5.14
（1） Y对X，即
Dependent Variable: Y Method: Least Squares Date: 11/13/17 Time: 20:58 Sample: 1971 1987 Included observations: 17
Variable X C
R-squared
Coefficient
0.260878 38.96907
Std. Error
0.016664 3.856351
t-Statistic
15.65490 10.10517
Prob. 0.0000 0.0000
96.41176 19.72216 6.123109 6.221134 6.132853 0.629301
?=b+bX Yi12i 0.942325 Mean dependent var 0.938480 S.D. dependent var 4.891751 Akaike info criterion 358.9385 Schwarz criterion -50.04642 Hannan-Quinn criter. 245.0760 Durbin-Watson stat 0.000000
Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
?
y=38.9690+0.2609xtt=(10.105)(15.655)2r=0.9423
1
（2）
?=b+bInX InY对InX，即 InYi12iDependent Variable: LNY Method: Least Squares Date: 11/13/17 Time: 21:40 Sample: 1971 1987 Included observations: 17
Variable C LNX
R-squared
Coefficient 1.404051 0.588965
Std. Error 0.156813 0.029317
t-Statistic 8.953649 20.08981
Prob. 0.0000 0.0000 4.547848 0.213165 -3.407698 -3.309673 -3.397954 0.734161
0.964166 Mean dependent var 0.961777 S.D. dependent var 0.041675 Akaike info criterion 0.026052 Schwarz criterion 30.96543 Hannan-Quinn criter. 403.6007 Durbin-Watson stat 0.000000
Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
?lny=1.4041+0.5890lnxt t=(8.954)(20.090)2r=0.9642
2
（3）
?=b+bX InY对X，即 InYi12iDependent Variable: LNY Method: Least Squares Date: 11/13/17 Time: 21:42 Sample: 1971 1987 Included observations: 17
Variable C X
R-squared
Coefficient 3.931578 0.002799
Std. Error 0.046430 0.000201
t-Statistic 84.67764 13.94972
Prob. 0.0000 0.0000 4.547848 0.213165 -2.715956 -2.617930 -2.706212 0.529132
0.928433 Mean dependent var 0.923662 S.D. dependent var 0.058896 Akaike info criterion 0.052031 Schwarz criterion 25.08562 Hannan-Quinn criter. 194.5946 Durbin-Watson stat 0.000000
Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
lny=3.9316+0.0028Xtt=(84.678)(13.950)2r=0.9284?
3
（4）
?=b+bInX Y对InX，即 Yi12i
Coefficient -192.9661 54.21257
Std. Error 16.38000 3.062278
t-Statistic -11.78059 17.70335
Prob. 0.0000 0.0000 96.41176 19.72216 5.889824 5.987849 5.899568 0.610822
Dependent Variable: Y Method: Least Squares Date: 11/13/17 Time: 21:43 Sample: 1971 1987 Included observations: 17
Variable C LNX
R-squared
0.954325 Mean dependent var 0.951280 S.D. dependent var 4.353186 Akaike info criterion 284.2535 Schwarz criterion -48.06350 Hannan-Quinn criter. 313.4086 Durbin-Watson stat 0.000000
Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
?
Y=?192.9661+54.2126lnXtt=(?11.781)(17.703)2r=0.9542
1 2 3 4 B解释个回归结果 ?=Ytt= 38.9690(10.105) 0.2609Xt(15.655) R2=0.9424 ?=lnYtt=?=lnYtt=1.4041(8.954)3.9316 0.5890lnXt(20.090)0.0028Xt(13.950) R2=0.9642 (84.678) R2=0.9284 ?=Ytt= ?192.9661(?11.781)54.2126Xt(17.703) R2=0.9543 ?=解：1.?1?Y斜率说明X每变动一个单位，Y的绝对变动量； ?X4
?=?Y/Y=E斜率便是弹性系数； 2. ?1?X/X?=?Y/Y斜率表示X每变动一个单位，Y的均值的瞬时增长率； 3. ?1?X?=4,. ?1?Y斜率表示X的相对变化对Y的绝对量的影响。
?X/XC对每一个模型求Y对X的变化率
?=?Y=0.2609； 2. ?Y=???Y=0.5890Y； 解：1. ?11?X?XXX 3.
?Y?/X=54.2126/X. ??Y=0.0028Y; 4. ?Y=?=?11?X?XD对每一个模型求Y对X的弹性，对其中的一些模型，求Y对X的均值弹性。 解：1. E=?Y/Y??X=0.2609X; =?1?X/XYYX220.19=0.2609?=0.5959 Y96.41176均值弹性=0.2609?2. E=?Y/Y?=0.5890; =?1?X/X?Y/Y??X=0.0028X; =?1?X/X3. E=均值弹性=0.0028?X=0.0028?220.19=0.6165 4. E=?Y/Y?/Y=54.2126/Y. =?1?X/X11=54.2126?=0.5623. Y96.41176均值弹性=0.2609?
E根据这些回归结果，你将选择那个模型？为什么？
解：无法判断，因为只有当模型的解释变量的类型相同时，才可比较拟合优度检验数R，对模型的选择还取决于模型的用途。
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计量经济学精要第四版课后习题答案(2020年10月整理).pdf
