u53cd\u4e4b\uff0c\u5219\u4e0d\u5b9c\u6295\u8d44\u3002<\/p>\n<p> \uff08\u80a1\u7968\u8d44\u6e90\u9986lixiangluntan.com\uff09<a href=\"http:\/\/www.lixiangluntan.com\" target=\"_blank\" rel=\"noopener noreferrer\">\u9ed1\u9a6c\u80a1\u7968<\/a><\/p>\n<blockquote style=\"font-size:15px;background-color: #F5F5F5;border:none;padding: 5px;margin:15px;line-height: 1.6;text-indent: 2em;\">\n<p style=\"margin-bottom:5px;\">\u4ef7\u503c\u6295\u8d44\u4e0e\u8d8b\u52bf\u6295\u8d44\u7684\u5173\u7cfb\u5982\u4f55<\/p>\n<p>\u3000\u3000\u4ef7\u503c\u6295\u8d44\u672a\u5fc5\u843d\u540e\u8d8b\u52bf\u6295\u8d44<\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>\u3000\u3000 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