INTRODUCTORY ECONOMETRICS – REPLICATING EXAMPLES
Chapter 18. Advanced Time Series Topics – Examples
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name: SN
log: ~Wooldridge\intro-econx\iexample18.smcl
log type: smcl
opened on: 21 Jan 2019, 15:10:39
. **********************************************
. * Solomon Negash - Replicating Examples
. * Wooldridge (2016). Introductory Econometrics: A Modern Approach. 6th ed.
. * STATA Program, version 15.1.
. * CHAPTER 18 Advanced Time Series Topics
. * Computer Exercises (Examples)
. ******************** SETUP *********************
. *Example 18.1. Housing Investment and Residential Price Inflation
. u hseinv, clear
. tsset year
time variable: year, 1947 to 1988
delta: 1 unit
. reg linvpc t
Source | SS df MS Number of obs = 42
-------------+---------------------------------- F(1, 40) = 20.19
Model | .409446973 1 .409446973 Prob > F = 0.0001
Residual | .811173061 40 .020279327 R-squared = 0.3354
-------------+---------------------------------- Adj R-squared = 0.3188
Total | 1.22062003 41 .02977122 Root MSE = .14241
------------------------------------------------------------------------------
linvpc | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
t | .0081459 .0018129 4.49 0.000 .0044819 .0118098
_cons | -.8412918 .044744 -18.80 0.000 -.9317228 -.7508608
------------------------------------------------------------------------------
. predict y, res
. g y1 = y[_n-1]
(1 missing value generated)
. g gprice_1 = gprice[_n-1]
(2 missing values generated)
. eststo GeometricDL: qui reg y gprice y1
. eststo RationalDL: qui reg y gprice y1 gprice_1
. estout, cells(b(nostar fmt(3)) se(par fmt(3))) stats(N r2_a, fmt(%5.0g) labels(Smaple-///
size Adjusted-R-squared)) varlabels(_cons constant) varwidth(18) ti("Table 18.1 ///
Distributed Lag Models for Housing Investment: log(invpc)")
Table 18.1 Distributed Lag Models for Housing Investment: log(invpc)
--------------------------------------------
GeometricDL RationalDL
b/se b/se
--------------------------------------------
gprice 3.095 3.256
(0.933) (0.970)
y1 0.340 0.547
(0.132) (0.152)
gprice_1 -2.936
(0.973)
constant -0.010 0.006
(0.018) (0.017)
--------------------------------------------
Smaple-size 41 40
Adjusted-R-squared .375 .504
--------------------------------------------
. est clear
. *Example 18.2. Unit Root Test for Three-Month T-Bill Rates
. u intqrt, clear
. reg cr3 r3_1
Source | SS df MS Number of obs = 123
-------------+---------------------------------- F(1, 121) = 6.12
Model | 9.22556542 1 9.22556542 Prob > F = 0.0148
Residual | 182.506035 121 1.50831434 R-squared = 0.0481
-------------+---------------------------------- Adj R-squared = 0.0403
Total | 191.7316 122 1.5715705 Root MSE = 1.2281
------------------------------------------------------------------------------
cr3 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
r3_1 | -.0907106 .0366782 -2.47 0.015 -.1633247 -.0180965
_cons | .6253371 .2608254 2.40 0.018 .1089645 1.14171
------------------------------------------------------------------------------
. display "roh = " 1 + _b[r3_1]
roh = .90928938
. di "t statistics on r3_1 = " _b[r3_1]/_se[r3_1]
t statistics on r3_1 = -2.4731506
. reg r3 r3_1
Source | SS df MS Number of obs = 123
-------------+---------------------------------- F(1, 121) = 614.60
Model | 927.002641 1 927.002641 Prob > F = 0.0000
Residual | 182.506035 121 1.50831434 R-squared = 0.8355
-------------+---------------------------------- Adj R-squared = 0.8341
Total | 1109.50868 122 9.09433341 Root MSE = 1.2281
------------------------------------------------------------------------------
r3 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
r3_1 | .9092894 .0366782 24.79 0.000 .8366753 .9819035
_cons | .6253371 .2608254 2.40 0.018 .1089645 1.14171
------------------------------------------------------------------------------
. *Example 18.3. Unit Root Test for Annual U.S. Inflation
. u phillips, clear
. reg cinf inf_1 cinf_1
Source | SS df MS Number of obs = 47
-------------+---------------------------------- F(2, 44) = 4.57
Model | 38.4043273 2 19.2021636 Prob > F = 0.0158
Residual | 184.96036 44 4.20364454 R-squared = 0.1719
-------------+---------------------------------- Adj R-squared = 0.1343
Total | 223.364687 46 4.85575407 Root MSE = 2.0503
------------------------------------------------------------------------------
cinf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inf_1 | -.3103252 .1027077 -3.02 0.004 -.517319 -.1033315
cinf_1 | .1383615 .1264026 1.09 0.280 -.1163861 .3931091
_cons | 1.360791 .5167103 2.63 0.012 .3194297 2.402152
------------------------------------------------------------------------------
. di "roh = " 1 + _b[inf_1]
roh = .68967477
. reg cinf inf_1
Source | SS df MS Number of obs = 48
-------------+---------------------------------- F(1, 46) = 9.79
Model | 54.3454788 1 54.3454788 Prob > F = 0.0030
Residual | 255.342656 46 5.55092731 R-squared = 0.1755
-------------+---------------------------------- Adj R-squared = 0.1576
Total | 309.688135 47 6.58910925 Root MSE = 2.356
------------------------------------------------------------------------------
cinf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inf_1 | -.3347414 .1069819 -3.13 0.003 -.5500849 -.1193979
_cons | 1.27665 .5576568 2.29 0.027 .1541456 2.399155
------------------------------------------------------------------------------
. di "roh2 = " 1 + _b[inf_1]
roh2 = .66525859
. *Example 18.4. Unit Root in the Log of U.S. Real Gross Domestic Product
. u inven, clear
. g lgdp_1 = ln(gdp[_n-1])
(1 missing value generated)
. g ggdp_1 = ggdp[_n-1]
(2 missing values generated)
. egen t=seq()
. reg ggdp t lgdp_1 ggdp_1
Source | SS df MS Number of obs = 35
-------------+---------------------------------- F(3, 31) = 3.78
Model | .004591904 3 .001530635 Prob > F = 0.0201
Residual | .012541759 31 .000404573 R-squared = 0.2680
-------------+---------------------------------- Adj R-squared = 0.1972
Total | .017133663 34 .000503931 Root MSE = .02011
------------------------------------------------------------------------------
ggdp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
t | .0058696 .002696 2.18 0.037 .0003712 .0113681
lgdp_1 | -.2096209 .086594 -2.42 0.022 -.3862305 -.0330113
ggdp_1 | .2637508 .1647395 1.60 0.120 -.0722377 .5997392
_cons | 1.650923 .6663996 2.48 0.019 .2917916 3.010054
------------------------------------------------------------------------------
. di "roh = " 1 + _b[lgdp_1]
roh = .79037908
. reg ggdp lgdp_1 ggdp_1
Source | SS df MS Number of obs = 35
-------------+---------------------------------- F(2, 32) = 2.96
Model | .002674172 2 .001337086 Prob > F = 0.0662
Residual | .014459491 32 .000451859 R-squared = 0.1561
-------------+---------------------------------- Adj R-squared = 0.1033
Total | .017133663 34 .000503931 Root MSE = .02126
------------------------------------------------------------------------------
ggdp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lgdp_1 | -.0226875 .0118894 -1.91 0.065 -.0469055 .0015304
ggdp_1 | .1671607 .1676688 1.00 0.326 -.1743696 .5086909
_cons | .2148857 .1004679 2.14 0.040 .0102392 .4195321
------------------------------------------------------------------------------
. di "roh = " 1 + _b[lgdp_1]
roh = .97731246
. *Example 18.5. Cointegration between Fertility and Personal Exemption
. u fertil3, clear
. tsset t
time variable: t, 1 to 72
delta: 1 unit
. reg gfr t pe
Source | SS df MS Number of obs = 72
-------------+---------------------------------- F(2, 69) = 34.53
Model | 13929.0853 2 6964.54264 Prob > F = 0.0000
Residual | 13918.8101 69 201.721886 R-squared = 0.5002
-------------+---------------------------------- Adj R-squared = 0.4857
Total | 27847.8954 71 392.223879 Root MSE = 14.203
------------------------------------------------------------------------------
gfr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
t | -.9051881 .1089923 -8.31 0.000 -1.122622 -.6877543
pe | .186662 .0346265 5.39 0.000 .1175841 .2557399
_cons | 109.9302 3.47526 31.63 0.000 102.9972 116.8631
------------------------------------------------------------------------------
. predict u, res
. //regression in levels
. reg cgfr cpe
Source | SS df MS Number of obs = 71
-------------+---------------------------------- F(1, 69) = 2.26
Model | 40.3237206 1 40.3237206 Prob > F = 0.1370
Residual | 1229.25866 69 17.8153428 R-squared = 0.0318
-------------+---------------------------------- Adj R-squared = 0.0177
Total | 1269.58238 70 18.1368911 Root MSE = 4.2208
------------------------------------------------------------------------------
cgfr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cpe | -.0426776 .0283672 -1.50 0.137 -.0992686 .0139134
_cons | -.7847796 .5020398 -1.56 0.123 -1.786322 .2167625
------------------------------------------------------------------------------
. // Augmented DF test for gfr & pe
. dfuller gfr, lags(1) trend
Augmented Dickey-Fuller test for unit root Number of obs = 70
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -1.474 -4.106 -3.480 -3.168
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.8378
. dfuller pe, lags(1) trend
Augmented Dickey-Fuller test for unit root Number of obs = 70
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -1.471 -4.106 -3.480 -3.168
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.8388
. //Regression in levels with a single lag & time trend, manually
. gen u_1=u[_n-1]
(1 missing value generated)
. gen cu = u - u_1
(1 missing value generated)
. gen cu_1 = cu[_n-1]
(2 missing values generated)
. reg cu u_1 cu_1 t
Source | SS df MS Number of obs = 70
-------------+---------------------------------- F(3, 66) = 3.07
Model | 291.902357 3 97.3007857 Prob > F = 0.0338
Residual | 2092.94085 66 31.711225 R-squared = 0.1224
-------------+---------------------------------- Adj R-squared = 0.0825
Total | 2384.84321 69 34.562945 Root MSE = 5.6313
------------------------------------------------------------------------------
cu | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
u_1 | -.1188282 .0490884 -2.42 0.018 -.2168364 -.0208201
cu_1 | .2378983 .1176739 2.02 0.047 .0029547 .4728418
t | .0257499 .0334197 0.77 0.444 -.0409748 .0924746
_cons | -1.150008 1.424272 -0.81 0.422 -3.993659 1.693644
------------------------------------------------------------------------------
. //Test alternativelly using the augumented DF command in Stata
. dfuller u, lags(1) trend reg
Augmented Dickey-Fuller test for unit root Number of obs = 70
---------- Interpolated Dickey-Fuller ---------
Test 1% Critical 5% Critical 10% Critical
Statistic Value Value Value
------------------------------------------------------------------------------
Z(t) -2.421 -4.106 -3.480 -3.168
------------------------------------------------------------------------------
MacKinnon approximate p-value for Z(t) = 0.3687
------------------------------------------------------------------------------
D.u | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
u |
L1. | -.1188282 .0490884 -2.42 0.018 -.2168364 -.0208201
LD. | .2378983 .1176739 2.02 0.047 .0029547 .4728418
_trend | .0257499 .0334197 0.77 0.444 -.0409748 .0924746
_cons | -1.124258 1.394914 -0.81 0.423 -3.909294 1.660778
------------------------------------------------------------------------------
. // First difference regression, with two lags (equation 11.27)
. reg cgfr cpe cpe_1 cpe_2
Source | SS df MS Number of obs = 69
-------------+---------------------------------- F(3, 65) = 6.56
Model | 293.259859 3 97.7532864 Prob > F = 0.0006
Residual | 968.199959 65 14.895384 R-squared = 0.2325
-------------+---------------------------------- Adj R-squared = 0.1971
Total | 1261.45982 68 18.5508797 Root MSE = 3.8595
------------------------------------------------------------------------------
cgfr | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cpe | -.0362021 .0267737 -1.35 0.181 -.089673 .0172687
cpe_1 | -.0139706 .0275539 -0.51 0.614 -.0689997 .0410584
cpe_2 | .1099896 .0268797 4.09 0.000 .0563071 .1636721
_cons | -.9636787 .4677599 -2.06 0.043 -1.89786 -.0294976
------------------------------------------------------------------------------
. *Example 18.6. Cointegrating Parameter for Interest Rates
. u intqrt, clear
. g cr3_2=cr3[_n-2]
(3 missing values generated)
. g cr3_a=cr3[_n+1]
(1 missing value generated)
. g cr3_b=cr3[_n+2]
(2 missing values generated)
. reg r6 r3 cr3 cr3_1 cr3_2 cr3_a cr3_b
Source | SS df MS Number of obs = 119
-------------+---------------------------------- F(6, 112) = 3176.06
Model | 1148.95762 6 191.492937 Prob > F = 0.0000
Residual | 6.75277093 112 .060292598 R-squared = 0.9942
-------------+---------------------------------- Adj R-squared = 0.9938
Total | 1155.71039 118 9.79415587 Root MSE = .24555
------------------------------------------------------------------------------
r6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
r3 | 1.038171 .0080773 128.53 0.000 1.022167 1.054175
cr3 | -.0531227 .0194406 -2.73 0.007 -.0916418 -.0146036
cr3_1 | -.0611365 .0190433 -3.21 0.002 -.0988684 -.0234046
cr3_2 | -.0437775 .0189032 -2.32 0.022 -.0812318 -.0063233
cr3_a | -.0035722 .0191223 -0.19 0.852 -.0414606 .0343163
cr3_b | .0123662 .0189704 0.65 0.516 -.0252213 .0499536
_cons | .0651458 .0569524 1.14 0.255 -.047698 .1779895
------------------------------------------------------------------------------
. *test Ho: B=1
. di (_b[r3]-1)/_se[r3]
4.7256731
. *Test serial correlation
. predict u, res
(5 missing values generated)
. g u_1 = u[_n-1]
(5 missing values generated)
. reg r6 r3 cr3 cr3_1 cr3_2 cr3_a cr3_b u_1
Source | SS df MS Number of obs = 118
-------------+---------------------------------- F(7, 110) = 2692.36
Model | 1144.45133 7 163.493048 Prob > F = 0.0000
Residual | 6.67973808 110 .060724892 R-squared = 0.9942
-------------+---------------------------------- Adj R-squared = 0.9938
Total | 1151.13107 117 9.83872711 Root MSE = .24642
------------------------------------------------------------------------------
r6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
r3 | 1.037809 .008159 127.20 0.000 1.02164 1.053979
cr3 | -.0529965 .0195394 -2.71 0.008 -.091719 -.014274
cr3_1 | -.0604062 .019224 -3.14 0.002 -.0985036 -.0223088
cr3_2 | -.0438142 .0189911 -2.31 0.023 -.0814501 -.0061783
cr3_a | -.0038517 .0192129 -0.20 0.841 -.0419272 .0342237
cr3_b | .0104361 .0192032 0.54 0.588 -.0276201 .0484922
u_1 | .1039441 .0961908 1.08 0.282 -.0866835 .2945718
_cons | .0675986 .0577105 1.17 0.244 -.0467701 .1819673
------------------------------------------------------------------------------
. reg u u_1
Source | SS df MS Number of obs = 118
-------------+---------------------------------- F(1, 116) = 1.22
Model | .070280124 1 .070280124 Prob > F = 0.2716
Residual | 6.68042825 116 .057589899 R-squared = 0.0104
-------------+---------------------------------- Adj R-squared = 0.0019
Total | 6.75070837 117 .057698362 Root MSE = .23998
------------------------------------------------------------------------------
u | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
u_1 | .1031829 .0934039 1.10 0.272 -.0818152 .2881811
_cons | .0000442 .022094 0.00 0.998 -.0437158 .0438041
------------------------------------------------------------------------------
. *Compare with Simple OLS
. reg r6 r3
Source | SS df MS Number of obs = 124
-------------+---------------------------------- F(1, 122) = 17710.54
Model | 1182.09126 1 1182.09126 Prob > F = 0.0000
Residual | 8.14289673 122 .066745055 R-squared = 0.9932
-------------+---------------------------------- Adj R-squared = 0.9931
Total | 1190.23416 123 9.6767005 Root MSE = .25835
------------------------------------------------------------------------------
r6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
r3 | 1.025899 .0077088 133.08 0.000 1.010639 1.04116
_cons | .1353736 .0548673 2.47 0.015 .0267584 .2439889
------------------------------------------------------------------------------
. *Example 18.7. Error Correction Model for Holding Yields
. u intqrt, clear
. g hy3_2=hy3[_n-2]
(2 missing values generated)
. g hy6_1hy3_2= hy6_1 - hy3_2
(2 missing values generated)
. reg chy6 chy3_1 hy6_1hy3_2
Source | SS df MS Number of obs = 122
-------------+---------------------------------- F(2, 119) = 223.79
Model | 51.8888369 2 25.9444184 Prob > F = 0.0000
Residual | 13.795981 119 .115932613 R-squared = 0.7900
-------------+---------------------------------- Adj R-squared = 0.7864
Total | 65.6848179 121 .542849734 Root MSE = .34049
------------------------------------------------------------------------------
chy6 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
chy3_1 | 1.218364 .2636012 4.62 0.000 .6964078 1.740321
hy6_1hy3_2 | -.8400485 .2441269 -3.44 0.001 -1.323444 -.3566528
_cons | .0898483 .042688 2.10 0.037 .0053217 .1743748
------------------------------------------------------------------------------
. *Example 18.8. Forecasting the U.S. Unemployment Rate
. u phillips, clear
. reg unem unem_1
Source | SS df MS Number of obs = 48
-------------+---------------------------------- F(1, 46) = 57.13
Model | 62.8162728 1 62.8162728 Prob > F = 0.0000
Residual | 50.5768515 46 1.09949677 R-squared = 0.5540
-------------+---------------------------------- Adj R-squared = 0.5443
Total | 113.393124 47 2.41261967 Root MSE = 1.0486
------------------------------------------------------------------------------
unem | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem_1 | .7323538 .0968906 7.56 0.000 .537323 .9273845
_cons | 1.571741 .5771181 2.72 0.009 .4100629 2.73342
------------------------------------------------------------------------------
. di "Forcasts of unem for 1997 =" %6.3f _b[_cons] + _b[unem_1]*5.4
Forcasts of unem for 1997 = 5.526
. reg unem unem_1 inf_1
Source | SS df MS Number of obs = 48
-------------+---------------------------------- F(2, 45) = 50.22
Model | 78.3083336 2 39.1541668 Prob > F = 0.0000
Residual | 35.0847907 45 .779662015 R-squared = 0.6906
-------------+---------------------------------- Adj R-squared = 0.6768
Total | 113.393124 47 2.41261967 Root MSE = .88298
------------------------------------------------------------------------------
unem | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem_1 | .6470261 .0838056 7.72 0.000 .4782329 .8158192
inf_1 | .1835766 .0411828 4.46 0.000 .1006302 .2665231
_cons | 1.303797 .4896861 2.66 0.011 .3175188 2.290076
------------------------------------------------------------------------------
. di "Forcasts of unem for 1997 =" %6.3f _b[_cons] + _b[unem_1]*5.4 + _b[inf_1]*3
Forcasts of unem for 1997 = 5.348
. *95% forecast interval
. g unem_1f = unem_1 - 5.4
(1 missing value generated)
. g inf_1f = inf_1 - 3
(1 missing value generated)
. reg unem unem_1f inf_1f
Source | SS df MS Number of obs = 48
-------------+---------------------------------- F(2, 45) = 50.22
Model | 78.3083334 2 39.1541667 Prob > F = 0.0000
Residual | 35.0847909 45 .779662019 R-squared = 0.6906
-------------+---------------------------------- Adj R-squared = 0.6768
Total | 113.393124 47 2.41261967 Root MSE = .88298
------------------------------------------------------------------------------
unem | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
unem_1f | .6470261 .0838056 7.72 0.000 .4782329 .8158192
inf_1f | .1835766 .0411828 4.46 0.000 .1006302 .2665231
_cons | 5.348468 .1365394 39.17 0.000 5.073463 5.623472
------------------------------------------------------------------------------
. di "Forcast = "%5.3f _b[_cons] ", SE = " %5.3f _se[_cons] " & se(e+1) = " %5.3f ///
[_se[_cons]^2 + e(rmse)^2]^0.5
Forcast = 5.348, SE = 0.137 & se(e+1) = 0.893
. di "The 95% forcast interval is " "[" %6.3f _b[_cons] - 1.96 * [_se[_cons]^2 + ///
e(rmse)^2]^0.5 " , "%6.3f _b[_cons] + 1.96 * [_se[_cons]^2 + e(rmse)^2]^0.5 "]"
The 95% forcast interval is [ 3.597 , 7.100]
. *Example 18.9. Out-of-Sample Comparisons of Unemployment Forecasts
. u phillips, clear
. qui reg unem unem_1
. di "RMSE = " %5.3f e(rmse)
RMSE = 1.049
. predict u, res
(1 missing value generated)
. g ua=abs(u)
(1 missing value generated)
. sum ua
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
ua | 48 .8129182 .633409 .0967489 2.827077
. di "MSE = " %5.3f r(mean)
MSE = 0.813
. qui reg unem unem_1 inf_1
. di "RMSE = " %5.3f e(rmse)
RMSE = 0.883
. predict uinf, res
(1 missing value generated)
. g uinfa=abs(uinf)
(1 missing value generated)
. sum uinfa
Variable | Obs Mean Std. Dev. Min Max
-------------+---------------------------------------------------------
uinfa | 48 .6493123 .562057 .0124001 2.172966
. di "MSE = " %5.3f r(mean)
MSE = 0.649
. *Example 18.10. Two-Year-Ahead Forecast for the Unemployment Rate
. u phillips, clear
. reg inf inf_1 unem_1, nohead
------------------------------------------------------------------------------
inf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inf_1 | .6588309 .1110218 5.93 0.000 .4352214 .8824404
unem_1 | .057266 .2259257 0.25 0.801 -.3977716 .5123036
_cons | .9740445 1.32011 0.74 0.464 -1.684794 3.632883
------------------------------------------------------------------------------
. reg inf inf_1
Source | SS df MS Number of obs = 48
-------------+---------------------------------- F(1, 46) = 38.67
Model | 214.647351 1 214.647351 Prob > F = 0.0000
Residual | 255.342659 46 5.55092736 R-squared = 0.4567
-------------+---------------------------------- Adj R-squared = 0.4449
Total | 469.99001 47 9.99978744 Root MSE = 2.356
------------------------------------------------------------------------------
inf | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
inf_1 | .6652586 .1069819 6.22 0.000 .4499151 .8806021
_cons | 1.27665 .5576568 2.29 0.027 .1541456 2.399155
------------------------------------------------------------------------------
. qui {
g inf_96=inf if year==1996
g inf_97= _b[_cons] + _b[inf]*inf_96
sum inf_97
}
. di "Inf_97 = " %5.2f r(mean)
Inf_97 = 3.27
. qui {
reg unem unem_1 inf_1
g unem_96 = unem if year==1996
g unem_97 = _b[_cons] + _b[unem_1]*unem_96 + _b[inf]*inf_96
g unem_98 = _b[_cons] + _b[unem_1]*unem_97 + _b[inf]*inf_97
sum unem_98
}
. di "unem_98 = " %5.2f r(mean)
unem_98 = 5.37
. log close
name: SN
log: ~Wooldridge\intro-econx\iexample16.smcl
log type: smcl
closed on: 21 Jan 2019, 15:10:41
-------------------------------------------------------------------------------------
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