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Bài tập kinh tế lượng Cuộc CMCN 4.0 sẽ có tác động sâu sắc đến TMDVQT: thúc đẩy TMDV mở rộng về quy mô, thay đổi về cơ cấu, đồng thời làm thay đổi cơ bản phương thức cung ứng và tiêu dùng DV
Trang 1Bài 1
reg wage educ exper nonwhite female married south 519=n-k-1
SST=SSE+SSR bậc tự do giá trị tb của tổng bp: SS/df
Source | SS df MS Number of obs = 526 (n) -+ - F( 6, 519) = 41.18 SSE-Model | 2309.32588 6(k) 384.887647 Prob > F = 0.0000SSR-Residual | 4851.08841 519 9.34699115 R-squared = 0.3225 -+ - Adj R-squared = 0.3147 SST-Total | 7160.41429 525 13.6388844 Root MSE = 3.0573 căn bậc 2 MS dùng để ss ………giữa các mẫu, chọn mẫu có ……… gt nhỏ hơn
-hs hồi quy wage | Coef Std Err t P>|t| [95% Conf Interval] -+ - educ | .5694223 .0519968 10.95 0.000 4672723 .6715723 exper | .0552699 .011024 5.01 0.000 0336127 076927 nonwhite | .0729731 .4437979 0.16 0.869 -.7988879 .9448342 female | -2.092037 .2717096 -7.70 0.000 -2.625823 -1.558251 married | .7150305 .2975101 2.40 0.017 1305585 1.299503 south | -.6416218 .2830282 -2.27 0.024 -1.197644 -.0856001 _cons | -1.410051 .7743316 -1.82 0.069 -2.93126 .1111587 hs chặn -
-SRF:
wage = 1.41 + 0.569educ + 0.055exper+0.072nonwhite 2.092female+0.715married-0.641south +u^
Trang 2Wage^ = 1.41 + 0.569educ + 0.055exper+0.072nonwhite 2.092female+0.715married-0.641south
Trang 3Bài 2
reg wage educ exper nonwhite female married south, robust
Linear regression Number of obs = 526 F( 6, 519) = 26.91 Prob > F = 0.0000 R-squared = 0.3225 Root MSE = 3.0573 - | Robust
wage | Coef Std Err t P>|t| [95% Conf Interval] -+ - educ | .5694223 .0657564 8.66 0.000 4402409 .6986037 exper | .0552699 .0103954 5.32 0.000 0348477 075692 nonwhite | .0729731 .3861896 0.19 0.850 -.6857137 83166 female | -2.092037 .2538627 -8.24 0.000 -2.590762 -1.593312 married | .7150305 .2672913 2.68 0.008 1899247 1.240136 south | -.6416218 .2715594 -2.36 0.019 -1.175113 -.108131 _cons | -1.410051 .8691299 -1.62 0.105 -3.117496 .2973942 -
Trang 4Bài 3
Source | SS df MS Number of obs = 1387 -+ - F( 5, 1381) =
Model | 28988.0176 5 5797.60351 Prob > F = 0.0000 Residual | 545486.724 1381 394.994007 R-squared =
-+ - Adj R-squared = 0.0470 Total | 574474.741 1386 414.48394 Root MSE = 19.874 - bwght | Coef Std Err t P>|t| [95% Conf Interval] -+ - cigs | -.5014951 .0915656 -5.48 0.000 -.6811179 -.3218723
-cigs byte %8.0g -cigs smked per day while pregparity byte %8.0g birth order of child
male byte %8.0g =1 if male childwhite byte %8.0g =1 if white
motheduc byte %8.0g mother's yrs of educ
1 Viết hàm SRF
Trang 52 Tính và giải thích ý nghĩa của hệ số xác định R2
3 Kiểm định sự phù hợp của mô hình
4 Kiểm định và giải thích ý nghĩa của biến cigs; male; motheduc với 3 phương pháp: kiểm định t, p-value, khoảng tin cậy
Bài 4
Source | SS df MS Number of obs = 1260 -+ - F( 8, 1251) = 29.51
Model | 4341.73975 8 542.717468 Prob > F = 0.0000 Residual | 23005.6994 1251 18.3898477 R-squared = 0.1588
-+ - Adj R-squared = 0.1534
Total | 27347.4392 1259 21.7215561 Root MSE = 4.2883 - wage | Coef Std Err t P>|t| [95% Conf Interval] -+ - looks | .6252181 .1790031 3.49 0.000 2740387 .9763975
exper byte %8.0g years of workforce experiencelooks byte %8.0g from 1 to 5
union byte %8.0g =1 if union membergoodhlth byte %8.0g =1 if good healthblack byte %8.0g =1 if black
female byte %8.0g =1 if femalemarried byte %8.0g =1 if married
Trang 6-+ - Adj R-squared = 0.0485 Total | 574474.741 1386 414.48394 Root MSE = 19.859 - bwght | Coef Std Err t P>|t| [95% Conf Interval] -+ - faminc | .0591903 .0335575 1.76 0.078 -.006639 .1250196
bwght int %8.0g birth weight, ounces
Trang 7faminc family income, $
cigs byte %8.0g cigs smked per day while pregparity byte %8.0g birth order of child
male byte %8.0g =1 if male childwhite byte %8.0g =1 if white
motheduc byte %8.0g mother's yrs of educBài 6
Source | SS df MS Number of obs = 88 -+ - F( 3, 84) =
Model | 309231.224 3 103077.075 Prob > F = 0.0000 Residual | 608623.281 84 7245.51526 R-squared =
-+ - Adj R-squared = 0.3132 Total | 917854.506 87 10550.0518 Root MSE = 85.121 - price | Coef Std Err t P>|t| [95% Conf Interval] -+ - bdrms | 57.68736 11.49826
lotsize | .0028554 .0009058 colonial | -2.202985 20.66589 _cons | 63.47836 39.90354 storage display value
variable name type format label variable label
price float %9.0g house price, $1000sbdrms byte %9.0g number of bdrms
-lotsize float %9.0g size of lot in square feet colonial byte %9.0g =1 if home is colonial style
1 Write SRF
Trang 82 Calculate R-square and explain the meaning of R-squared3.Test for the overall significance of the model
4 Test for the significance of each independent variable with 3 methods: critical value, p-value and confidence interval; and explain the meaning of variables.
Intepreting the meaning of independent variablesBdrms (reject Ho)
- Number of bedrooms have statistically significant effect on the house price And the
effect is positive (because ^βbdrms>0¿.
- Given the sample we have, when number of bedrooms increase by 1 room, the average price of house increases by 57.68736 thousand USD, holding other factors fixed.
Lotsize (reject Ho)
- size of lot in square feet has statistically significant effect on the house price And
the effect is positive (because ^βlotsize>0).
- Given the sample we have, when size of lot increases by 1 square feet, the average price of house increases by 0028554 thousand USD, holding other factors fixed.Colonial (Not reject Ho)
Trang 9- Having colonial style or not does not have statistically significant effect on the
-+ - Adj R-squared = 0.0782 Total | 19.4060994 140 138614996 Root MSE = 35745 - colGPA | Coef Std Err t P>|t| [95% Conf Interval] -+ - soph | .3056211 .2102452 1.45 0.148 -.1101791 .7214214
Campus = 1 if living in campus
Trang 10Clubs = 1 if joining a club
Skipped: numbers of classes skipped
Bài 8
Source | SS df MS Number of obs = 173 -+ - F( 2, 170) = 25.47
Model | 11170.8751 2 5585.43753 Prob > F = 0.0000 Residual | 37286.3735 170 219.331609 R-squared = 0.2305
-+ - Adj R-squared = 0.2215 Total | 48457.2486 172 281.728189 Root MSE = 14.81 - voteA | Coef Std Err t P>|t| [95% Conf Interval] -+ - democA | 9.192198 2.267415 4.05 0.000 4.716283 13.66811
expendA | .0242475 .004022 6.03 0.000 016308 .0321869
_cons | 37.87049 2.129706 17.78 0.000 33.66642 42.07457democA byte %3.2f =1 if A is democrat
voteA byte %5.2f percent vote for A
expendA float %8.2f camp expends by A, $1000s
Trang 11Reject Ho
- Being democrat has statistically significant effect on voteA
- The estimated results show that being democrate will increase voteA by 9.19%
compared with not being democrat, holding other factors fixed.
Reject Ho
- Expenditure on compaign of A has statistically significant effect on voteA
- The estimated results show that when expenditure on compaign of A increase 1 unit,
voteA will increase by 0.024 unit.
Trang 12Bài 9
Source | SS df MS Number of obs = 88 -+ - F( 3, 84) = (SSE)Model | 309231.224 3 103077.075 Prob > F = 0.0000 (SSR)Residual | 608623.281 84 7245.51526 R-squared =
-+ - Adj R-squared = 0.3132 (SST)Total | 917854.506 87 10550.0518 Root MSE = 85.121 - price | Coef Std Err t P>|t| [95% Conf Interval] -+ - bdrms | 57.68736 11.49826
lotsize | .0028554 .0009058 colonial | -2.202985 20.66589 _cons | 63.47836 39.90354 storage display value
variable name type format label variable label -price float %9.0g house price, $1000sbdrms byte %9.0g number of bdrmslotsize float %9.0g size of lot in square feetcolonial byte %9.0g =1 if home is
Trang 13Một số ví dụ câu hỏi trắc nghiệm:
them by 0.5, the new intercept and slope estimates will be:a 4 and 2.5
b 8 and 2.5c 8 and 10d 16 and 10
2 Consider the following estimated model:^
Yi = -24.5 + 12X2 i−¿ 8X3 i We observe that Yi = 125, X2 i = 13 and X3 i = 5 What is the value of the residual of this observation?
a 33.5b -15.5c -46.5d 69.5
3 Studying inflation in the United States from 1970 to 2006 is an example of usinga randomized controlled experiments.
b time series data.c panel data.