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Part1. 繪製地圖 安裝繪製地圖所需程式. ssc install spmap ssc install shp2dta ssc install mif2dta. 讀取 CHN_adm1 地圖檔。創出 chinaprovince 以及 coord 兩個檔案. shp2dta using CHN_adm1, database (chinaprovince) coordinates(coord) genid(id) gencentroids(c). *打開 chinaprovince 檔案,刪除 Paracel Islands. - PowerPoint PPT Presentation
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Part1.繪製地圖安裝繪製地圖所需程式
ssc install spmap ssc install shp2dtassc install mif2dta
讀取 CHN_adm1地圖檔。創出chinaprovince以及 coord兩個
檔案shp2dta using CHN_adm1, database (chinaprovince) coordinates(coord) genid(id) gencentroids(c)
*打開 chinaprovince檔案,刪除 Paracel Islands
把想顯示於地圖的 data放進 chinaprovince中
繪出 2007年中國 FDI分布圖spmap FDI_2007 using coord, id(id) clnumber(5)
可點選 start graph editor編輯
Part2.建立空間計量矩陣findit spmat
點選第一個超連結
點選 (click here to install)進行下載
讀取 chinaprovince,並將 x_c 跟y_c改名
改為 longitude 與 latitude
生成距離矩陣spmat idistance idistance_province longitude latitude, id(id) dfunction(euclidean) normalize(row)
生成 stata可讀之檔案idistance_province.spmat
spmat save idistance_province using idistance_province.spmat
(此時 stata資料夾出現idistance_province.spmat)
將剛剛的idistance_province.spmat檔案轉為我們可閱讀的 txt檔spmat export idistance_province using idistance_province.txt
此時 stata資料夾出現idistance_province.txt
生成相鄰矩陣spmat contiguity contiguity_province using coord, id(id) normalize(row)
生成 stata可讀之檔案spmat save contiguity_province using contiguity_province.spmat
將剛剛的contiguity_province.spmat檔案轉為我們可閱讀的 txt檔spmat export contiguity_province using contiguity_province.txt
此時 stata資料夾出現contiguity_province.txt
Part.3 跑空間計量findit xsmle
點選第一個超連結
往下拉,點選 (click here to install)進行下載
spmat use W using "idistance_province.spmat "(使用距離矩陣跑空間計量 )spmat use W using "contiguity_province.spmat"(使用相鄰矩陣跑空間計量 )
告知 stata要跑 panel data,才可以跑空間計量
xtset id year
開啟資料 data,公布年度 _上課用
xsmle FDI FT GDP WAGE OPEN CFDI , wmat(W) mode(sdm) re(其中第一個變數 stata會自動設定為 Y ,之後都是X , re為隨機效果模型, fe為固定效果模型。mode可選 sar 與 sdm)
CFDI .0927104 .0070784 13.10 0.000 .078837 .1065837
OPEN 52617.83 15357.92 3.43 0.001 22516.87 82718.8
WAGE -.8204718 .4860468 -1.69 0.091 -1.773106 .1321623
GDP .6832036 .5361676 1.27 0.203 -.3676656 1.734073
FT 94.43746 84.33592 1.12 0.263 -70.85791 259.7328
Direct
sigma_e 1.43e+07 2820431 5.08 0.000 8799255 1.99e+07
lgt_theta -2.983231 .2074382 -14.38 0.000 -3.389802 -2.57666
Variance
rho .1448937 .3745166 0.39 0.699 -.5891454 .8789328
Spatial
CFDI -.0250511 .059445 -0.42 0.673 -.1415612 .091459
OPEN 7278.992 46083.16 0.16 0.874 -83042.34 97600.32
WAGE -1.156691 1.337933 -0.86 0.387 -3.778992 1.465609
GDP 2.992128 3.799625 0.79 0.431 -4.455 10.43926
FT 166.7804 605.3808 0.28 0.783 -1019.744 1353.305
Wx
_cons 34602.23 25657.5 1.35 0.177 -15685.55 84890.02
CFDI .0925311 .0064227 14.41 0.000 .0799428 .1051194
OPEN 52942.06 14912.52 3.55 0.000 23714.05 82170.06
WAGE -.8209484 .457982 -1.79 0.073 -1.718577 .0766798
GDP .5898734 .4588287 1.29 0.199 -.3094144 1.489161
FT 95.36754 91.50272 1.04 0.297 -83.9745 274.7096
Main
FDI Coef. Std. Err. z P>|z| [95% Conf. Interval]
Log-likelihood = -992.2360
CFDI .0884814 .0062748 14.10 0.000 .076183 .1007798
OPEN 31394.1 12006.58 2.61 0.009 7861.647 54926.56
WAGE -.6336338 .4152306 -1.53 0.127 -1.447471 .1802032
GDP .4246668 .5147828 0.82 0.409 -.5842889 1.433623
FT 69.75591 71.38884 0.98 0.329 -70.16364 209.6755
Direct
sigma2_e 8724978 1279760 6.82 0.000 6216695 1.12e+07
Variance
rho -.0728405 .4367999 -0.17 0.868 -.9289525 .7832716
Spatial
CFDI -.0074108 .0547492 -0.14 0.892 -.1147173 .0998957
OPEN 11053.53 40357.06 0.27 0.784 -68044.86 90151.92
WAGE -1.446171 1.164901 -1.24 0.214 -3.729335 .8369927
GDP 3.910964 3.290304 1.19 0.235 -2.537914 10.35984
FT -37.54984 494.9329 -0.08 0.940 -1007.6 932.5008
Wx
CFDI .0881605 .0050674 17.40 0.000 .0782286 .0980925
OPEN 31436 12066.89 2.61 0.009 7785.338 55086.66
WAGE -.6528307 .388018 -1.68 0.092 -1.413332 .1076707
GDP .3990044 .3815596 1.05 0.296 -.3488387 1.146847
FT 68.49903 73.67781 0.93 0.353 -75.90682 212.9049
Main
FDI Coef. Std. Err. z P>|z| [95% Conf. Interval]
Log-likelihood = -875.1221
Sem模型xsmle FDI FT GDP WAGE OPEN CFDI , ematrix(W) mode(sem) re
將 W 改為相鄰矩陣spmat use W using "contiguity_province.spmat" ,replace
重複剛剛的步驟xsmle FDI FT GDP WAGE OPEN CFDI , wmat(W) mode(sdm) re
WAGE -.8280938 .2907724 -2.85 0.004 -1.397997 -.2581904
GDP .4329611 .4719747 0.92 0.359 -.4920923 1.358015
FT 115.4091 73.59384 1.57 0.117 -28.83218 259.6504
Direct
sigma_e 1.19e+07 2355401 5.05 0.000 7280572 1.65e+07
lgt_theta -2.959432 .2054211 -14.41 0.000 -3.36205 -2.556814
Variance
rho .3649768 .1315159 2.78 0.006 .1072104 .6227432
Spatial
CFDI -.0460164 .0178403 -2.58 0.010 -.0809827 -.0110501
OPEN 1975.193 24510.05 0.08 0.936 -46063.61 50014
WAGE -.2951333 .4505019 -0.66 0.512 -1.178101 .5878342
GDP 1.385675 .9683436 1.43 0.152 -.512244 3.283593
FT 315.4662 163.4135 1.93 0.054 -4.81836 635.7507
Wx
_cons 20322.98 9976.056 2.04 0.042 770.2701 39875.69
CFDI .0953237 .0064428 14.80 0.000 .082696 .1079513
OPEN 51826.03 13089.78 3.96 0.000 26170.53 77481.54
WAGE -.7870454 .2761907 -2.85 0.004 -1.328369 -.2457217
GDP .2640631 .4217304 0.63 0.531 -.5625133 1.09064
FT 83.52678 79.32219 1.05 0.292 -71.94186 238.9954
Main
FDI Coef. Std. Err. z P>|z| [95% Conf. Interval]
Log-likelihood = -984.3404
CFDI .0892304 .0050846 17.55 0.000 .0792647 .0991961
OPEN 33359.6 11465.38 2.91 0.004 10887.86 55831.34
WAGE -.5767623 .2488954 -2.32 0.020 -1.064588 -.0889363
GDP .3193532 .4195972 0.76 0.447 -.5030423 1.141749
FT 93.11735 58.38873 1.59 0.111 -21.32246 207.5572
Direct
sigma2_e 7293312 1084031 6.73 0.000 5168651 9417974
Variance
rho .3369109 .1406159 2.40 0.017 .0613088 .612513
Spatial
CFDI -.0385547 .0164329 -2.35 0.019 -.0707626 -.0063468
OPEN -12976.88 21164.02 -0.61 0.540 -54457.6 28503.85
WAGE -.333219 .3885812 -0.86 0.391 -1.094824 .4283861
GDP 1.470184 .8387266 1.75 0.080 -.1736897 3.114058
FT 239.7663 130.445 1.84 0.066 -15.90124 495.4339
Wx
CFDI .0895429 .0050829 17.62 0.000 .0795806 .0995051
OPEN 33836.74 10791.88 3.14 0.002 12685.05 54988.42
WAGE -.5378699 .2190107 -2.46 0.014 -.967123 -.1086169
GDP .1562309 .3554739 0.44 0.660 -.5404851 .8529469
FT 66.30069 63.39159 1.05 0.296 -57.94453 190.5459
Main
FDI Coef. Std. Err. z P>|z| [95% Conf. Interval]
Log-likelihood = -868.0404
檢定使用 sdm模型或是 sar模型
如果拒絕 H0,則使用 sdm
test [Wx]FT=[Wx]GDP=[Wx]WAGE=[Wx]OPEN=[Wx]CFDI=0
拒絕 H0,使用 sdm
Prob > chi2 = 0.0186
chi2( 5) = 13.57
( 5) [Wx]FT = 0
( 4) [Wx]FT - [Wx]CFDI = 0
( 3) [Wx]FT - [Wx]OPEN = 0
( 2) [Wx]FT - [Wx]WAGE = 0
( 1) [Wx]FT - [Wx]GDP = 0
. test [Wx]FT=[Wx]GDP=[Wx]WAGE=[Wx]OPEN=[Wx]CFDI=0
檢定使用 sdm模型或是 sem模型
如果拒絕 H0,則使用 sdmtestnl ([Wx]FT = -[Spatial]rho*[Main]FT) ([Wx]GDP = -[Spatial]rho*[Main]GDP) ([Wx]WAGE = -[Spatial]rho*[Main]WAGE) ([Wx]OPEN = -[Spatial]rho*[Main]OPEN) ([Wx]CFDI = -[Spatial]rho*[Main]CFDI)
拒絕 H0,使用 sdm
Prob > chi2 = 0.0431
chi2(5) = 11.45
(5) [Wx]CFDI = -[Spatial]rho*[Main]CFDI
(4) [Wx]OPEN = -[Spatial]rho*[Main]OPEN
(3) [Wx]WAGE = -[Spatial]rho*[Main]WAGE
(2) [Wx]GDP = -[Spatial]rho*[Main]GDP
(1) [Wx]FT = -[Spatial]rho*[Main]FT
> OPEN = -[Spatial]rho*[Main]OPEN) ([Wx]CFDI = -[Spatial]rho*[Main]CFDI)
. testnl ([Wx]FT = -[Spatial]rho*[Main]FT) ([Wx]GDP = -[Spatial]rho*[Main]GDP) ([Wx]WAGE = -[Spatial]rho*[Main]WAGE) ([Wx]
使用 hausmam test檢定固定效果模型或隨機效果模型
先跑隨機效果模型,並儲存結果
xsmle FDI FT GDP WAGE OPEN CFDI , wmat(W) mode(sdm) re
estimates store re
再先跑固定效果模型,並且儲存結果
xsmle FDI FT GDP WAGE OPEN CFDI , wmat(W) mode(sdm) fe
estimates store fe
檢定使用固定效果模型或隨機效果模型
hausman re fe
拒絕 H0,使用固定效果模型
.
Prob>chi2 = 0.0523
= 5.90
chi2(2) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xsmle
b = consistent under Ho and Ha; obtained from xsmle
CFDI .0953237 .0895429 .0057808 .003959
OPEN 51826.03 33836.74 17989.3 7407.956
WAGE -.7870454 -.5378699 -.2491755 .1682724
GDP .2640631 .1562309 .1078323 .2269248
FT 83.52678 66.30069 17.22609 47.68141
re fe Difference S.E.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
Part4. Moran’s I
findit spatwmat
點選第二個連結
點選 (click here to install)進行下載
讀取 chinaprovince
spatwmat, name(Chinaweights) xcoord(longitude) ycoord(latitude) band(0 8)
You are advised to extend the distance band
Beware! 2 locations have no neighbors
Smallest maximum distance: 24.06
Largest minimum distance: 9.86
Maximum distance: 43.1
3rd quartile distance: 19.4
Median distance: 13.2
1st quartile distance: 8.6
Minimum distance: 0.7
Friction parameter: 1
Distance band: 0 < d <= 8
Dimension: 31x31
1. Inverse distance weights matrix Chinaweights
The following matrix has been created:
. spatwmat, name(Chinaweights) xcoord(longitude) ycoord(latitude) band(0 8)
下載繪製Moran’s I工具findit splagvar
點選第一個連結
點選 (click here to install)進行下載
讀取檔案moran_FDI
splagvar FDI_2007, wname(Chinaweights) wfrom(Stata) moran(FDI_2007) plot(FDI_2007)