Bài tập ktl exercise

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

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Bài 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

-SRF:

wage = 1.41 + 0.569educ + 0.055exper+0.072nonwhite 2.092female+0.715married-0.641south +u^

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Wage^ = 1.41 + 0.569educ + 0.055exper+0.072nonwhite 2.092female+0.715married-0.641south

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Bài 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

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Bài 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 =

-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

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2 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

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

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bwght int %8.0g birth weight, ounces

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faminc 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 =

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

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2 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)

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- Having colonial style or not does not have statistically significant effect on the

Campus = 1 if living in campus

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Clubs = 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

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

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Reject 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.

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Bài 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 =

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

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Một 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.