econ, keep the project (HW5)
The statistic from STATA
name: <unnamed>
log: /Users/NONAME/Desktop/Eco 490/DATA_2.txt
log type: text
opened on: 21 Mar 2017, 16:34:24
. ********************************************;
. /*bring in the data*/
> ********************************************;
. use DATA_2.dta, clear;
. /*bring in thr stata data set*/
>
> ********************************************;
. gen age1539= Age1519+Age2024+Age2529+Age3034+Age3539 ;
. gen age4069= Age4044+Age4549+Age5054+Age5559+Age6064+Age6569;
. **********************************************;
. centile(age1539),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
age1539 | 350 25 .328 .326 .33
| 75 .345 .342 .346
. centile(age4069),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
age4069 | 350 25 .363 .3603467 .366
| 75 .38425 .382 .386
. centile(RealGDP),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
RealGDP | 350 25 40137.25 38540.73 41777.26
| 75 51910.50 50614.74 52907.49
. centile(MedianIncomedollars),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
MedianInco~s | 350 25 46655 45329.2 47336.65
| 75 58297.25 56940.2 60035.44
. centile(UnemploymentRate),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
Unemployme~e | 350 25 0.07 0.06 0.07
| 75 0.09 0.09 0.10
. centile(Black),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
Black | 350 25 .035 .026 .0433306
| 75 .161 .148 .1706533
. centile(Asian),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
Asian | 350 25 .017 .015 .018
| 75 .047 .04 .052
. centile(White),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
White | 350 25 .719 .712 .7393306
| 75 .88825 .8818898 .9006533
. centile(ViolentCrimes),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
ViolentCri~s | 350 25 255.55 245.8387 266.6314
| 75 450.55 427.2118 475.0533
. centile(PropertyCrimes),centile(25 75);
-- Binom. Interp. --
Variable | Obs Percentile Centile [95% Conf. Interval]
-------------+-------------------------------------------------------------
PropertyCr~s | 350 25 2226.43 2168.92 2330.52
| 75 3255.22 3137.07 3352.22
. **********************************************;
. summarize;
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
State | 0
Year | 350 2012 2.002863 2009 2015
ViolentCri~s | 350 360.6469 135.5629 99.3 730.2
PropertyCr~s | 350 2746.115 599.2977 1406.6 4015.3
RealGDP | 350 46925.79 8955.848 31169 73464
-------------+---------------------------------------------------------
MedianInco~s | 350 52847.16 8646.483 36796 74551
socialwelf~e | 0
Unemployme~e | 350 .1034743 .4531606 .029 8.55
White | 350 .8020629 .1157772 .269 .972
Black | 350 .1111743 .0954121 .006 .38
-------------+---------------------------------------------------------
Asian | 350 .04538 .0765542 .006 .573
Age59 | 350 .0654114 .0058252 .053 .089
Age1014 | 350 .0662429 .0045623 .057 .086
Age1519 | 350 .0701257 .0038026 .057 .082
Age2024 | 350 .0712 .0054683 .06 .101
-------------+---------------------------------------------------------
Age2529 | 350 .0675114 .0053805 .054 .085
Age3034 | 350 .0641286 .0046957 .054 .079
Age3539 | 350 .0635543 .0043941 .053 .076
Age4044 | 350 .0671514 .0049435 .054 .081
Age4549 | 350 .0715771 .0056201 .052 .088
-------------+---------------------------------------------------------
Age5054 | 350 .07226 .0048753 .053 .086
Age5559 | 350 .0655543 .0054815 .046 .081
Age6064 | 350 .0556514 .0060113 .035 .073
Age6569 | 350 .0417543 .0055688 .025 .058
age1539 | 350 .33652 .0158256 .291 .396
-------------+---------------------------------------------------------
age4069 | 350 .3739486 .0218118 .279 .43
. sort State;
. encode State, gen(state_id);
. ********************************************;
. reg PropertyCrimes MedianIncomedollars RealGDP UnemploymentRate Black Asian White age153
> 9 age4069 i.state_id i.Year, cluster(state_id);
Linear regression Number of obs = 350
F(13, 49) = .
Prob > F = .
R-squared = 0.9570
Root MSE = 137.27
(Std. Err. adjusted for 50 clusters in state_id)
-------------------------------------------------------------------------------------
| Robust
PropertyCrimes | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
MedianIncomedollars | -.0060071 .022509 -0.27 0.791 -.0512405 .0392264
RealGDP | .0046066 .0104227 0.44 0.660 -.0163385 .0255518
UnemploymentRate | -28.04805 5.816488 -4.82 0.000 -39.73672 -16.35938
Black | -6874.629 6067.316 -1.13 0.263 -19067.36 5318.099
Asian | 1276.381 2519.728 0.51 0.615 -3787.202 6339.965
White | -1982.737 3056.564 -0.65 0.520 -8125.133 4159.658
age1539 | 2707.282 7910.592 0.34 0.734 -13189.65 18604.21
age4069 | -20617.38 9312.451 -2.21 0.032 -39331.45 -1903.313
|
state_id |
AL | 2400.724 1425.055 1.68 0.098 -463.0324 5264.48
AR | 1780.449 1081.396 1.65 0.106 -392.6973 3953.595
AZ | 522.973 666.0025 0.79 0.436 -815.4091 1861.355
CA | -576.2339 510.1494 -1.13 0.264 -1601.417 448.9497
CO | 209.0887 542.9357 0.39 0.702 -881.9815 1300.159
CT | -47.95686 566.3596 -0.08 0.933 -1186.099 1090.185
DE | 1989.338 1146.658 1.73 0.089 -314.958 4293.635
FL | 1991.71 984.3913 2.02 0.049 13.50154 3969.918
GA | 2175.293 1541.849 1.41 0.165 -923.1675 5273.754
HI | -586.6794 2312.251 -0.25 0.801 -5233.322 4059.964
IA | -132.501 823.1698 -0.16 0.873 -1786.723 1521.721
ID | -943.2848 951.0444 -0.99 0.326 -2854.48 967.9105
IL | 389.0734 737.1482 0.53 0.600 -1092.281 1870.428
IN | 887.485 844.7876 1.05 0.299 -810.1792 2585.149
KS | 507.8039 795.723 0.64 0.526 -1091.261 2106.869
KY | 647.1312 907.2298 0.71 0.479 -1176.015 2470.278
LA | 2504.743 1675.806 1.49 0.141 -862.9155 5872.401
MA | 207.6908 428.9405 0.48 0.630 -654.2974 1069.679
MD | 2010.871 1540.232 1.31 0.198 -1084.34 5106.082
ME | 1137.849 823.6854 1.38 0.173 -517.4085 2793.107
MI | 1014.103 901.6894 1.12 0.266 -797.9097 2826.116
MN | 293.2229 579.518 0.51 0.615 -871.3621 1457.808
MO | 1375.827 904.7836 1.52 0.135 -442.4037 3194.058
MS | 2078.452 1969.53 1.06 0.296 -1879.467 6036.372
MT | 542.5687 775.4562 0.70 0.487 -1015.769 2100.906
NC | 1847.489 1150.785 1.61 0.115 -465.1006 4160.079
ND | -884.3143 801.0474 -1.10 0.275 -2494.079 725.4507
NE | 24.504 815.4887 0.03 0.976 -1614.282 1663.29
NH | 920.2534 690.7954 1.33 0.189 -467.9519 2308.459
NJ | 313.6128 707.0874 0.44 0.659 -1107.333 1734.558
NM | 740.9333 637.5558 1.16 0.251 -540.2831 2022.15
NV | 344.1416 453.9228 0.76 0.452 -568.0505 1256.334
NY | -106.6446 705.5636 -0.15 0.880 -1524.528 1311.239
OH | 1505.867 917.5678 1.64 0.107 -338.0547 3349.788
OK | 760.22 763.3107 1.00 0.324 -773.7104 2294.15
OR | 775.9838 602.4031 1.29 0.204 -434.5905 1986.558
PA | 553.6233 774.6468 0.71 0.478 -1003.088 2110.334
RI | 511.0151 523.9758 0.98 0.334 -541.9537 1563.984
SC | 2841.442 1481.629 1.92 0.061 -136.0034 5818.888
SD | -858.1967 770.2424 -1.11 0.271 -2406.057 689.6634
TN | 1920.702 1034.76 1.86 0.069 -158.7253 4000.129
TX | 567.3101 756.9549 0.75 0.457 -953.8478 2088.468
UT | -1335.236 1045.8 -1.28 0.208 -3436.85 766.3777
VA | 748.8239 963.5247 0.78 0.441 -1187.451 2685.099
VT | 820.8961 791.0668 1.04 0.305 -768.8123 2410.604
WA | 1252.844 312.7454 4.01 0.000 624.3588 1881.33
WI | 418.9148 710.8242 0.59 0.558 -1009.54 1847.37
WV | 736.5258 935.3412 0.79 0.435 -1143.113 2616.164
WY | -116.2851 726.0749 -0.16 0.873 -1575.387 1342.817
|
Year |
2010 | 127.9972 85.76137 1.49 0.142 -44.34672 300.3411
2011 | 172.3629 107.8418 1.60 0.116 -44.35328 389.0791
2012 | 195.0527 124.0573 1.57 0.122 -54.24988 444.3552
2013 | 108.517 138.1386 0.79 0.436 -169.0828 386.1169
2014 | -36.39789 155.1562 -0.23 0.816 -348.1959 275.4001
2015 | -117.2035 168.9351 -0.69 0.491 -456.6913 222.2843
|
_cons | 11147.71 6994.879 1.59 0.117 -2909.021 25204.45
-------------------------------------------------------------------------------------
. outreg2 using replication2.xls, alpha(0.01,0.05,0.10) excel bd(3) bf(f) stats(coef se)
> rd(3) bra ctitle(clusterSE) replace;
replication2.xls
dir : seeout
. *******************************************************;
. /* Fixed Effects*/
> xtset state_id;
panel variable: state_id (balanced)
. xtreg PropertyCrimes MedianIncomedollars RealGDP UnemploymentRate Black Asian White age1
> 539 age4069,fe;
Fixed-effects (within) regression Number of obs = 350
Group variable: state_id Number of groups = 50
R-sq: Obs per group:
within = 0.4908 min = 7
between = 0.0008 avg = 7.0
overall = 0.0001 max = 7
F(8,292) = 35.18
corr(u_i, Xb) = -0.8694 Prob > F = 0.0000
-------------------------------------------------------------------------------------
PropertyCrimes | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
MedianIncomedollars | -.0365099 .0096487 -3.78 0.000 -.0554997 -.01752
RealGDP | -.0003389 .0064981 -0.05 0.958 -.013128 .0124502
UnemploymentRate | -52.20825 21.56138 -2.42 0.016 -94.64367 -9.772833
Black | 109.9738 3169.399 0.03 0.972 -6127.788 6347.735
Asian | -4053.025 1425.993 -2.84 0.005 -6859.552 -1246.498
White | 5176.204 1267.506 4.08 0.000 2681.598 7670.809
age1539 | 12986.3 6407.358 2.03 0.044 375.8391 25596.76
age4069 | -17154.13 5612.795 -3.06 0.002 -28200.79 -6107.463
_cons | 2761.533 4204.283 0.66 0.512 -5513.007 11036.07
--------------------+----------------------------------------------------------------
sigma_u | 1183.0421
sigma_e | 166.50878
rho | .98057528 (fraction of variance due to u_i)
-------------------------------------------------------------------------------------
F test that all u_i=0: F(49, 292) = 46.80 Prob > F = 0.0000
. estimate store fe;
. xtreg PropertyCrimes MedianIncomedollars RealGDP UnemploymentRate Black Asian White age1
> 539 age4069,re;
Random-effects GLS regression Number of obs = 350
Group variable: state_id Number of groups = 50
R-sq: Obs per group:
within = 0.4454 min = 7
between = 0.3021 avg = 7.0
overall = 0.3093 max = 7
Wald chi2(8) = 237.26
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
-------------------------------------------------------------------------------------
PropertyCrimes | Coef. Std. Err. z P>|z| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
MedianIncomedollars | -.0346684 .0074118 -4.68 0.000 -.0491953 -.0201415
RealGDP | -.0063172 .0060524 -1.04 0.297 -.0181797 .0055454
UnemploymentRate | -62.66521 22.60474 -2.77 0.006 -106.9697 -18.36074
Black | 596.9885 815.3799 0.73 0.464 -1001.127 2195.104
Asian | 614.028 728.4658 0.84 0.399 -813.7388 2041.795
White | -482.719 613.9305 -0.79 0.432 -1686.001 720.5626
age1539 | 17552.88 5245.17 3.35 0.001 7272.538 27833.23
age4069 | -7609.792 4120.071 -1.85 0.065 -15684.98 465.3991
_cons | 2112.874 3227.023 0.65 0.513 -4211.974 8437.723
--------------------+----------------------------------------------------------------
sigma_u | 455.04708
sigma_e | 166.50878
rho | .88191654 (fraction of variance due to u_i)
-------------------------------------------------------------------------------------
. estimate store re;
. hausman fe re;
Note: the rank of the differenced variance matrix (6) does not equal the number of
coefficients being tested (8); be sure this is what you expect, or there may be
problems computing the test. Examine the output of your estimators for anything
unexpected and possibly consider scaling your variables so that the coefficients
are on a similar scale.
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fe re Difference S.E.
-------------+----------------------------------------------------------------
MedianInco~s | -.0365099 -.0346684 -.0018415 .0061776
RealGDP | -.0003389 -.0063172 .0059783 .0023651
Unemployme~e | -52.20825 -62.66521 10.45696 .
Black | 109.9738 596.9885 -487.0147 3062.718
Asian | -4053.025 614.028 -4667.053 1225.884
White | 5176.204 -482.719 5658.923 1108.901
age1539 | 12986.3 17552.88 -4566.584 3680.005
age4069 | -17154.13 -7609.792 -9544.333 3811.625
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 45.38
Prob>chi2 = 0.0000
(V_b-V_B is not positive definite)
. tsset state_id Year;
panel variable: state_id (strongly balanced)
time variable: Year, 2009 to 2015
delta: 1 unit
. xtserial PropertyCrimes MedianIncomedollars RealGDP UnemploymentRate Black Asian White a
> ge1539 age4069, output ;
Linear regression Number of obs = 300
F(8, 49) = 263.36
Prob > F = 0.0000
R-squared = 0.1921
Root MSE = 147.52
(Std. Err. adjusted for 50 clusters in state_id)
-------------------------------------------------------------------------------------
| Robust
D.PropertyCrimes | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
MedianIncomedollars |
D1. | -.0242108 .0133243 -1.82 0.075 -.0509869 .0025653
|
RealGDP |
D1. | -.006721 .0104194 -0.65 0.522 -.0276596 .0142176
|
UnemploymentRate |
D1. | -29.31866 1.870971 -15.67 0.000 -33.07852 -25.55881
|
Black |
D1. | -3886.062 2042.317 -1.90 0.063 -7990.252 218.1284
|
Asian |
D1. | -4052.66 1038.201 -3.90 0.000 -6139.004 -1966.317
|
White |
D1. | 4613.612 1449.886 3.18 0.003 1699.957 7527.267
|
age1539 |
D1. | 8967.636 5293.369 1.69 0.097 -1669.787 19605.06
|
age4069 |
D1. | -9073.082 4880.889 -1.86 0.069 -18881.59 735.4306
-------------------------------------------------------------------------------------
Wooldridge test for autocorrelation in panel data
H0: no first order autocorrelation
F( 1, 49) = 57.853
Prob > F = 0.0000
. /*ending*/
> log close;
name: <unnamed>
log: /Users/NONAME/Desktop/Eco 490/DATA_2.txt
log type: text
closed on: 21 Mar 2017, 16:34:24
------------------------------------------------------------------------------------------
. # delimit cr
delimiter now cr
.
end of do-file
. su age4069
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
age4069 | 350 .3739486 .0218118 .279 .43
.
. hausman fe re;
Note: the rank of the differenced variance matrix (6) does not equal the number of
coefficients being tested (8); be sure this is what you expect, or there may be
problems computing the test. Examine the output of your estimators for anything
unexpected and possibly consider scaling your variables so that the coefficients are
on a similar scale.
---- Coefficients ----
| (b) (B) (b-B) sqrt(diag(V_b-V_B))
| fe re Difference S.E.
-------------+----------------------------------------------------------------
MedianInco~s | -.0365099 -.0346684 -.0018415 .0061776
RealGDP | -.0003389 -.0063172 .0059783 .0023651
Unemployme~e | -52.20825 -62.66521 10.45696 .
Black | 109.9738 596.9885 -487.0147 3062.718
Asian | -4053.025 614.028 -4667.053 1225.884
White | 5176.204 -482.719 5658.923 1108.901
age1539 | 12986.3 17552.88 -4566.584 3680.005
age4069 | -17154.13 -7609.792 -9544.333 3811.625
------------------------------------------------------------------------------
b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 45.38
Prob>chi2 = 0.0000
(V_b-V_B is not positive definite)
. tsset state_id Year;
panel variable: state_id (strongly balanced)
time variable: Year, 2009 to 2015
delta: 1 unit
. xtserial PropertyCrimes MedianIncomedollars RealGDP UnemploymentRate Black Asian White age15
> 39 age4069, output ;
Linear regression Number of obs = 300
F(8, 49) = 263.36
Prob > F = 0.0000
R-squared = 0.1921
Root MSE = 147.52
(Std. Err. adjusted for 50 clusters in state_id)
-------------------------------------------------------------------------------------
| Robust
D.PropertyCrimes | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
MedianIncomedollars |
D1. | -.0242108 .0133243 -1.82 0.075 -.0509869 .0025653
|
RealGDP |
D1. | -.006721 .0104194 -0.65 0.522 -.0276596 .0142176
|
UnemploymentRate |
D1. | -29.31866 1.870971 -15.67 0.000 -33.07852 -25.55881
|
Black |
D1. | -3886.062 2042.317 -1.90 0.063 -7990.252 218.1284
|
Asian |
D1. | -4052.66 1038.201 -3.90 0.000 -6139.004 -1966.317
|
White |
D1. | 4613.612 1449.886 3.18 0.003 1699.957 7527.267
|
age1539 |
D1. | 8967.636 5293.369 1.69 0.097 -1669.787 19605.06
|
age4069 |
D1. | -9073.082 4880.889 -1.86 0.069 -18881.59 735.4306
-------------------------------------------------------------------------------------
Wooldridge test for autocorrelation in panel data
H0: no first order autocorrelation
F( 1, 49) = 57.853
Prob > F = 0.0000
. *******************************************************;
. generate logpropertycrimes = log(PropertyCrimes);
. generate logMedianIncomedollars=log(MedianIncomedollars);
. generate logRealGDP=log(RealGDP);
. /*Alter2*/
> xtreg logpropertycrimes MedianIncomedollars RealGDP UnemploymentRate Black Asian White age15
> 39 age4069 ,fe;
Fixed-effects (within) regression Number of obs = 350
Group variable: state_id Number of groups = 50
R-sq: Obs per group:
within = 0.4695 min = 7
between = 0.0002 avg = 7.0
overall = 0.0003 max = 7
F(8,292) = 32.30
corr(u_i, Xb) = -0.9012 Prob > F = 0.0000
-------------------------------------------------------------------------------------
logpropertycrimes | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------------+----------------------------------------------------------------
MedianIncomedollars | -.0000175 3.85e-06 -4.55 0.000 -.0000251 -9.96e-06
RealGDP | 1.75e-06 2.60e-06 0.68 0.500 -3.35e-06 6.86e-06
UnemploymentRate | -.0249207 .008613 -2.89 0.004 -.0418722 -.0079692
Black | .6818503 1.266066 0.54 0.591 -1.80992 3.173621
Asian | -1.846826 .569635 -3.24 0.001 -2.967937 -.7257153
White | 2.50183 .5063249 4.94 0.000 1.505321 3.498339
age1539 | 4.246971 2.559519 1.66 0.098 -.7904733 9.284414
age4069 | -7.093295 2.242118 -3.16 0.002 -11.50606 -2.680534
_cons | 7.965341 1.679466 4.74 0.000 4.659947 11.27073
--------------------+----------------------------------------------------------------
sigma_u | .50346388
sigma_e | .06651452
rho | .98284537 (fraction of variance due to u_i)
-------------------------------------------------------------------------------------
F test that all u_i=0: F(49, 292) = 41.74 Prob > F = 0.0000
. /*Alter3*/
> xtreg logpropertycrimes logMedianIncomedollars logRealGDP UnemploymentRate Black Asian White
> age1539 age4069,fe;
Fixed-effects (within) regression Number of obs = 350
Group variable: state_id Number of groups = 50
R-sq: Obs per group:
within = 0.4639 min = 7
between = 0.0036 avg = 7.0
overall = 0.0006 max = 7
F(8,292) = 31.58
corr(u_i, Xb) = -0.9085 Prob > F = 0.0000
----------------------------------------------------------------------------------------
logpropertycrimes | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
logMedianIncomedollars | -.7794694 .2093569 -3.72 0.000 -1.191509 -.3674296
logRealGDP | -.0288089 .1460175 -0.20 0.844 -.3161892 .2585713
UnemploymentRate | -.0247135 .0086591 -2.85 0.005 -.0417556 -.0076714
Black | .3308476 1.258671 0.26 0.793 -2.14637 2.808065
Asian | -2.041885 .5717036 -3.57 0.000 -3.167067 -.9167025
White | 2.571208 .5107763 5.03 0.000 1.565938 3.576478
age1539 | 4.270877 2.576027 1.66 0.098 -.7990578 9.340811
age4069 | -6.731182 2.248002 -2.99 0.003 -11.15552 -2.306842
_cons | 15.74556 2.702799 5.83 0.000 10.42612 21.065
-----------------------+----------------------------------------------------------------
sigma_u | .52166872
sigma_e | .06686315
rho | .98383754 (fraction of variance due to u_i)
----------------------------------------------------------------------------------------
F test that all u_i=0: F(49, 292) = 42.05 Prob > F = 0.0000
. *******************************************************;
. /*ending*/
> log close;
name: <unnamed>
log: /Users/NONAME/Desktop/Eco 490/DATA_2.txt
log type: text
closed on: 23 Mar 2017, 16:33:32
--------------------------------------------------------
