多元線性回歸模型線性與非線性估計(jì)檢驗(yàn)實(shí)驗(yàn)報(bào)告(共7頁(yè))

1、湖南商學(xué)院模擬實(shí)驗(yàn)報(bào)告(bogo)實(shí)驗(yàn)(shyn)地點(diǎn): 實(shí)驗(yàn)樓 時(shí)間(shjin):課程名稱(chēng)計(jì)量經(jīng)濟(jì)學(xué)模擬實(shí)驗(yàn)實(shí)驗(yàn)項(xiàng)目名稱(chēng)多元線性回歸模型線性與非線性估計(jì)檢驗(yàn)班級(jí) 姓名學(xué)號(hào)學(xué)時(shí)小組成員實(shí)驗(yàn)?zāi)康模?掌握生產(chǎn)函數(shù)的估計(jì)、檢驗(yàn)以及多參數(shù)的線性約束檢驗(yàn)等內(nèi)容實(shí)驗(yàn)說(shuō)明：數(shù)據(jù)來(lái)源于教材p65頁(yè)表4.1.1，工作文件夾是sy3.WF1，實(shí)驗(yàn)?zāi)康氖亲寣W(xué)生掌握生產(chǎn)函數(shù)的估計(jì)、檢驗(yàn)以及多參數(shù)的線性約束檢驗(yàn)等內(nèi)容。注意：實(shí)際GDP是以1978年為100計(jì)算、資本存量是以1952年的不變價(jià)格計(jì)算。實(shí)驗(yàn)內(nèi)容：1.估計(jì)雙對(duì)數(shù)模型，以及說(shuō)明各回歸系數(shù)的經(jīng)濟(jì)含義； 由于實(shí)驗(yàn)數(shù)據(jù)中已給出變量的對(duì)數(shù)形式，所以new objec

2、ts“eq01”“gdp1 c k1 l1Dependent Variable: GDP1Method: Least SquaresDate: 02/27/13 Time: 08:41Sample: 1978 2006Included observations: 29CoefficientStd. Errort-StatisticProb.C-9.0474860.787252-11.492500.0000K10.7475960.02199733.986640.0000L10.6788800.0901477.5308220.0000R-squared0.998348Mean dependent

3、var5.887599Adjusted R-squared0.998220S.D. dependent var0.800400S.E. of regression0.033765Akaike info criterion-3.841113Sum squared resid0.029641Schwarz criterion-3.699669Log likelihood58.69614Hannan-Quinn criter.-3.796814F-statistic7854.199Durbin-Watson stat0.799482Prob(F-statistic)0.000000Second-St

4、age SSR0.029641在回歸方程中點(diǎn)viewrepresentations所以該模型函數(shù)形式為L(zhǎng)n GDP= -9.0474855847 + 0.747595717651LnK + 0.678880192961LnL回歸系數(shù)的經(jīng)濟(jì)含義:資本每增加1%，GDP平均增加0.74759571765%,勞動(dòng)每增加1%，GDP平均增加0.67888019296%2.對(duì)模型做t檢驗(yàn)和F檢驗(yàn)；T(0)=-11.49250,T(1)=33.98664,T(2)=7.530822,P值均為0，所以T檢驗(yàn)說(shuō)明回歸模型中系數(shù)不為0，在一定顯著性水平下這個(gè)模型是有意義的，模型中解釋變量對(duì)于被解釋變量有一定解釋

5、力度。F=7854.199,P=0.000000，F(xiàn)檢驗(yàn)說(shuō)明拒絕原假設(shè)，模型總體存在。3.在5%的顯著性水平下對(duì)隨機(jī)干擾項(xiàng)的方差做如下檢驗(yàn)：和輸入scalar deltasqrhat1=0.027/(29-3)4.利用F統(tǒng)計(jì)量來(lái)檢驗(yàn)： 打開(kāi)eq1ViewCoefficient TestsWald CoefficientRestrictions輸入c(2)+c(3)=1okWald Test:Equation: EQ1Test StatisticValuedfProbabilityF-statistic37.38918(1, 26)0.0000Chi-square37.3891810.0000N

6、ull Hypothesis Summary:Normalized Restriction (= 0)ValueStd. Err.-1 + C(2) + C(3)0.4264760.069746Restrictions are linear in coefficients.從輸出結(jié)果來(lái)看P=0.0000，是拒絕原假設(shè)的，所以1+215*.對(duì)模型進(jìn)行非線性O(shè)LS估計(jì)：設(shè)定初始值(雙擊序列C，在c(1)、c(2)和c(3)所對(duì)應(yīng)的單元格中分別輸入0，option中的收斂精度設(shè)為0.001，迭代次數(shù)100次)，保存模型；objecteq2GDP=C(1)*(KC(2)*(LC(3)optionokD

7、ependent Variable: GDPMethod: Least SquaresDate: 03/22/15 Time: 17:28Sample: 1978 2006Included observations: 29Convergence achieved after 1 iterationGDP=C(1)*(KC(2)*(LC(3)VariableCoefficientStd. Errort-StatisticProb.C(1)4.0149733.34E+171.20E-171.0000C(2)1.9420011.44E+151.35E-151.0000C(3)-4.5477188.7

8、1E+15-5.22E-161.0000R-squared-1.858693Mean dependent var481.4144Adjusted R-squared-2.078593S.D. dependent var359.3645S.E. of regression630.5381Akaike info criterion15.82872Sum squared resid10337035Schwarz criterion15.97017Log likelihood-226.5165Hannan-Quinn criter.15.87302Durbin-Watson stat0.008228b

9、. 設(shè)定初始值(雙擊序列C，在c(1)、c(2)和c(3)所對(duì)應(yīng)的單元格中分別輸入0，option中的收斂精度設(shè)為0.00001，迭代次數(shù)1000次)，保存模型；objecteq3GDP=C(1)*(KC(2)*(LC(3)optionokDependent Variable: GDPMethod: Least SquaresDate: 03/22/15 Time: 17:28Sample: 1978 2006Included observations: 29Convergence achieved after 1 iterationGDP=C(1)*(KC(2)*(LC(3)Variable

10、CoefficientStd. Errort-StatisticProb.C(1)4.0149733.34E+171.20E-171.0000C(2)1.9420011.44E+151.35E-151.0000C(3)-4.5477188.71E+15-5.22E-161.0000R-squared-1.858693Mean dependent var481.4144Adjusted R-squared-2.078593S.D. dependent var359.3645S.E. of regression630.5381Akaike info criterion15.82872Sum squared resid10337035Schwarz criterion15.97017Log likelihood-226.5165Hannan-Quinn criter.15.87302Durbin-Watson stat0