INTRODUCTORY ECONOMETRICS – REPLICATING EXAMPLES
Chapter 9 – Examples
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name: SN
log: ~Wooldridge\intro-econx\iexample9.smcl
log type: smcl
opened on: 10 Jan 2019, 14:06:45
. **********************************************
. * Solomon Negash - Replicating Examples
. * Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6th ed.
. * STATA Program, version 15.1.
. * Chapter 9 - More on Specification and Data Issues
. * Computer Exercises (Examples)
. ******************** SETUP *********************
. *Example 9.1. Economic Model of Crime
. u crime1, clear
. g avgsensq = avgsen^2
. local x "pcnv avgsen tottime ptime86 qemp86 inc86 black hispan"
. local x2 "pcnvsq pt86sq inc86sq"
. eststo hetrosked: qui reg narr86 `x'
. eststo robust: qui reg narr86 `x' `x2', r
. estout , cells(b(nostar fmt(4)) se(par fmt(4))) stats(r2 N, fmt(%9.3f %9.0g) labels(R-squared))
> varlabels(_cons intercept) varwidth(20) ti(Dependent Variables: narr86)
Dependent Variables: narr86
----------------------------------------------
hetrosked robust
b/se b/se
----------------------------------------------
pcnv -0.1332 0.5525
(0.0404) (0.1702)
avgsen -0.0113 -0.0170
(0.0122) (0.0142)
tottime 0.0120 0.0120
(0.0094) (0.0129)
ptime86 -0.0408 0.2874
(0.0088) (0.0694)
qemp86 -0.0505 -0.0141
(0.0144) (0.0168)
inc86 -0.0015 -0.0034
(0.0003) (0.0006)
black 0.3265 0.2923
(0.0454) (0.0581)
hispan 0.1939 0.1636
(0.0397) (0.0397)
pcnvsq -0.7302
(0.1723)
pt86sq -0.0296
(0.0058)
inc86sq 0.0000
(0.0000)
intercept 0.5687 0.5046
(0.0360) (0.0389)
----------------------------------------------
R-squared 0.072 0.103
N 2725 2725
----------------------------------------------
. est clear
. *Example 9.2. Housing Price Equation
. u hprice1, clear
. reg price lotsize sqrft bdrms
Source | SS df MS Number of obs = 88
-------------+---------------------------------- F(3, 84) = 57.46
Model | 617130.701 3 205710.234 Prob > F = 0.0000
Residual | 300723.805 84 3580.0453 R-squared = 0.6724
-------------+---------------------------------- Adj R-squared = 0.6607
Total | 917854.506 87 10550.0518 Root MSE = 59.833
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lotsize | .0020677 .0006421 3.22 0.002 .0007908 .0033446
sqrft | .1227782 .0132374 9.28 0.000 .0964541 .1491022
bdrms | 13.85252 9.010145 1.54 0.128 -4.065141 31.77018
_cons | -21.77031 29.47504 -0.74 0.462 -80.38466 36.84405
------------------------------------------------------------------------------
. predict prhat, xb
. g prhat2=prhat^2
. g prhat3 = prhat^3
. reg price lotsize sqrft bdrms prhat2 prhat3
Source | SS df MS Number of obs = 88
-------------+---------------------------------- F(5, 82) = 39.35
Model | 647870.679 5 129574.136 Prob > F = 0.0000
Residual | 269983.827 82 3292.48569 R-squared = 0.7059
-------------+---------------------------------- Adj R-squared = 0.6879
Total | 917854.506 87 10550.0518 Root MSE = 57.38
------------------------------------------------------------------------------
price | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lotsize | .0001538 .005203 0.03 0.976 -.0101967 .0105043
sqrft | .017602 .2992508 0.06 0.953 -.5777031 .6129071
bdrms | 2.175252 33.8881 0.06 0.949 -65.23897 69.58948
prhat2 | .0003534 .0070989 0.05 0.960 -.0137687 .0144754
prhat3 | 1.55e-06 6.55e-06 0.24 0.814 -.0000115 .0000146
_cons | 166.0939 317.4324 0.52 0.602 -465.3803 797.5682
------------------------------------------------------------------------------
. test prhat2 prhat3
( 1) prhat2 = 0
( 2) prhat3 = 0
F( 2, 82) = 4.67
Prob > F = 0.0120
. reg lprice llotsize lsqrft bdrms
Source | SS df MS Number of obs = 88
-------------+---------------------------------- F(3, 84) = 50.42
Model | 5.15504028 3 1.71834676 Prob > F = 0.0000
Residual | 2.86256324 84 .034078134 R-squared = 0.6430
-------------+---------------------------------- Adj R-squared = 0.6302
Total | 8.01760352 87 .092156362 Root MSE = .1846
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
llotsize | .1679667 .0382812 4.39 0.000 .0918404 .244093
lsqrft | .7002324 .0928652 7.54 0.000 .5155597 .8849051
bdrms | .0369584 .0275313 1.34 0.183 -.0177906 .0917074
_cons | -1.297042 .6512836 -1.99 0.050 -2.592191 -.001893
------------------------------------------------------------------------------
. predict lprhat, xb
. g lprhat2=lprhat^2
. g lprhat3 = lprhat^3
. reg lprice llotsize lsqrft bdrms lprhat2 lprhat3
Source | SS df MS Number of obs = 88
-------------+---------------------------------- F(5, 82) = 32.41
Model | 5.32360036 5 1.06472007 Prob > F = 0.0000
Residual | 2.69400316 82 .032853697 R-squared = 0.6640
-------------+---------------------------------- Adj R-squared = 0.6435
Total | 8.01760352 87 .092156362 Root MSE = .18126
------------------------------------------------------------------------------
lprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
llotsize | -4.190767 12.59521 -0.33 0.740 -29.24665 20.86512
lsqrft | -17.38995 52.48991 -0.33 0.741 -121.8091 87.0292
bdrms | -.927485 2.769755 -0.33 0.739 -6.437411 4.582441
lprhat2 | 3.920341 13.01424 0.30 0.764 -21.96914 29.80982
lprhat3 | -.1933459 .7520741 -0.26 0.798 -1.689461 1.302769
_cons | 88.07241 240.9744 0.37 0.716 -391.3024 567.4472
------------------------------------------------------------------------------
. test lprhat2 lprhat3
( 1) lprhat2 = 0
( 2) lprhat3 = 0
F( 2, 82) = 2.57
Prob > F = 0.0831
. *Example 9.3. IQ as a Proxy for Ability
. u wage2, clear
. eststo A: qui reg lwage educ exper tenur married south urban black
. eststo B: qui reg lwage educ exper tenur married south urban black IQ
. eststo C: qui reg lwage educ exper tenur married south urban black IQ c.educ#c.IQ
. estout , cells(b(nostar fmt(3)) se(par fmt(3))) stats(r2 N, fmt(%9.3f %9.0g) labels(R-squared))
> varlabels(_cons intercept) varwidth(20) ti(Dependent Variables: log(wage))
Dependent Variables: log(wage)
-----------------------------------------------------------
A B C
b/se b/se b/se
-----------------------------------------------------------
educ 0.065 0.054 0.018
(0.006) (0.007) (0.041)
exper 0.014 0.014 0.014
(0.003) (0.003) (0.003)
tenure 0.012 0.011 0.011
(0.002) (0.002) (0.002)
married 0.199 0.200 0.201
(0.039) (0.039) (0.039)
south -0.091 -0.080 -0.080
(0.026) (0.026) (0.026)
urban 0.184 0.182 0.184
(0.027) (0.027) (0.027)
black -0.188 -0.143 -0.147
(0.038) (0.039) (0.040)
IQ 0.004 -0.001
(0.001) (0.005)
c.educ#c.IQ 0.000
(0.000)
intercept 5.395 5.176 5.648
(0.113) (0.128) (0.546)
-----------------------------------------------------------
R-squared 0.253 0.263 0.263
N 935 935 935
-----------------------------------------------------------
. est clear
. *Example 9.4. City Crime Rates
. u crime2, clear
. eststo A: qui reg lcrmrte unem llawexp if year==87
. eststo B: qui reg lcrmrte unem llawexp lcrmrt_1 if year==87
. estout , cells(b(nostar fmt(3)) se(par fmt(3))) stats(r2 N, fmt(%9.3f %9.0g) labels(R-squared))
> varlabels(_cons intercept) varwidth(20) ti("Table 9.3 Dependent Variable: log(crmrte_87)")
Table 9.3 Dependent Variable: log(crmrte_87)
----------------------------------------------
A B
b/se b/se
----------------------------------------------
unem -0.029 0.009
(0.032) (0.020)
llawexpc 0.203 -0.140
(0.173) (0.109)
lcrmrt_1 1.194
(0.132)
intercept 3.343 0.076
(1.251) (0.821)
----------------------------------------------
R-squared 0.057 0.680
N 46 46
----------------------------------------------
. est clear
. *Example 9.5. Savings Function with Measurement Error
. **NA
. *Example 9.6. Measurement Error in Scrap Rates
. **NA
. *Example 9.7. GPA Equation with Measurement Error
. **NA
. *Example 9.8. R&D Intensity and Firm Size
. u rdchem, clear
. reg rdintens sales profmarg
Source | SS df MS Number of obs = 32
-------------+---------------------------------- F(2, 29) = 1.19
Model | 8.28423732 2 4.14211866 Prob > F = 0.3173
Residual | 100.549233 29 3.46721493 R-squared = 0.0761
-------------+---------------------------------- Adj R-squared = 0.0124
Total | 108.83347 31 3.51075711 Root MSE = 1.862
------------------------------------------------------------------------------
rdintens | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sales | .0000534 .0000441 1.21 0.236 -.0000368 .0001435
profmarg | .0446166 .0461805 0.97 0.342 -.0498332 .1390664
_cons | 2.625261 .5855328 4.48 0.000 1.427712 3.82281
------------------------------------------------------------------------------
. reg rdintens sales profmarg if sales<30000
Source | SS df MS Number of obs = 31
-------------+---------------------------------- F(2, 28) = 2.92
Model | 18.7880289 2 9.39401445 Prob > F = 0.0702
Residual | 89.9330615 28 3.21189505 R-squared = 0.1728
-------------+---------------------------------- Adj R-squared = 0.1137
Total | 108.72109 30 3.62403635 Root MSE = 1.7922
------------------------------------------------------------------------------
rdintens | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sales | .0001856 .0000842 2.20 0.036 .0000131 .0003581
profmarg | .0478411 .0444831 1.08 0.291 -.0432784 .1389605
_cons | 2.296851 .5918045 3.88 0.001 1.084594 3.509107
------------------------------------------------------------------------------
. *Example 9.9. R&D Intensity
. u rdchem, clear
. reg lrd lsales profmarg
Source | SS df MS Number of obs = 32
-------------+---------------------------------- F(2, 29) = 162.23
Model | 85.5967531 2 42.7983766 Prob > F = 0.0000
Residual | 7.65051127 29 .263810733 R-squared = 0.9180
-------------+---------------------------------- Adj R-squared = 0.9123
Total | 93.2472644 31 3.00797627 Root MSE = .51363
------------------------------------------------------------------------------
lrd | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lsales | 1.08422 .060195 18.01 0.000 .9611073 1.207333
profmarg | .0216557 .0127826 1.69 0.101 -.0044877 .0477991
_cons | -4.378273 .4680185 -9.35 0.000 -5.335479 -3.421068
------------------------------------------------------------------------------
. reg lrd lsales profmarg if sales<30000
Source | SS df MS Number of obs = 31
-------------+---------------------------------- F(2, 28) = 131.42
Model | 71.7652353 2 35.8826176 Prob > F = 0.0000
Residual | 7.64520626 28 .273043081 R-squared = 0.9037
-------------+---------------------------------- Adj R-squared = 0.8968
Total | 79.4104415 30 2.64701472 Root MSE = .52254
------------------------------------------------------------------------------
lrd | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lsales | 1.088047 .0671137 16.21 0.000 .9505712 1.225524
profmarg | .0217552 .013024 1.67 0.106 -.0049232 .0484335
_cons | -4.40414 .5110218 -8.62 0.000 -5.450921 -3.357359
------------------------------------------------------------------------------
. *Example 9.10. State Infant Mortality Rates
. u infmrt, clear
. reg infmort lpcinc lphysic lpopul if year==1990
Source | SS df MS Number of obs = 51
-------------+---------------------------------- F(3, 47) = 2.53
Model | 32.162998 3 10.7209993 Prob > F = 0.0684
Residual | 199.084471 47 4.23583981 R-squared = 0.1391
-------------+---------------------------------- Adj R-squared = 0.0841
Total | 231.247469 50 4.62494938 Root MSE = 2.0581
------------------------------------------------------------------------------
infmort | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lpcinc | -4.684662 2.604124 -1.80 0.078 -9.923484 .5541607
lphysic | 4.153261 1.512659 2.75 0.009 1.110185 7.196338
lpopul | -.0878224 .28725 -0.31 0.761 -.6656948 .4900499
_cons | 33.85931 20.42785 1.66 0.104 -7.236219 74.95484
------------------------------------------------------------------------------
. reg infmort lpcinc lphysic lpopul if year==1990 & DC==0
Source | SS df MS Number of obs = 50
-------------+---------------------------------- F(3, 46) = 5.76
Model | 26.8600265 3 8.95334216 Prob > F = 0.0020
Residual | 71.4631754 46 1.55354729 R-squared = 0.2732
-------------+---------------------------------- Adj R-squared = 0.2258
Total | 98.3232019 49 2.00659596 Root MSE = 1.2464
------------------------------------------------------------------------------
infmort | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lpcinc | -.5669275 1.641216 -0.35 0.731 -3.870524 2.736669
lphysic | -2.741837 1.190773 -2.30 0.026 -5.138739 -.3449347
lpopul | .6292349 .1911062 3.29 0.002 .2445581 1.013912
_cons | 23.95479 12.41946 1.93 0.060 -1.044288 48.95388
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. log close
name: SN
log: ~Wooldridge\intro-econx\iexample9.smcl
log type: smcl
closed on: 10 Jan 2019, 14:06:46
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