居民消费水平如何让与居民收入建立多元回归模型 本人想要一篇关于居民消费的论文
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\u3000\u3000\u4e00\u3001\u5f15\u8a00
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\u3000\u3000obs Y X1 X2 X3 X4 X5
\u3000\u30001978 184.0000 3624.100 343.4000 133.6000 12.00000 100.7000
\u3000\u30001979 207.0000 4038.200 405.0000 160.2000 13.34000 101.9000
\u3000\u30001980 236.0000 4517.800 477.6000 191.3000 11.87000 107.5000
\u3000\u30001981 262.0000 4862.400 500.4000 223.4000 14.55000 102.5000
\u3000\u30001982 284.0000 5294.700 535.3000 270.1000 15.68000 102.0000
\u3000\u30001983 311.0000 5934.500 564.6000 309.8000 13.29000 102.0000
\u3000\u30001984 354.0000 7171.000 652.1000 355.3000 13.08000 102.7000
\u3000\u30001985 437.0000 8964.400 739.1000 397.6000 14.26000 109.3000
\u3000\u30001986 485.0000 10202.20 899.6000 423.8000 15.57000 106.5000
\u3000\u30001987 550.0000 11962.50 1002.200 462.6000 16.61000 107.3000
\u3000\u30001988 693.0000 14928.30 1181.400 545.0000 15.73000 118.8000
\u3000\u30001989 762.0000 16909.20 1373.900 601.5000 15.04000 118.0000
\u3000\u30001990 803.0000 18547.90 1510.200 686.3000 14.39000 103.1000
\u3000\u30001991 896.0000 21617.80 1700.600 708.6000 12.98000 103.4000
\u3000\u30001992 1070.000 26638.10 2026.600 784.0000 11.60000 106.4000
\u3000\u30001993 1331.000 34634.40 2577.400 921.6000 11.45000 114.7000
\u3000\u30001994 1746.000 46759.40 3496.200 1221.000 11.21000 124.1000
\u3000\u30001995 2236.000 58478.10 4283.000 1577.700 10.55000 117.1000
\u3000\u30001996 2641.000 67884.60 4838.900 1926.100 10.42000 108.3000
\u3000\u30001997 2834.000 74462.60 5160.300 2090.100 10.06000 102.8000
\u3000\u30001998 2972.000 78345.20 5425.100 2102.000 9.140000 99.20000
\u3000\u30001999 3180.000 82067.50 5854.000 2210.300 8.180000 98.50000
\u3000\u30002000 3415.000 89468.10 6280.000 2253.400 7.580000 100.1000
\u3000\u30002001 3654.000 97314.80 6859.600 2366.400 6.950000 102.1000
\u3000\u30002002 3910.000 105172.3 7702.800 2745.600 6.450000 99.70000
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\u3000\u3000\u5047\u5b9a\u6a21\u578b\u4e2d\u968f\u673a\u8bef\u5dee\u9879 \u6ee1\u8db3\u53e4\u5178\u5047\u5b9a\uff0c\u8fd0\u7528OLS\u6cd5\u4f30\u8ba1\u6a21\u578b\u53c2\u6570\uff0c\u7ed3\u679c\u5982\u4e0b\uff1a
\u3000\u3000Dependent Variable: Y
\u3000\u3000Method: Least Squares
\u3000\u3000Date: 12/12/05 Time: 14:50
\u3000\u3000Sample: 1978 2002
\u3000\u3000Included observations: 25
\u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.
\u3000\u3000C 93.22748 10.02780 9.296901 0.0000
\u3000\u3000X1 0.036811 0.000203 181.1983 0.0000
\u3000\u3000R-squared 0.999300 Mean dependent var 1418.120
\u3000\u3000Adjusted R-squared 0.999270 S.D. dependent var 1269.558
\u3000\u3000S.E. of regression 34.31248 Akaike info criterion 9.985514
\u3000\u3000Sum squared resid 27078.96 Schwarz criterion 10.08302
\u3000\u3000Log likelihood -122.8189 F-statistic 32832.82
\u3000\u3000Durbin-Watson stat 0.894184 Prob(F-statistic) 0.000000
\u3000\u3000(9.2969) (181.1983)
\u3000\u3000\u5176\u4e2d\uff0c\u53ef\u51b3\u7cfb\u6570 =0.9993\u3002\u4ece\u56de\u5f52\u7ed3\u679c\u53ef\u4ee5\u770b\u51fa\uff0c\u6a21\u578b\u62df\u5408\u5ea6\u5f88\u597d\uff0c\u53ef\u51b3\u7cfb\u6570\u5f88\u9ad8\uff0c\u8fd9\u4e5f\u8868\u660e\u56fd\u5185\u751f\u4ea7\u603b\u503c\u786e\u5b9e\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u6709\u663e\u8457\u5f71\u54cd\u3002\u5176\u4e2d\uff0cGDP\u6bcf\u589e\u957f1\u4ebf\u5143\uff0c\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u5e73\u5747\u589e\u52a00.04\u5143\u3002
\u3000\u30002\u3001\u5c45\u6c11\u4eba\u5747\u6536\u5165\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5f71\u54cd
\u3000\u3000\u5982\u679c\u8bf4\u56fd\u5185\u751f\u4ea7\u603b\u503c\u662f\u5b8f\u89c2\u5f71\u54cd\u56e0\u7d20\uff0c\u90a3\u4e48\u5c45\u6c11\u7684\u4eba\u5747\u6536\u5165\u5c31\u662f\u5fae\u89c2\u5f71\u54cd\u56e0\u7d20\u3002\u7531\u4e8e\u6211\u56fd\u57ce\u4e61\u5dee\u8ddd\u6bd4\u8f83\u663e\u8457\uff0c\u4e8e\u662f\u5728\u8fd9\u91cc\u5206\u522b\u8003\u5bdf\u4e86\u57ce\u9547\u5c45\u6c11\u548c\u519c\u6751\u5c45\u6c11\u7684\u53ef\u652f\u914d\u6536\u5165\u5bf9\u6d88\u8d39\u6c34\u5e73\u7684\u5f71\u54cd\u3002\u8bbe\u57ce\u9547\u5c45\u6c11\u4eba\u5747\u53ef\u652f\u914d\u6536\u5165\u4e3a \uff0c\u519c\u6751\u5c45\u6c11\u4eba\u5747\u7eaf\u6536\u5165\u4e3a \uff0c\u5b83\u4eec\u4e0e\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5173\u7cfb\u4e3a\uff1a
\u3000\u3000\uff0c
\u3000\u3000\u8fd0\u7528OLS\u6cd5\u4f30\u8ba1\u7ed3\u679c\u5982\u4e0b\uff1a
\u3000\u3000\u57ce\u9547\u5c45\u6c11\u53ef\u652f\u914d\u6536\u5165\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5f71\u54cd
\u3000\u3000Dependent Variable: Y
\u3000\u3000Method: Least Squares
\u3000\u3000Date: 12/12/05 Time: 14:51
\u3000\u3000Sample: 1978 2002
\u3000\u3000Included observations: 25
\u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.
\u3000\u3000C 9.629737 18.72683 0.514222 0.6120
\u3000\u3000X2 0.530391 0.005288 100.2944 0.0000
\u3000\u3000R-squared 0.997719 Mean dependent var 1418.120
\u3000\u3000Adjusted R-squared 0.997620 S.D. dependent var 1269.558
\u3000\u3000S.E. of regression 61.94203 Akaike info criterion 11.16689
\u3000\u3000Sum squared resid 88246.75 Schwarz criterion 11.26440
\u3000\u3000Log likelihood -137.5862 F-statistic 10058.98
\u3000\u3000Durbin-Watson stat 0.834725 Prob(F-statistic) 0.000000
\u3000\u3000\uff080.5142\uff09 \uff08100.2944\uff09 =0.9977
\u3000\u3000\u519c\u6751\u5c45\u6c11\u7eaf\u6536\u5165\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5f71\u54cd
\u3000\u3000Dependent Variable: Y
\u3000\u3000Method: Least Squares
\u3000\u3000Date: 12/12/05 Time: 14:51
\u3000\u3000Sample: 1978 2002
\u3000\u3000Included observations: 25
\u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.
\u3000\u3000C -113.4612 28.65894 -3.959014 0.0006
\u3000\u3000X3 1.491763 0.021689 68.78073 0.0000
\u3000\u3000R-squared 0.995162 Mean dependent var 1418.120
\u3000\u3000Adjusted R-squared 0.994951 S.D. dependent var 1269.558
\u3000\u3000S.E. of regression 90.20660 Akaike info criterion 11.91870
\u3000\u3000Sum squared resid 187156.3 Schwarz criterion 12.01621
\u3000\u3000Log likelihood -146.9838 F-statistic 4730.789
\u3000\u3000Durbin-Watson stat 1.010615 Prob(F-statistic) 0.000000
\u3000\u3000\uff08-3.9590\uff09 \uff0868.7807\uff09 R²=0.9952
\u3000\u3000\u7531\u6570\u636e\u5206\u6790\u7684\u7ed3\u8bba\u53ef\u77e5\uff0c\u519c\u6751\u5c45\u6c11\u4eba\u5747\u7eaf\u6536\u5165\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5f71\u54cd\u5927\u5927\u8d85\u8fc7\u4e86\u57ce\u9547\u5c45\u6c11\u4eba\u5747\u53ef\u652f\u914d\u6536\u5165\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5f71\u54cd\u3002\u9020\u6210\u8fd9\u79cd\u60c5\u51b5\uff0c\u4e3b\u8981\u6709\u4ee5\u4e0b\u51e0\u4e2a\u539f\u56e0\uff1a\u7b2c\u4e00\u662f\u6211\u56fd\u662f\u519c\u6c11\u4eba\u53e3\u5360\u7edd\u5927\u591a\u6570\u7684\u56fd\u5bb6\uff0c\u800c\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u662f\u4ee5\u4eba\u53e3\u6570\u4e3a\u6743\u6570\u5bf9\u519c\u6751\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u548c\u57ce\u9547\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u8fdb\u884c\u52a0\u6743\u5e73\u5747\u8ba1\u7b97\u800c\u5f97\u5230\u7684\uff1b\u7b2c\u4e8c\u662f\u519c\u6751\u5c45\u6c11\u7684\u6d88\u8d39\u52a8\u529b\u8fdc\u8fdc\u5927\u4e8e\u57ce\u9547\u5c45\u6c11\u3002\u6839\u636e\u8054\u5408\u56fd\u7cae\u519c\u7ec4\u7ec7\u63d0\u51fa\u7684\u6807\u51c6\uff0c\u6069\u683c\u5c14\u7cfb\u6570\u572859%\u4ee5\u4e0a\u4e3a\u8d2b\u56f0\uff0c50\u201459%\u4e3a\u6e29\u9971\uff0c40\u201450%\u4e3a\u5c0f\u5eb7\uff0c30\u201440%\u4e3a\u5bcc\u88d5\uff0c\u4f4e\u4e8e30%\u4e3a\u6700\u5bcc\u88d5\u30021978\u5e74\uff0c\u6211\u56fd\u57ce\u4e61\u5c45\u6c11\u7684\u6069\u683c\u5c14\u7cfb\u6570\u5206\u522b\u4e3a57.5%\u548c67.7%\uff0c\u4e5f\u5c31\u662f\u8bf4\u57ce\u9547\u5c45\u6c11\u53ea\u5c5e\u4e8e\u52c9\u5f3a\u6e29\u9971\uff0c\u519c\u6751\u5c45\u6c11\u5219\u5904\u4e8e\u7edd\u5bf9\u8d2b\u56f0\u3002\u7136\u800c\u52302001\u5e74\uff0c\u519c\u6751\u5c45\u6c11\u5bb6\u5ead\u7684\u6069\u683c\u5c14\u7cfb\u6570\u964d\u81f347.8%\uff0c\u800c\u57ce\u9547\u5c45\u6c11\u5bb6\u5ead\u7684\u6069\u683c\u5c14\u7cfb\u6570\u5219\u964d\u81f337.9%, \u53ef\u89c1\u519c\u6751\u5c45\u6c11\u76ee\u524d\u7684\u6d88\u8d39\u9700\u6c42\u5927\u4e8e\u57ce\u9547\u5c45\u6c11\u3002
\u3000\u30003\u3001\u4eba\u53e3\u81ea\u7136\u589e\u957f\u7387\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5f71\u54cd
\u3000\u3000\u4eba\u53e3\u7684\u591a\u5c11\u4e0e\u6d88\u8d39\u6c34\u5e73\u7684\u9ad8\u4f4e\u6709\u5bc6\u5207\u7684\u5173\u7cfb\u3002\u7531\u7ecf\u9a8c\u5206\u6790\u53ef\u77e5\uff0c\u5728\u4eba\u53e3\u6570\u91cf\u4e00\u5b9a\u7684\u60c5\u51b5\u4e0b\uff0c\u7ecf\u6d4e\u53d1\u5c55\u6c34\u5e73\u8d8a\u9ad8\uff0c\u6d88\u8d39\u54c1\u6570\u91cf\u8d8a\u591a\uff0c\u90a3\u4e48\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u5c31\u4f1a\u8d8a\u9ad8\uff1b\u53cd\u4e4b\uff0c\u5728\u7ecf\u6d4e\u53d1\u5c55\u6c34\u5e73\u7a33\u5b9a\u7684\u6761\u4ef6\u4e0b\uff0c\u4eba\u53e3\u6570\u91cf\u7684\u591a\u5c11\u5c31\u51b3\u5b9a\u7740\u6d88\u8d39\u6c34\u5e73\u7684\u9ad8\u4f4e\u3002\u56e0\u6b64\uff0c\u4e0b\u9762\u4ee5\u4eba\u53e3\u81ea\u7136\u589e\u957f\u7387\u4e3a\u89e3\u91ca\u53d8\u91cf\uff0c\u8bbe\u4e3aX4\u8fdb\u884c\u56de\u5f52\u5206\u6790\u3002
\u3000\u3000\u8bbe
\u3000\u3000\u56de\u5f52\u4f30\u8ba1\u7ed3\u679c\u5982\u4e0b\uff1a
\u3000\u3000Dependent Variable: Y
\u3000\u3000Method: Least Squares
\u3000\u3000Date: 12/12/05 Time: 14:52
\u3000\u3000Sample: 1978 2002
\u3000\u3000Included observations: 25
\u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.
\u3000\u3000C 6120.843 519.8942 11.77325 0.0000
\u3000\u3000X4 -389.3241 41.89861 -9.292053 0.0000
\u3000\u3000R-squared 0.789651 Mean dependent var 1418.120
\u3000\u3000Adjusted R-squared 0.780506 S.D. dependent var 1269.558
\u3000\u3000S.E. of regression 594.7908 Akaike info criterion 15.69092
\u3000\u3000Sum squared resid 8136851. Schwarz criterion 15.78843
\u3000\u3000Log likelihood -194.1364 F-statistic 86.34224
\u3000\u3000Durbin-Watson stat 0.548669 Prob(F-statistic) 0.000000
\u3000\u3000(11.7733) (-9.2921)
\u3000\u3000\u56de\u5f52\u7ed3\u679c\u8868\u660e\uff0c\u4eba\u53e3\u6bcf\u589e\u957f1%\u3002\uff0c\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u5e73\u5747\u4e0b\u964d389.32\u5143\u3002\u5176\u539f\u56e0\u4e3b\u8981\u662f\u6211\u56fd\u4eba\u53e3\u57fa\u6570\u5927\uff0c\u5373\u4f7f\u589e\u957f\u7387\u5f88\u4f4e\uff0c\u4e5f\u4f7f\u5f97\u4ee5\u4eba\u53e3\u5e73\u5747\u6765\u8ba1\u7b97\u7684\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u6709\u663e\u8457\u6027\u53d8\u52a8\u3002
\u3000\u30004\u3001\u6d88\u8d39\u7269\u4ef7\u6307\u6570\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5f71\u54cd
\u3000\u3000\u6309\u7ecf\u6d4e\u7406\u8bba\u5206\u6790\uff0c\u7269\u4ef7\u8d8a\u9ad8\uff0c\u8d8a\u4f1a\u6291\u5236\u4eba\u4eec\u7684\u6d88\u8d39\uff0c\u6d88\u8d39\u6c34\u5e73\u4f1a\u8d8a\u4f4e\u3002\u6545\u5728\u6b64\u5f15\u5165\u6d88\u8d39\u7269\u4ef7\u6307\u6570\u8fdb\u884c\u56de\u5f52\u5206\u6790\u3002
\u3000\u3000Dependent Variable: Y
\u3000\u3000Method: Least Squares
\u3000\u3000Date: 12/12/05 Time: 14:51
\u3000\u3000Sample: 1978 2002
\u3000\u3000Included observations: 25
\u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.
\u3000\u3000C 5071.259 3968.544 1.277864 0.2140
\u3000\u3000X5 -34.35080 37.23965 -0.922425 0.3659
\u3000\u3000R-squared 0.035675 Mean dependent var 1418.120
\u3000\u3000Adjusted R-squared -0.006253 S.D. dependent var 1269.558
\u3000\u3000S.E. of regression 1273.521 Akaike info criterion 17.21358
\u3000\u3000Sum squared resid 37302690 Schwarz criterion 17.31109
\u3000\u3000Log likelihood -213.1697 F-statistic 0.850869
\u3000\u3000Durbin-Watson stat 0.052147 Prob(F-statistic) 0.365883
\u3000\u3000\u4ece\u7ed3\u679c\u770b\u51fa\uff0c\u53ef\u51b3\u7cfb\u6570\u5f88\u4f4e\uff0ct\u7edf\u8ba1\u68c0\u9a8c\u4e0d\u663e\u8457\uff0c\u5c3d\u7ba1\u4ece\u7ecf\u6d4e\u80cc\u666f\u5206\u6790\u6765\u770b\uff0c\u6d88\u8d39\u7269\u4ef7\u6307\u6570\u53ef\u80fd\u5f71\u54cd\u6d88\u8d39\u6c34\u5e73\uff0c\u4f46\u56de\u5f52\u7ed3\u679c\u663e\u793a\u5e76\u975e\u5982\u6b64\uff0c\u8fd9\u53ef\u80fd\u4e0e\u7edf\u8ba1\u6570\u636e\u8bef\u5dee\u4ee5\u53ca\u4f30\u8ba1\u65b9\u6cd5\u6709\u5173\u7cfb\u3002
\u3000\u3000\u4e09\u3001\u5f71\u54cd\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u591a\u56e0\u7d20\u5206\u6790
\u3000\u3000\u5728\u524d\u9762\u5206\u6790\u7684\u57fa\u7840\u4e0a\uff0c\u5c06\u6240\u6709\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u5f71\u54cd\u663e\u8457\u7684\u89e3\u91ca\u53d8\u91cf\uff08\u6d88\u8d39\u7269\u4ef7\u6307\u6570\u9664\u5916\uff09\u653e\u8fdb\u540c\u4e00\u4e2a\u6a21\u578b\uff0c\u8fdb\u884c\u591a\u5143\u56de\u5f52\u5206\u6790\uff0c\u7ed3\u679c\u5982\u4e0b\uff1a
\u3000\u3000Dependent Variable: Y
\u3000\u3000Method: Least Squares
\u3000\u3000Date: 12/12/05 Time: 15:06
\u3000\u3000Sample: 1978 2002
\u3000\u3000Included observations: 25
\u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.
\u3000\u3000C -34.92876 68.33338 -0.511152 0.6148
\u3000\u3000X2 -0.106034 0.067316 -1.575168 0.1309
\u3000\u3000X3 0.209742 0.092590 2.265268 0.0348
\u3000\u3000X4 7.788147 4.980314 1.563786 0.1336
\u3000\u3000X1 0.039598 0.005350 7.401766 0.0000
\u3000\u3000R-squared 0.999631 Mean dependent var 1418.120
\u3000\u3000Adjusted R-squared 0.999557 S.D. dependent var 1269.558
\u3000\u3000S.E. of regression 26.73036 Akaike info criterion 9.586334
\u3000\u3000Sum squared resid 14290.24 Schwarz criterion 9.830109
\u3000\u3000Log likelihood -114.8292 F-statistic 13529.64
\u3000\u3000Durbin-Watson stat 1.529774 Prob(F-statistic) 0.000000
\u3000\u3000\u4ece\u56de\u5f52\u7ed3\u679c\u770b\uff0c\u5c3d\u7ba1\u53ef\u51b3\u7cfb\u6570\u5f88\u9ad8\uff0cF\u7edf\u8ba1\u503c\u5f88\u5927\uff0c\u8bf4\u660e\u6a21\u578b\u5728\u6574\u4f53\u4e0a\u7ebf\u6027\u56de\u5f52\u62df\u5408\u8f83\u597d\uff0c\u4f46\u5e38\u6570\u9879\u7684\u56de\u5f52\u7cfb\u6570\u4e0d\u663e\u8457\uff0c\u57ce\u9547\u5c45\u6c11\u53ef\u652f\u914d\u6536\u5165\u4e0e\u4eba\u53e3\u81ea\u7136\u589e\u957f\u7387\u7684\u7b26\u53f7\u4e0e\u7ecf\u6d4e\u610f\u4e49\u76f8\u6096\uff0c\u8868\u660e\u6a21\u578b\u4e2d\u89e3\u91ca\u53d8\u91cf\u5b58\u5728\u4e25\u91cd\u7684\u591a\u91cd\u5171\u7ebf\u6027\u3002
\u3000\u3000\u4e0b\u9762\u770b\u5404\u53d8\u91cf\u4e4b\u95f4\u7684\u7b80\u5355\u76f8\u5173\u7cfb\u6570\uff1a
\u3000\u3000X2 X3 X4 X1 Y
\u3000\u3000X2 1.000000 0.996093 -0.893822 0.999424 0.998859
\u3000\u3000X3 0.996093 1.000000 -0.872752 0.996649 0.997578
\u3000\u3000X4 -0.893822 -0.872752 1.000000 -0.895038 -0.888623
\u3000\u3000X1 0.999424 0.996649 -0.895038 1.000000 0.999650
\u3000\u3000Y 0.998859 0.997578 -0.888623 0.999650 1.000000
\u3000\u3000\u7531\u4e0a\u8868\u53ef\u4ee5\u770b\u51fa\uff0c\u89e3\u91ca\u53d8\u91cf\u4e4b\u95f4\u786e\u5b9e\u5b58\u5728\u9ad8\u5ea6\u7ebf\u6027\u76f8\u5173\uff0c\u4e8e\u662f\u8fd0\u7528OLS\u65b9\u6cd5\u9010\u4e00\u6c42Y\u5bf9\u5404\u4e2a\u89e3\u91ca\u53d8\u91cf\u7684\u56de\u5f52\uff0c\u5e76\u7ed3\u5408\u7ecf\u6d4e\u610f\u4e49\u548c\u7edf\u8ba1\u68c0\u9a8c\u9009\u51fa\u62df\u5408\u7ed3\u679c\u6700\u597d\u7684\u4e00\u5143\u7ebf\u6027\u56de\u5f52\u65b9\u7a0b\uff0c\u5728\u6b64\u57fa\u7840\u4e0a\u5c06\u5176\u4f59\u89e3\u91ca\u53d8\u91cf\u9010\u4e00\u4ee3\u5165\u5e76\u62df\u5408\uff0c\u6700\u7ec8\u5f97\u5230\u5982\u4e0b\u6a21\u578b\uff1a
\u3000\u3000Dependent Variable: Y
\u3000\u3000Method: Least Squares
\u3000\u3000Date: 12/12/05 Time: 15:19
\u3000\u3000Sample: 1978 2002
\u3000\u3000Included observations: 25
\u3000\u3000Variable Coefficient Std. Error t-Statistic Prob.
\u3000\u3000C -34.31193 19.08905 -1.797467 0.0860
\u3000\u3000X2 0.352578 0.047965 7.350744 0.0000
\u3000\u3000X3 0.502716 0.135078 3.721667 0.0012
\u3000\u3000R-squared 0.998600 Mean dependent var 1418.120
\u3000\u3000Adjusted R-squared 0.998473 S.D. dependent var 1269.558
\u3000\u3000S.E. of regression 49.61351 Akaike info criterion 10.75857
\u3000\u3000Sum squared resid 54153.00 Schwarz criterion 10.90483
\u3000\u3000Log likelihood -131.4821 F-statistic 7846.542
\u3000\u3000Durbin-Watson stat 1.349085 Prob(F-statistic) 0.000000
\u3000\u3000\uff08-1.7975\uff09 \uff087.3507\uff09 \uff083.77217\uff09
\u3000\u3000\u4ece\u4e0b\u56fe\u4e5f\u53ef\u4ee5\u770b\u51fa\uff0c\u6a21\u578b\u7684\u62df\u5408\u7a0b\u5ea6\u975e\u5e38\u597d\uff0c\u8fd9\u4e5f\u8bf4\u660e\u57ce\u4e61\u5c45\u6c11\u4eba\u5747\u6536\u5165\u5bf9\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u76f4\u63a5\u5f71\u54cd\u6700\u5927\u3002\u519c\u6751\u5c45\u6c11\u4eba\u5747\u7eaf\u6536\u5165\u6bcf\u589e\u52a01\u5143\uff0c\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u5e73\u5747\u589e\u52a00.50\u5143\uff1b\u57ce\u9547\u5c45\u6c11\u4eba\u5747\u53ef\u652f\u914d\u6536\u5165\u6bcf\u589e\u52a01\u5143\uff0c\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u5e73\u5747\u589e\u52a00.35\u5143\u3002
\u3000\u3000\u56db\u3001\u63d0\u9ad8\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u5bf9\u7b56\u5efa\u8bae
\u3000\u3000\u6839\u636e\u4ee5\u4e0a\u5206\u6790\uff0c\u53ef\u4ee5\u770b\u51fa\u63d0\u9ad8\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u7684\u6839\u672c\u9014\u5f84\u662f\u5927\u529b\u53d1\u5c55\u751f\u4ea7\u529b\u3002\u4f46\u5728\u5927\u529b\u53d1\u5c55\u751f\u4ea7\u529b\uff0c\u589e\u52a0\u57ce\u4e61\u5c45\u6c11\u53ef\u652f\u914d\u6536\u5165\u7684\u540c\u65f6\uff0c\u5fc5\u987b\u4e25\u683c\u63a7\u5236\u4eba\u53e3\u589e\u957f\u3002\u4e3a\u6b64\uff0c\u6211\u4eec\u53ef\u4ee5\u91c7\u53d6\u4ee5\u4e0b\u63aa\u65bd\uff1a
\u3000\u3000\uff08\u4e00\uff09\u63d0\u9ad8\u5c45\u6c11\u6574\u4f53\u6536\u5165\u6c34\u5e73\uff0c\u7279\u522b\u662f\u519c\u6751\u5c45\u6c11\u6536\u5165\u6c34\u5e73\u3002
\u3000\u3000\u4e2d\u56fd\u662f\u4e00\u4e2a\u519c\u4e1a\u5927\u56fd\uff0c\u519c\u6751\u5c45\u6c11\u6536\u5165\u6c34\u5e73\u4f4e\u662f\u5c45\u6c11\u6d88\u8d39\u6c34\u5e73\u96be\u4ee5\u63d0\u9ad8\u7684\u91cd\u8981\u539f\u56e0\u3002\u5207\u5b9e\u63d0\u9ad8\u519c\u6c11\u6536\u5165\uff0c\u4e0d\u4ec5\u662f\u519c\u6c11\u7531\u6e29\u9971\u8fdb\u5165\u5c0f\u5eb7\u3001\u6539\u5584\u519c\u6c11\u751f\u6d3b\u8d28\u91cf\u7684\u5173\u952e\uff0c\u4e5f\u662f\u523a\u6fc0\u6d88\u8d39\u3001\u4fc3\u8fdb\u7ecf\u6d4e\u5065\u5eb7\u5feb\u901f\u534f\u8c03\u53d1\u5c55\u7684\u91cd\u8981\u7740\u529b\u70b9\u3002
\u3000\u30001\u3001\u8c03\u6574\u519c\u4e1a\u7ed3\u6784\uff0c\u63d0\u9ad8\u519c\u4ea7\u54c1\u54c1\u8d28\u3002\u8c03\u6574\u548c\u4f18\u5316\u519c\u4e1a\u7ed3\u6784\uff0c\u5927\u529b\u53d1\u5c55\u9ad8\u4ea7\u3001\u4f18\u8d28\u3001\u9ad8\u6821\u519c\u4e1a\uff0c\u8fd9\u662f\u5f53\u524d\u589e\u52a0\u519c\u6751\u5c45\u6c11\u6536\u5165\u7684\u5173\u952e\u63aa\u65bd\u3002\u8c03\u6574\u7ed3\u6784\u7684\u91cd\u70b9\u662f\u6539\u5584\u519c\u4ea7\u54c1\u54c1\u79cd\uff0c\u63d0\u9ad8\u8d28\u91cf\uff0c\u589e\u52a0\u6548\u76ca\u3002\u4e00\u662f\u8981\u6293\u4f4f\u5f53\u524d\u519c\u4ea7\u54c1\u4f9b\u7ed9\u5145\u8db3\u7684\u65f6\u673a\uff0c\u52a0\u5feb\u8c03\u6574\u7cae\u98df\u54c1\u79cd\u7ed3\u6784\uff1b\u4e8c\u662f\u5927\u529b\u53d1\u5c55\u755c\u7267\u4e1a\u3002\u755c\u7267\u4e1a\u5728\u519c\u4e1a\u751f\u4ea7\u4e2d\u5904\u4e8e\u201c\u524d\u62c9\u540e\u5e26\u201d\u7684\u91cd\u8981\u73af\u8282\uff0c\u641e\u597d\u4e86\u53ef\u4ee5\u4fc3\u8fdb\u79cd\u690d\u4e1a\u3001\u5e26\u52a8\u52a0\u5de5\u4e1a\uff0c\u5b9e\u73b0\u519c\u4ea7\u54c1\u8f6c\u5316\u589e\u503c\u3002\u4e09\u662f\u53d1\u6325\u79cd\u690d\u4e1a\u4f20\u7edf\u4f18\u52bf\uff0c\u53d1\u5c55\u519c\u6797\u7267\u6e14\u4e1a\u548c\u540d\u3001\u7279\u3001\u4f18\u3001\u65b0\u4ea7\u54c1\uff0c\u519c\u4ea7\u54c1\u4e5f\u8981\u63d0\u9ad8\u54c1\u724c\u610f\u8bc6\uff0c\u9760\u54c1\u724c\u5f00\u62d3\u5e02\u573a\u3002
\u3000\u30002\u3001\u4f9d\u9760\u79d1\u6280\u8fdb\u6b65\uff0c\u964d\u4f4e\u519c\u4e1a\u751f\u4ea7\u6210\u672c\u3002\u5728\u5f53\u524d\u589e\u6536\u56f0\u96be\u7684\u60c5\u51b5\u4e0b\uff0c\u964d\u4f4e\u751f\u4ea7\u6210\u672c\uff0c\u51cf\u5c11\u519c\u6c11\u7684\u652f\u51fa\u4e5f\u662f\u589e\u52a0\u519c\u6c11\u6536\u5165\u7684\u4e00\u6761\u91cd\u8981\u9014\u5f84\u3002\u76ee\u524d\uff0c\u7531\u4e8e\u6280\u672f\u76f8\u5bf9\u843d\u540e\uff0c\u6211\u56fd\u519c\u4e1a\u8d44\u6e90\u7684\u5229\u7528\u7387\u8fdc\u8fdc\u4f4e\u4e8e\u53d1\u8fbe\u56fd\u5bb6\u6c34\u5e73\uff0c\u7279\u522b\u662f\u519c\u6c11\u5728\u7528\u6c34\u3001\u7528\u7535\u3001\u7528\u5730\u7b49\u5f88\u591a\u65b9\u9762\uff0c\u7f3a\u4e4f\u79d1\u5b66\u6307\u5bfc\uff0c\u6d6a\u8d39\u4e25\u91cd\u3002\u636e\u6d4b\u7b97\uff0c\u4ece1988\u5e74\u52301996\u5e74\uff0c\u7cae\u98df\u589e\u957f\u4e8627.6%,\u6536\u8d2d\u4ef7\u683c\u6307\u6570\u589e\u957f\u4e86172.9%,\u4f46\u540c\u671f\u603b\u6210\u672c\u5374\u589e\u957f\u4e86274.3%\u3002\u8fd9\u4e5f\u8bf4\u660e\uff0c\u964d\u4f4e\u6210\u672c\uff0c\u589e\u52a0\u6548\u76ca\u662f\u63a8\u52a8\u519c\u4e1a\u8282\u80fd\u589e\u6548\uff0c\u589e\u52a0\u519c\u6c11\u6536\u5165\u7684\u91cd\u8981\u63aa\u65bd\u3002
\u3000\u30003\u3001\u63a8\u52a8\u519c\u4e1a\u4ea7\u4e1a\u5316\u7ecf\u8425\uff0c\u5efa\u7acb\u6709\u5229\u4e8e\u519c\u6c11\u589e\u6536\u7684\u4ea7\u4e1a\u4f53\u7cfb\u548c\u5229\u76ca\u673a\u5236\u3002\u63a8\u8fdb\u519c\u4e1a\u4ea7\u4e1a\u5316\uff0c\u5e94\u7a81\u51fa\u6293\u597d\u5efa\u8bbe\u519c\u4ea7\u54c1\u57fa\u5730\uff0c\u57f9\u80b2\u9f99\u5934\u4f01\u4e1a\uff0c\u5efa\u7acb\u5229\u76ca\u673a\u5236\uff0c\u5b8c\u5584\u793e\u4f1a\u5316\u670d\u52a1\u4f53\u7cfb\u7b49\u51e0\u4e2a\u5173\u952e\u73af\u8282\u3002
\u3000\u30004\u3001\u5207\u5b9e\u51cf\u8f7b\u519c\u6c11\u8d1f\u62c5\u3002\u5728\u9010\u6b65\u51cf\u5c11\u519c\u4e1a\u7a0e\u4ee5\u5916\u7684\u519c\u6751\u5404\u9879\u6536\u8d39\u9879\u76ee\u548c\u6570\u989d\u7684\u540c\u65f6\uff0c\u628a\u771f\u6b63\u5e94\u7531\u519c\u6c11\u627f\u62c5\u7684\u5408\u7406\u6027\u6536\u8d39\u8d39\u7528\u7528\u7acb\u6cd5\u7684\u5f62\u5f0f\u786e\u5b9a\u4e0b\u6765\uff0c\u662f\u51cf\u8f7b\u519c\u6c11\u8d1f\u62c5\u7684\u5de5\u4f5c\u8d70\u4e0a\u6cd5\u5236\u5316\u3001\u6b63\u89c4\u5316\u7684\u8f68\u9053\u3002\u540c\u65f6\u8fd8\u8981\u72e0\u6293\u57fa\u5c42\u653f\u5e9c\u53ca\u5e72\u90e8\u7684\u5ec9\u653f\u5efa\u8bbe\uff0c\u6d88\u9664\u5411\u519c\u6c11\u4e71\u644a\u6d3e\u3001\u4e71\u6536\u8d39\u7684\u5404\u79cd\u9690\u60a3\uff0c\u8fdb\u4e00\u6b65\u52a0\u5f3a\u519c\u6751\u7cbe\u795e\u6587\u660e\u5efa\u8bbe\uff0c\u5f15\u5bfc\u519c\u6c11\u5065\u5eb7\u6d88\u8d39\u3002
\u3000\u3000\uff08\u4e8c\uff09\u4e25\u683c\u63a7\u5236\u4eba\u53e3\u589e\u957f
\u3000\u3000\u63a7\u5236\u4eba\u53e3\u589e\u957f\u662f\u4eba\u53e3\u95ee\u9898\u7684\u91cd\u70b9\u548c\u96be\u70b9\u3002\u4eba\u53e3\u81ea\u7136\u589e\u957f\u7387\u8d8a\u9ad8\uff0c\u8d8a\u662f\u963b\u788d\u793e\u4f1a\u7ecf\u6d4e\u7684\u53d1\u5c55\u548c\u4eba\u7c7b\u7684\u8fdb\u6b65\u3002\u6211\u4eec\u8981\u7ee7\u7eed\u5b9e\u884c\u8ba1\u5212\u751f\u80b2\u653f\u7b56\uff0c\u5b9e\u73b0\u63a7\u5236\u4eba\u53e3\u89c4\u6a21\u7684\u65e2\u5b9a\u76ee\u6807\u3002\u6839\u636e\u6211\u56fd\u4eba\u53e3\u73b0\u72b6\u548c\u7ecf\u6d4e\u53d1\u5c55\u6c34\u5e73\uff0c\u8981\u628a\u63a7\u5236\u4eba\u53e3\u51fa\u751f\u7387\u3001\u63d0\u9ad8\u4eba\u53e3\u7d20\u8d28\u548c\u89e3\u51b3\u4eba\u53e3\u8001\u9f84\u5316\u7b49\u95ee\u9898\u901a\u76d8\u8003\u8651\uff0c\u5236\u5b9a\u4e00\u4e2a\u5408\u7406\u589e\u957f\u3001\u63d0\u9ad8\u8d28\u91cf\u3001\u4f18\u5316\u5e74\u9f84\u7ed3\u6784\u7684\u7efc\u5408\u4eba\u53e3\u65b9\u6848\u3002\u540c\u65f6\u52a0\u5f3a\u5bf9\u76ee\u524d\u4eba\u53e3\u72b6\u51b5\u548c\u4eba\u53e3\u52a8\u6001\u7684\u7814\u7a76\u5206\u6790\uff0c\u4e3a\u4eba\u53e3\u63a7\u5236\u3001\u5c31\u4e1a\u3001\u8fc1\u79fb\u4e0e\u57ce\u5e02\u5316\u7b49\u6b63\u786e\u51b3\u7b56\u63d0\u4f9b\u4f9d\u636e\u3002
一、影响居民消费水平的因素
宏观经济模型GDP＝C+I+G+(X-M)，经济发展应该紧紧抓住消费这驾马车，而居民消费水平的高低受制于多种因素。凯恩斯消费理论认为居民消费主要受收入影响，我国居民消费一直很低，消费意愿不强，可通过计量分析找到影响我国居民消费水平的主要因素，从根本上改善消费不足，促进我国经济的持续稳定健康发展。
消费分为居民消费和政府消费，居民消费包括农村居民消费和城镇居民消费。结合居民消费水平的影响因素，列出了国内生产总值、职工平均工资指数、城镇居民消费价格指数、普通中学及高等学校在校生数、卫生机构数和基本设施铁路公路货运量等相关因素，进行计量分析，得到回归
模型。
二、居民消费水平模型的总体分析框架
(1)多元线性回归法OLS概述
回归分析是计量经济分析中使用最多的方法，在现实问题研究中，因变量往往受制于多个经济变量的影响，通过统计资料，根据多个解释变量的最优组合来建立回归方程预测被解释变量的回归分析称为多元线性回归法。其模型基本形式为:
以上供参考。
绛旓細涓銆佸奖鍝灞呮皯娑堣垂姘村钩鐨勫洜绱犲畯瑙傜粡娴庢ā鍨婫DP锛滳+I+G+(X-M)锛岀粡娴庡彂灞曞簲璇ョ揣绱ф姄浣忔秷璐硅繖椹鹃┈杞︼紝鑰屽眳姘戞秷璐规按骞崇殑楂樹綆鍙楀埗浜庡绉嶅洜绱犮傚嚡鎭╂柉娑堣垂鐞嗚璁や负灞呮皯娑堣垂涓昏鍙鏀跺叆褰卞搷锛屾垜鍥藉眳姘戞秷璐逛竴鐩村緢浣庯紝娑堣垂鎰忔効涓嶅己锛屽彲閫氳繃璁￠噺鍒嗘瀽鎵惧埌褰卞搷鎴戝浗灞呮皯娑堣垂姘村钩鐨勪富瑕佸洜绱狅紝浠庢牴鏈笂鏀瑰杽娑堣垂涓嶈冻锛屼績杩涙垜鍥...
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绛旓細1.鎻愰珮灞呮皯鏀跺叆姘村钩锛氭敼鍠勫姵鍔ㄥ姏灏变笟鐘跺喌銆佸姞寮鸿亴涓氬煿璁佹彁楂樻渶浣庡伐璧勬爣鍑嗙瓑锛岄兘鏈夊埄浜庢彁楂樺眳姘戞敹鍏ユ按骞筹紝浠庤屽鍔犱釜浜哄彲鏀厤鏀跺叆锛岃繘鑰屽埡婵娑堣垂闇姹傘2.浼樺寲绋庢敹鏀跨瓥锛氶氳繃鍑忓厤鎴栬皟鏁撮傚害闄嶄綆閮ㄥ垎鍟嗗搧鐨勫緛绋庣巼锛屼互鍙婇噰鍙栬储鏀垮鍔辩瓑鏀跨瓥锛屾帹鍔ㄤ汉浠鍔犺喘涔拌繖浜涘晢鍝佺殑鎰忔効銆3.鎵╁ぇ绀句細淇濋殰瑕嗙洊鑼冨洿锛氬姞寮虹ぞ浼氫繚闅...
绛旓細鎻愰珮灞呮皯娑堣垂姘村钩鐨勫缓璁 涓锛夊姫鍔涘鍔犲眳姘戠壒鍒槸鍐滄皯鏀跺叆鏀跺叆鏄秷璐圭殑鍐冲畾鍥犵礌锛屾墿澶у啘鏉戞秷璐规渶鏍规湰鐨勮繕鍦ㄤ簬鎻愰珮鍐滄皯鏀跺叆銆傜洰鍓嶅煄闀囧寲鍙戝睍宸茶繘鍏ュ姞閫熼樁娈碉紝鏂板啘鏉戝缓璁惧凡寮濮嬪惎鍔紝宸ヤ笟鍖栧彂灞曠┖闂村箍闃旓紝鍐滄皯澧炴敹杩涚▼蹇呭皢涓庢绱у瘑鑱旂郴鍦ㄤ竴璧凤紝鏀跺叆澧為暱浼氳秺鏉ヨ秺渚濋潬闈炲啘浜т笟鐨勫彂灞曞拰鍔冲姩鍔涚殑娴佸姩杞Щ銆傚洜姝わ紝瑕侀傚簲...
绛旓細涓銆佽杩涗竴姝ユ敼鍠勫浗姘鏀跺叆鍒嗛厤缁撴瀯锛岄氳繃瀹屽杽绋庡埗銆佸鍔犺浆绉绘敮浠樸佹彁楂灞呮皯灏ゅ叾鏄腑浣庢敹鍏ョ兢浣撴敹鍏ュ湪鏀跺叆鍒嗛厤涓殑姣旈噸銆傚彲浠ラ噰鍙栧畬鍠勭◣鍒躲佸姞蹇啘鏉戝湡鍦板拰浣忓畢鐨勫競鍦哄寲娴佽浆锛屼互鎻愰珮鍐滄潙灞呮皯鐨勮储浜фф敹鍏ャ備釜绋庢柟闈紝鎸夌収鐗╀环姘村钩鍔ㄦ佽皟鏁村熀鏈墸闄ら锛屼粠褰撳墠鐨勭患鍚堜笌鍒嗙被鐩哥粨鍚堢殑绋庡埗璧板悜缁煎悎寰佹敹锛屾敼鍙樼洰鍓嶄釜绋庡緛鏀...
绛旓細涓轰簡淇冭繘灞呮皯娑堣垂锛岄瑕佷换鍔℃槸鎺ㄥ姩缁忔祹澧為暱妯″紡鐨勮浆鍙橈紝浠庝緷璧栨姇璧勮浆鍚戜緷璧栨秷璐归┍鍔ㄣ傝繖闇瑕佷繚鎸佺粡娴庣ǔ鍋ュ闀匡紝浠ュ鍔犲浗姘戣储瀵屾婚噺锛屽苟璋冩暣鍥芥皯鏀跺叆鍒嗛厤锛岀‘淇濆眳姘戝疄闄呬汉鍧囧彲鏀厤鏀跺叆涓嶨DP澧為暱鍚屾锛屽噺灏戦暱鏈熺殑鐩稿鍔ｅ娍銆傚悓鏃讹紝閫氳繃缂╁皬鏀跺叆宸窛锛岄檷浣庡熀灏肩郴鏁帮紝鏍规嵁杈归檯娑堣垂鐜囬掑噺瑙勫緥锛屾湁鍔╀簬鎻愬崌鏁翠綋娑堣垂姘村钩銆傚叾...
绛旓細褰卞搷娑堣垂鐨勫洜绱狅細1銆佷富瑕佹槸灞呮皯鐨鏀跺叆鍜鐗╀环鎬讳綋姘村钩 2銆佸眳姘戠殑鏀跺叆褰卞搷銆3銆佺墿浠风殑鍙樺姩涔熸槸褰卞搷娑堣垂鐨勬渶涓昏鍥犵礌涔嬩竴銆4銆佹秷璐瑰績鐞嗐鏀跺叆涓庢秷璐鐨勫叧绯伙細鏀跺叆鏄秷璐圭殑鍩虹鍜屽墠鎻愩
绛旓細(1) 灞呮皯娑堣垂姘村钩鍙楀緢澶氬洜绱犲奖鍝嶏紝鍏朵腑涓昏鏄眳姘戠殑鏀跺叆鍜岀墿浠锋绘按骞炽(2) 瑕佹彁楂樺眳姘戠殑娑堣垂姘村钩锛氣憼淇濇寔缁忔祹鐨勭ǔ瀹氬闀匡紝澧炲姞灞呮皯鏀跺叆銆傗憽寤虹珛鍜屽畬鍠勭ぞ浼氫繚闅滃埗搴︺傗憿缂╁皬鏀跺叆宸窛銆傗懀绋冲畾鐗╀环銆備粠瀹忚瑙掑害鏉ヨ锛屾寚涓鍥藉眳姘戝湪涓瀹氭椂鏈熷钩鍧囦韩鐢ㄧ殑鐢熸椿娑堣垂鐨勪骇鍝(涓庡姵鍔)鐨勬暟閲忎笌璐ㄩ噺锛屾垨鍏ㄤ綋娑堣垂鑰呮寜浜哄潎...
