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1、精品 2009-2010 學(xué)年度第 2 學(xué)期 計(jì)量經(jīng)濟(jì)學(xué)實(shí)驗(yàn)報(bào)告書 專 業(yè) 金融學(xué) 班 級 三班 學(xué) 號 6 學(xué)生姓名 經(jīng)濟(jì)與貿(mào)易學(xué)院 實(shí)驗(yàn)一 Eviews基本操作實(shí)驗(yàn) 一、實(shí)驗(yàn)?zāi)康模赫莆誆views基本操作。 二、實(shí)驗(yàn)要求： （1）EViews軟件的安裝； （2）數(shù)據(jù)的輸入、編輯與序列生成；

2、 （3）圖形分析與描述統(tǒng)計(jì)分析； （4）數(shù)據(jù)文件的存貯、調(diào)用與轉(zhuǎn)換。 三、實(shí)驗(yàn)結(jié)果報(bào)告： （圍繞實(shí)驗(yàn)要求，結(jié)合實(shí)驗(yàn)的內(nèi)容撰寫報(bào)告） 一、數(shù)據(jù)的輸入、序列生成 二、圖形分析 obs Y X 1985 2041 8964 1986 2091 10202 1987 2140 11963 1988 2391 14928 1989 2727 16909 1990 2822 18548 1991 2990 21618 1992 3297 26638 1993 4255 34634 1994 5127 4

3、6759 1995 6038 58478 1996 6910 67885 1997 8234 74463 1998 9263 79396 obs T X X1 X2 1985 1 8964 80353296 0.000111557340473 1986 2 10202 104080804 9.80199960792e-05 1987 3 11963 143113369 8.35910724735e-05 1988 4 14928 222845184 6.6988210075e-05 1989 5 1

4、6909 285914281 5.91401029038e-05 1990 6 18548 344028304 5.39141686435e-05 1991 7 21618 467337924 4.62577481728e-05 1992 8 26638 709583044 3.75403558826e-05 1993 9 34634 1199513956 2.88733614367e-05 1994 10 46759 2186404081 2.13862571911e-05 1995 11 58478 3419676484 1.

5、71004480317e-05 1996 12 67885 4608373225 1.47307947264e-05 1997 13 74463 5544738369 1.34294884708e-05 1998 14 79396 6303724816 1.25950929518e-05 以上可以看出我國稅收與GDP呈線性遞增關(guān)系 Y X Mean 4309.000 35098.93 Median 3143.500 24128.00 Maximum 9263.000 79396.00 Minimum 2

6、041.000 8964.000 Std. Dev. 2422.631 25378.06 Skewness 0.869889 0.635116 Kurtosis 2.396109 1.847265 Jarque-Bera 1.978382 1.716333 Probability 0.371877 0.423939 Observations 14 14 實(shí)驗(yàn)二 一元線性回歸分析過程實(shí)驗(yàn) 一、實(shí)驗(yàn)?zāi)康模赫莆找辉€性回歸模型的估計(jì)方法、檢驗(yàn)方法和預(yù)測方法。

7、二、實(shí)驗(yàn)要求： （1）會選擇方程進(jìn)行一元線性回歸； （2）掌握一元回歸分析過程； （3）掌握一元回歸模型的基本檢驗(yàn)方法； （4）會對回歸方程進(jìn)行經(jīng)濟(jì)學(xué)解釋 （5）估計(jì)非線性回歸模型，并進(jìn)行模型比較 三、實(shí)驗(yàn)結(jié)果報(bào)告： （圍繞實(shí)驗(yàn)要求，結(jié)合實(shí)驗(yàn)的內(nèi)容撰寫報(bào)告） 一、 圖形分析 兩變量趨勢圖分析結(jié)果顯示，我國稅收收入與GDP二者存在差距逐漸增大的增長趨勢。相關(guān)圖分析顯示，我國稅收收入增長與GDP密切相關(guān)，二者為非線性的曲線相關(guān)關(guān)系。 我國稅收與GDP的相關(guān)圖 二、估計(jì)一元線性回歸模型 Dependent Varia

8、ble: Y Method: Least Squares Date: 06/22/10 Time: 19:29 Sample: 1985 1998 Included observations: 14 Variable Coefficient Std. Error t-Statistic Prob. C 987.5417 155.1430 6.365364 0.0000 GDP 0.094631 0.003627 26.09310 0.0000 R-squared 0.982680 Mean dependent var 4309.00

9、0 Adjusted R-squared 0.981237 S.D. dependent var 2422.631 S.E. of regression 331.8482 Akaike info criterion 14.57880 Sum squared resid 1321479. Schwarz criterion 14.67009 Log likelihood -100.0516 F-statistic 680.8498 Durbin-Watson stat 0.796256 Prob(F-statistic

10、) 0.000000 Y=987.54+0.095GDP R^2=0.983 (6.37) (26.09) 二、 估計(jì)非線性回歸模型 1、 雙對數(shù)模型 Dependent Variable: LOG(Y) Method: Least Squares Date: 06/22/10 Time: 19:45 Sample: 1985 1998 Included observations: 14 Variable Coefficient Std. Error t-Statistic Prob. C 1.270443 0.33

11、1668 3.830470 0.0024 LOG(GDP) 0.682297 0.032415 21.04866 0.0000 R-squared 0.973629 Mean dependent var 8.233505 Adjusted R-squared 0.971431 S.D. dependent var 0.528347 S.E. of regression 0.089302 Akaike info criterion -1.862014 Sum squared resid 0.095699 Schwarz

12、criterion -1.770720 Log likelihood 15.03409 F-statistic 443.0462 Durbin-Watson stat 0.476382 Prob(F-statistic) 0.000000 LOG（Y）=1.27+0.68LOG(GDP) R^2=0.97 (3.83) (21.05) 2、對數(shù)模型 Dependent Variable: Y Method: Least Squares Date: 06/22/10 Time: 19:50 Sample:

13、 1985 1998 Included observations: 14 Variable Coefficient Std. Error t-Statistic Prob. C -26163.32 3149.684 -8.306649 0.0000 LOG(GDP) 2985.923 307.8313 9.699870 0.0000 R-squared 0.886886 Mean dependent var 4309.000 Adjusted R-squared 0.877460 S.D. dependent var 2422

14、.631 S.E. of regression 848.0607 Akaike info criterion 16.45535 Sum squared resid 8630484. Schwarz criterion 16.54664 Log likelihood -113.1874 F-statistic 94.08748 Durbin-Watson stat 0.318941 Prob(F-statistic) 0.000000 Y=-26163.32+2985.92LOG(GDP) R^2=0.887 (-8.31)

15、 (9.7) 3、指數(shù)模型 Dependent Variable: LOG(Y) Method: Least Squares Date: 06/22/10 Time: 19:55 Sample: 1985 1998 Included observations: 14 Variable Coefficient Std. Error t-Statistic Prob. C 7.508605 0.032400 231.7463 0.0000 GDP 2.07E-05 7.57E-07 27.26846 0.0000 R-squared 0

16、.984118 Mean dependent var 8.233505 Adjusted R-squared 0.982794 S.D. dependent var 0.528347 S.E. of regression 0.069303 Akaike info criterion -2.369086 Sum squared resid 0.057635 Schwarz criterion -2.277792 Log likelihood 18.58360 F-statistic 743.5689 Durbin-Wa

17、tson stat 0.600192 Prob(F-statistic) 0.000000 4、二次模型 Dependent Variable: Y Method: Least Squares Date: 06/22/10 Time: 19:59 Sample: 1985 1998 Included observations: 14 Variable Coefficient Std. Error t-Statistic Prob. C 2323.813 114.4226 20.30904 0.0000 GDP^2

18、1.08E-06 4.07E-08 26.65249 0.0000 R-squared 0.983388 Mean dependent var 4309.000 Adjusted R-squared 0.982003 S.D. dependent var 2422.631 S.E. of regression 325.0002 Akaike info criterion 14.53709 Sum squared resid 1267502. Schwarz criterion 14.62839 Log likelihood

19、 -99.75965 F-statistic 710.3550 Durbin-Watson stat 0.645855 Prob(F-statistic) 0.000000 四、模型比較 （以二次模型、指數(shù)模型為例） 二次函數(shù)回歸模型殘差分別表 指數(shù)函數(shù)模型殘差分布表 實(shí)驗(yàn)三 多元線性回歸模型 一、實(shí)驗(yàn)?zāi)康模赫莆斩嘣€性回歸模型的估計(jì)和檢驗(yàn)方法。 二、實(shí)驗(yàn)要求： （1）會選擇方程進(jìn)行多元線性回歸； （2）掌握多元回歸分析過程； （3）掌握多元回歸模型的基本檢驗(yàn)方法； （4）會對回

20、歸方程進(jìn)行經(jīng)濟(jì)學(xué)解釋。 （5）比較選擇最佳模型 三、實(shí)驗(yàn)結(jié)果報(bào)告： （圍繞實(shí)驗(yàn)要求，結(jié)合實(shí)驗(yàn)的內(nèi)容撰寫報(bào)告） 一、 多元線性回歸模型的建立 Dependent Variable: Y Method: Least Squares Date: 06/22/10 Time: 20:30 Sample: 1978 1994 Included observations: 17 Variable Coefficient Std. Error t-Statistic Prob. C -675.3208 2682.060 -0.251792 0.8

21、051 T 77.67893 115.6731 0.671538 0.5136 L 0.666665 0.853626 0.780980 0.4488 K 0.776417 0.104459 7.432745 0.0000 R-squared 0.995764 Mean dependent var 6407.249 Adjusted R-squared 0.994786 S.D. dependent var 2486.742 S.E. of regression 179.5630 Akaike info criteri

22、on 13.42125 Sum squared resid 419157.5 Schwarz criterion 13.61730 Log likelihood -110.0807 F-statistic 1018.551 Durbin-Watson stat 1.510903 Prob(F-statistic) 0.000000 因此，我國國有獨(dú)立工業(yè)企業(yè)的生產(chǎn)函數(shù)為： （模型1） ＝(-0.252) (0.672) (0.781) (7.433) ，說明模型有很高的擬合優(yōu)度

23、，F(xiàn)檢驗(yàn)也是高度顯著的，說明職工人數(shù)L、資金K和時(shí)間變量對工業(yè)總產(chǎn)值的總影響是顯著的。但是，模型中其他變量（包括常數(shù)項(xiàng)）的統(tǒng)計(jì)量值都較小，未通過檢驗(yàn)。因此需要做適當(dāng)?shù)恼{(diào)整。 二、建立剔除時(shí)間變量的二元線性回歸模型 Dependent Variable: Y Method: Least Squares Date: 06/22/10 Time: 20:36 Sample: 1978 1994 Included observations: 17 Variable Coefficient Std. Error t-Statistic Prob. C -2387.26

24、9 816.8895 -2.922390 0.0111 L 1.208532 0.273020 4.426528 0.0006 K 0.834496 0.057421 14.53287 0.0000 R-squared 0.995617 Mean dependent var 6407.249 Adjusted R-squared 0.994990 S.D. dependent var 2486.742 S.E. of regression 176.0069 Akaike info criterion 13.33771

25、Sum squared resid 433697.8 Schwarz criterion 13.48475 Log likelihood -110.3705 F-statistic 1589.953 Durbin-Watson stat 1.481994 Prob(F-statistic) 0.000000 此時(shí)我國國有獨(dú)立工業(yè)企業(yè)的生產(chǎn)函數(shù)為： （模型2） ＝(-2.922) (4.427) (14.533) 模型2的擬合優(yōu)度較模型1并無多大變化，F(xiàn)檢驗(yàn)也是高度顯著的。

26、但這里，解釋變量、常數(shù)項(xiàng)的檢驗(yàn)值都比較大，顯著性概率都小于0.05，因此模型2較模型1更為合理。 三、建立非線性回歸模型——C-D生產(chǎn)函數(shù) Dependent Variable: LNY Method: Least Squares Date: 06/22/10 Time: 20:42 Sample: 1978 1994 Included observations: 17 Variable Coefficient Std. Error t-Statistic Prob. C -1.951253 1.665320 -1.171698 0.2609 LNL

27、 0.604467 0.272697 2.216625 0.0437 LNK 0.673658 0.072357 9.310131 0.0000 R-squared 0.995753 Mean dependent var 8.692837 Adjusted R-squared 0.995147 S.D. dependent var 0.394921 S.E. of regression 0.027512 Akaike info criterion -4.189602 Sum squared resid 0.010597

28、 Schwarz criterion -4.042564 Log likelihood 38.61162 F-statistic 1641.407 Durbin-Watson stat 1.338201 Prob(F-statistic) 0.000000 C-D生產(chǎn)函數(shù)的估計(jì)式為： （模型3） ＝ (-1.172) (2.217) (9.310) 從模型3中看出，資本與勞動(dòng)的產(chǎn)出彈性都是在0到1之間，模型的經(jīng)濟(jì)意義合理，而且擬合優(yōu)度較模型2還略有提高，解釋變量都通過了顯著性檢驗(yàn)。

29、 實(shí)驗(yàn)四 異方差模擬實(shí)驗(yàn) 一、實(shí)驗(yàn)?zāi)康模毫私猱惙讲钅Ｐ偷臋z驗(yàn)方法和異方差模型的處理方法。 二、實(shí)驗(yàn)要求： （1）模擬線性回歸模型中隨機(jī)擾動(dòng)項(xiàng)為異方差的樣本數(shù)據(jù) （2）進(jìn)行Goldfeld-Quandt檢驗(yàn) （3）利用WLS方法進(jìn)行參數(shù)估計(jì)，建立模型。 三、實(shí)驗(yàn)結(jié)果報(bào)告： （圍繞實(shí)驗(yàn)要求，結(jié)合實(shí)驗(yàn)的內(nèi)容撰寫報(bào)告） 一、人均消費(fèi)與人均收入 Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 19:15 Sample: 1 27 Included observations

30、: 27 Variable Coefficient Std. Error t-Statistic Prob. C 15.83853 9.416160 1.682058 0.1050 X 0.103854 0.011149 9.314931 0.0000 R-squared 0.776322 Mean dependent var 94.44444 Adjusted R-squared 0.767375 S.D. dependent var 45.00712 S.E. of regression 21.70747 Ak

31、aike info criterion 9.064377 Sum squared resid 11780.36 Schwarz criterion 9.160365 Log likelihood -120.3691 F-statistic 86.76793 Durbin-Watson stat 2.614427 Prob(F-statistic) 0.000000 Y=15.84+0.104X R^2=0.78 T統(tǒng)計(jì) 1.68 9.31 F=86.77 戈德菲爾德—匡特法（雙變量模型）檢驗(yàn) 前1-10個(gè)數(shù)據(jù)的回歸 De

32、pendent Variable: Y Method: Least Squares Date: 06/23/10 Time: 20:18 Sample: 1 10 Included observations: 10 Variable Coefficient Std. Error t-Statistic Prob. C -3.121210 10.53931 -0.296149 0.7747 X 0.144960 0.026196 5.533703 0.0006 R-squared 0.792863 Mean dependent var

33、 52.50000 Adjusted R-squared 0.766971 S.D. dependent var 20.76455 S.E. of regression 10.02368 Akaike info criterion 7.624634 Sum squared resid 803.7933 Schwarz criterion 7.685151 Log likelihood -36.12317 F-statistic 30.62187 Durbin-Watson stat 2.703606 Prob(F-s

34、tatistic) 0.000551 RSS1=803.79 后10個(gè)數(shù)據(jù)的回歸 Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 20:20 Sample: 18 27 Included observations: 10 Variable Coefficient Std. Error t-Statistic Prob. C 48.41870 56.70995 0.853795 0.4180 X 0.075211 0.047631 1.579027 0.1530

35、 R-squared 0.237611 Mean dependent var 136.4000 Adjusted R-squared 0.142312 S.D. dependent var 36.04688 S.E. of regression 33.38354 Akaike info criterion 10.03086 Sum squared resid 8915.686 Schwarz criterion 10.09138 Log likelihood -48.15430 F-statistic 2.4933

36、26 Durbin-Watson stat 2.988119 Prob(F-statistic) 0.152983 RSS2=8915.69 RSS2/RSS1= 11.09>F(8,8)=3.44 所以存在異方差 利用WLS進(jìn)行異方差的消除(W=1/RESID) Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 19:59 Sample: 1 27 Included observations:

37、 27 Weighting series: RESID Variable Coefficient Std. Error t-Statistic Prob. C 58.98937 22.78914 2.588486 0.0158 X 0.067308 0.018290 3.680133 0.0011 Weighted Statistics R-squared 0.941484 Mean dependent var -1.18E+17 Adjusted R-squared 0.939144 S.D. depende

38、nt var 8.31E+17 S.E. of regression 2.05E+17 Akaike info criterion 82.63371 Sum squared resid 1.05E+36 Schwarz criterion 82.72969 Log likelihood -1113.555 F-statistic 13.54338 Durbin-Watson stat 0.338876 Prob(F-statistic) 0.001121 Unweighted Statistics R-squ

39、ared 0.557188 Mean dependent var 94.44444 Adjusted R-squared 0.539475 S.D. dependent var 45.00712 S.E. of regression 30.54273 Sum squared resid 23321.45 Durbin-Watson stat 1.287687 二、 對某地區(qū)31年來居民的收入與儲蓄建立的線性回歸模型進(jìn)行異方差檢驗(yàn)及校正方法。 Dependent Variable: Y Method: Least Squar

40、es Date: 06/23/10 Time: 20:08 Sample: 1 31 Included observations: 31 Variable Coefficient Std. Error t-Statistic Prob. C -665.6043 113.4187 -5.868556 0.0000 X 0.084550 0.004687 18.04056 0.0000 R-squared 0.918186 Mean dependent var 1230.000 Adjusted R-squared 0.915365

41、 S.D. dependent var 817.1759 S.E. of regression 237.7341 Akaike info criterion 13.84252 Sum squared resid 1639007. Schwarz criterion 13.93504 Log likelihood -212.5591 F-statistic 325.4618 Durbin-Watson stat 1.036781 Prob(F-statistic) 0.000000 Y=-665.6+0.08X R^2

42、=0.918 (-5.87) (18.04) Goldfeld-Quandt檢驗(yàn)前10個(gè)數(shù)據(jù)的回歸 Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 21:19 Sample: 1 11 Included observations: 11 Variable Coefficient Std. Error t-Statistic Prob. C -744.6351 195.4108 -3.810614 0.0041 X 0.088258 0.015705 5.61

43、9619 0.0003 R-squared 0.778216 Mean dependent var 331.3636 Adjusted R-squared 0.753574 S.D. dependent var 260.8157 S.E. of regression 129.4724 Akaike info criterion 12.72778 Sum squared resid 150867.9 Schwarz criterion 12.80012 Log likelihood -68.00278 F-stati

44、stic 31.58011 Durbin-Watson stat 1.142088 Prob(F-statistic) 0.000326 RSS1=150867.9 后10個(gè)數(shù)據(jù)的回歸 Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 21:21 Sample: 20 31 Included observations: 12 Variable Coefficient Std. Error t-Statistic Prob. C 1141.066 709.842

45、8 1.607491 0.1390 X 0.029409 0.021992 1.337264 0.2108 R-squared 0.151699 Mean dependent var 2084.250 Adjusted R-squared 0.066869 S.D. dependent var 287.2405 S.E. of regression 277.4706 Akaike info criterion 14.24032 Sum squared resid 769899.2 Schwarz criterion

46、14.32114 Log likelihood -83.44191 F-statistic 1.788274 Durbin-Watson stat 2.864726 Prob(F-statistic) 0.210758 RSS2=769899.2 F=FRSS2/RSS1=5.103>F(8,8)=3.44所以存在異方差 利用WLS進(jìn)行消除(W=1/RESID) Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 20:41 Sample: 1 31 Inclu

47、ded observations: 31 Weighting series: 1/RESID Variable Coefficient Std. Error t-Statistic Prob. C -686.0761 23.55233 -29.12986 0.0000 X 0.085747 0.001967 43.58293 0.0000 Weighted Statistics R-squared 0.995497 Mean dependent var 126.3255 Adjusted R-squared 0.9953

48、42 S.D. dependent var 1586.032 S.E. of regression 108.2469 Akaike info criterion 12.26905 Sum squared resid 339804.5 Schwarz criterion 12.36156 Log likelihood -188.1702 F-statistic 1899.471 Durbin-Watson stat 0.156397 Prob(F-statistic) 0.000000 Unweighted Stati

49、stics R-squared 0.917939 Mean dependent var 1230.000 Adjusted R-squared 0.915110 S.D. dependent var 817.1759 S.E. of regression 238.0918 Sum squared resid 1643943. Durbin-Watson stat 1.923620 三、全國各地區(qū)年人均通訊費(fèi)用支出與家庭可支配收入建立的線性回歸模型進(jìn)行異方差檢驗(yàn)及校正方法。 Goldfeld-Quandt檢驗(yàn)前1

50、0個(gè)數(shù)據(jù)的回歸 Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 21:09 Sample: 1 30 Included observations: 30 Variable Coefficient Std. Error t-Statistic Prob. C -56.91798 36.20624 -1.572049 0.1272 X 0.058075 0.006480 8.962009 0.0000 R-squared 0.741501 Mean dep

51、endent var 256.8727 Adjusted R-squared 0.732269 S.D. dependent var 97.56583 S.E. of regression 50.48324 Akaike info criterion 10.74550 Sum squared resid 71359.62 Schwarz criterion 10.83891 Log likelihood -159.1825 F-statistic 80.31760 Durbin-Watson stat 2.008179

52、 Prob(F-statistic) 0.000000 Goldfeld-Quandt檢驗(yàn)前10個(gè)數(shù)據(jù)的回歸 Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 21:12 Sample: 1 10 Included observations: 10 Variable Coefficient Std. Error t-Statistic Prob. C -261.1499 358.2945 -0.728869 0.4869 X 0.106334 0.085327

53、1.246183 0.2480 R-squared 0.162564 Mean dependent var 185.2400 Adjusted R-squared 0.057885 S.D. dependent var 25.97864 S.E. of regression 25.21555 Akaike info criterion 9.469655 Sum squared resid 5086.592 Schwarz criterion 9.530172 Log likelihood -45.34828 F-s

54、tatistic 1.552972 Durbin-Watson stat 3.044685 Prob(F-statistic) 0.247952 RSS1=5086.592 后10個(gè)數(shù)據(jù)的回歸 Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 21:13 Sample: 21 30 Included observations: 10 Variable Coefficient Std. Error t-Statistic Prob. C -75.48340

55、 154.9201 -0.487241 0.6392 X 0.060433 0.021628 2.794170 0.0234 R-squared 0.493907 Mean dependent var 350.4440 Adjusted R-squared 0.430646 S.D. dependent var 115.8410 S.E. of regression 87.40844 Akaike info criterion 11.95592 Sum squared resid 61121.88 Schwarz c

56、riterion 12.01643 Log likelihood -57.77959 F-statistic 7.807387 Durbin-Watson stat 1.846850 Prob(F-statistic) 0.023407 Rss2=61121.88 F=Rss2/Rss1=12.02>F(8,8)=3.44所以存在異方差 利用WLS進(jìn)行消除(W=1/RESID) Dependent Variable: Y Method: Least Squares Date: 06/23/10 Time: 21:16 Sample: 1 30

57、 Included observations: 30 Weighting series: 1/RESID Variable Coefficient Std. Error t-Statistic Prob. C -46.99125 9.238453 -5.086485 0.0000 X 0.056230 0.001717 32.74588 0.0000 Weighted Statistics R-squared 1.000000 Mean dependent var 255.5239 Adjusted R-squared

58、 1.000000 S.D. dependent var 1400.279 S.E. of regression 0.025604 Akaike info criterion -4.427763 Sum squared resid 0.018356 Schwarz criterion -4.334350 Log likelihood 68.41644 F-statistic 1072.292 Durbin-Watson stat 0.130304 Prob(F-statistic) 0.000000 Unweight

59、ed Statistics R-squared 0.740752 Mean dependent var 256.8727 Adjusted R-squared 0.731494 S.D. dependent var 97.56583 S.E. of regression 50.55628 Sum squared resid 71566.25 Durbin-Watson stat 1.998810 實(shí)驗(yàn)五 序列自相關(guān)模擬實(shí)驗(yàn) 一、實(shí)驗(yàn)?zāi)康模毫私庑蛄邢嚓P(guān)模型的檢驗(yàn)方法以及序列相關(guān)模型的處理方法。

60、 二、實(shí)驗(yàn)要求： （1）模擬線性回歸模型中隨機(jī)擾動(dòng)項(xiàng)為序列自相關(guān)的樣本數(shù)據(jù)， （2）進(jìn)行D-W檢驗(yàn)； （3）利用Durbin兩步法進(jìn)行參數(shù)估計(jì)，建立模型 三、實(shí)驗(yàn)結(jié)果報(bào)告： （圍繞實(shí)驗(yàn)要求，結(jié)合實(shí)驗(yàn)的內(nèi)容撰寫報(bào)告） 實(shí)驗(yàn)六 計(jì)量經(jīng)濟(jì)分析的創(chuàng)新性實(shí)驗(yàn) 一、實(shí)驗(yàn)?zāi)康模禾岣哂?jì)量分析的創(chuàng)新能力。 二、實(shí)驗(yàn)要求： （1）提出一個(gè)經(jīng)濟(jì)問題； （2）提出經(jīng)濟(jì)模型； （3）收集相關(guān)數(shù)據(jù)并進(jìn)行檢驗(yàn)； （4）建立計(jì)量經(jīng)濟(jì)模型，并提出對策建議。 三、實(shí)驗(yàn)結(jié)果報(bào)告： （圍繞實(shí)驗(yàn)要求，結(jié)合實(shí)驗(yàn)的內(nèi)容撰寫報(bào)告） .