g X1=X-L.X g Y1=Y-L.Y reg Y1 X1
Source SS df MS Number of obs = 27 F( 1, 25) = 397.46 Model 485227703 1 485227703 Prob > F = 0.0000 Residual 30520493.1 25 1220819.73 R-squared = 0.9408 Adj R-squared = 0.9385 Total 515748196 26 19836469.1 Root MSE = 1104.9 Y1 Coef. Std. Err. t P>|t| [95% Conf. Interval] X1 .5964135 .0299158 19.94 0.000 .5348008 .6580262 _cons 889.3387 260.8835 3.41 0.002 352.0391 1426.638
estat dwatson
Durbin-Watson d-statistic( 2, 27) = .9608428
4-练习题10（P155）
reg Y X1 X2
Source SS df MS Number of obs = 10 F( 2, 7) = 88.85 Model 855305.867 2 427652.934 Prob > F = 0.0000 Residual 33694.1329 7 4813.44755 R-squared = 0.9621 Adj R-squared = 0.9513 Total 889000 9 98777.7778 Root MSE = 69.379 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X1 .5684245 .7160975 0.79 0.453 -1.124877 2.261726 X2 -.0058326 .0702937 -0.08 0.936 -.1720507 .1603855 _cons 245.5158 69.52348 3.53 0.010 81.11887 409.9127
第五章
例5.1.1(P160)
reg Y1 X1
Source SS df MS Number of obs = 31 F( 1, 29) = 583.13 Model 182436639 1 182436639 Prob > F = 0.0000 Residual 9072820.63 29 312855.884 R-squared = 0.9526 Adj R-squared = 0.9510 Total 191509460 30 6383648.66 Root MSE = 559.34 Y1 Coef. Std. Err. t P>|t| [95% Conf. Interval] X1 .6919714 .0286553 24.15 0.000 .6333648 .750578 _cons 450.3413 388.9091 1.16 0.256 -345.0672 1245.75
reg Y2 X2
Source SS df MS Number of obs = 31 F( 1, 29) = 247.88 Model 60399153.9 1 60399153.9 Prob > F = 0.0000 Residual 7066328.2 29 243666.49 R-squared = 0.8953 Adj R-squared = 0.8916 Total 67465482.1 30 2248849.4 Root MSE = 493.63 Y2 Coef. Std. Err. t P>|t| [95% Conf. Interval] X2 .7195035 .0456999 15.74 0.000 .6260367 .8129703 _cons 179.1848 221.5788 0.81 0.425 -273.9948 632.3644
tab(region),g(D)
region Freq. Percent Cum. 3??ò 31 50.00 50.00 ??′? 31 50.00 100.00 Total 62 100.00drop D1 g DX=D2*X
reg Y X D2 DX
Source SS df MS Number of obs = 62 F( 3, 58) = 992.43 Model 828461102 3 276153701 Prob > F = 0.0000 Residual 16139148.8 58 278261.187 R-squared = 0.9809 Adj R-squared = 0.9799 Total 844600251 61 13845905.8 Root MSE = 527.5 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X .6919714 .0270245 25.61 0.000 .6378759 .7460669 D2 -271.1565 436.5699 -0.62 0.537 -1145.046 602.7331 DX .0275321 .0558151 0.49 0.624 -.0841939 .1392581 _cons 450.3413 366.7772 1.23 0.224 -283.843 1184.526
例5.2.2（P168）（和书上的答案略有不同）
g X1=X[_n-1] g X2=X[_n-2] g X3=X[_n-3] g X4=X[_n-4] g X5=X[_n-5] g X6=X[_n-6] g X7=X[_n-7] g lnX=ln(X) g lnX1=ln(X1) g lnX2=ln(X2) g lnX3=ln(X3) g lnX4=ln(X4) g lnX5=ln(X5) g lnX6=ln(X6) g lnX7=ln(X7) g lnY=ln(Y)
g W0=lnX+lnX1+lnX2+lnX3+lnX4+lnX5+lnX6+lnX7
g W1=lnX1+2*lnX2+3*lnX3+lnX*4+lnX5*5+lnX6*6+lnX7*7 g W2=4*lnX2+9*lnX3+lnX4*16+lnX5*25+lnX6*36+lnX7*49 reg lnY W0 W1 W2
Source SS df MS Number of obs = 21 F( 3, 17) = 1187.99 Model 5.01994038 3 1.67331346 Prob > F = 0.0000 Residual .023944895 17 .001408523 R-squared = 0.9953 Adj R-squared = 0.9944 Total 5.04388527 20 .252194264 Root MSE = .03753 lnY Coef. Std. Err. t P>|t| [95% Conf. Interval] W0 .142537 .023324 6.11 0.000 .0933277 .1917464 W1 -.0580477 .0206204 -2.82 0.012 -.101553 -.0145424 W2 .0062676 .002926 2.14 0.047 .0000942 .012441 _cons 6.708366 .0426449 157.31 0.000 6.618393 6.798339
例5.2.2（P173）
tsset year
time variable: year, 1978 to 2007
delta: 1 unit
g Yt1=Y[_n-1] reg Y X P Yt1
Source SS df MS Number of obs = 29 F( 3, 25) = 5869.00 Model 2.2684e+09 3 756146042 Prob > F = 0.0000 Residual 3220932.79 25 128837.312 R-squared = 0.9986 Adj R-squared = 0.9984 Total 2.2717e+09 28 81130680.7 Root MSE = 358.94 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X .0357098 .012565 2.84 0.009 .0098317 .061588 P 7.455727 3.065732 2.43 0.023 1.141733 13.76972 Yt1 .7236337 .1327963 5.45 0.000 .4501346 .9971328 _cons -202.5274 221.9648 -0.91 0.370 -659.6724 254.6176
练习题5（P186）
（和例5.2.3相似，具体步骤略） （1）估计Y*
tsset year
time variable: year, 1970 to 1991
delta: 1 unit g Yt1=Y[_n-1] reg Y X Yt1
Source SS df MS Number of obs = 21 F( 2, 18) = 621.38 Model 51963.6177 2 25981.8089 Prob > F = 0.0000 Residual 752.640861 18 41.8133812 R-squared = 0.9857 Adj R-squared = 0.9841 Total 52716.2586 20 2635.81293 Root MSE = 6.4663 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X .6480192 .1034473 6.26 0.000 .4306844 .865354 Yt1 .2415177 .1223811 1.97 0.064 -.0155954 .4986308 _cons -14.5344 4.87717 -2.98 0.008 -24.78095 -4.287846 通过自回归模型的参数估计，可以得到Y即理想的或长期的新建厂房企业开支。
(2)存量调整模型（对数转换）： g lnY=ln(Y)
g lnYt1=ln(Yt1) g lnX=ln(X)
*
reg lnY lnX lnYt1
Source SS df MS Number of obs = 21 F( 2, 18) = 1023.79 Model 6.15325454 2 3.07662727 Prob > F = 0.0000 Residual .054092643 18 .003005147 R-squared = 0.9913 Adj R-squared = 0.9903 Total 6.20734719 20 .310367359 Root MSE = .05482 lnY Coef. Std. Err. t P>|t| [95% Conf. Interval] lnX .9837083 .1342437 7.33 0.000 .7016728 1.265744 lnYt1 .1866692 .1068091 1.75 0.098 -.0377283 .4110668 _cons -1.134494 .2164561 -5.24 0.000 -1.589251 -.6797361
（3）以X*代表理想的销售量，用X和Xt-1表示，带入Y方程中，重新估计模型Y。通过整理得到β0，β1。（结果略）
g Xt1=X[_n-1] reg Y X Xt1
Source SS df MS Number of obs = 21 F( 2, 18) = 614.92 Model 51955.8336 2 25977.9168 Prob > F = 0.0000 Residual 760.425036 18 42.2458353 R-squared = 0.9856 Adj R-squared = 0.9840 Total 52716.2586 20 2635.81293 Root MSE = 6.4997 Y Coef. Std. Err. t P>|t| [95% Conf. Interval] X .4240542 .22186 1.91 0.072 -.0420564 .8901649 Xt1 .4196949 .2190631 1.92 0.071 -.0405395 .8799293 _cons -16.1012 4.505287 -3.57 0.002 -25.56645 -6.635942