误差修正模型的stata应用

误差修正模型的stata应用

误差修正模型:

如果用两个变量，人均消费y和人均收入x(从格林的数据获得)来研究误差修正模型。

令z=(y x)’，则模型为:

k

,z,A，,z，p,z，, ,t0t,1it,1ti,1

,,,,'其中，

如果令，即滞后项为1，则模型为 k,1

,z,A，,z，p,z，,t0t,11t,1t

实际上为两个方程的估计:

,y,a，by，bx，p,y，p,x，,ty11t,112t,111t,112t,11t

,x,a，by，bx，p,y，p,x，,tx21t,122t,121t,122t,12t

用ols命令做出的结果:

gen t=_n

tsset t

time variable: t, 1 to 204

gen ly=L.y

(1 missing value generated)

gen lx=L.x

(1 missing value generated)

reg D.y ly lx D.ly D.lx

Source | SS df MS Number of obs = 202 -------------+------------------------------ F( 4, 197) = 21.07

Model | 37251.2525 4 9312.81313 Prob > F = 0.0000

Residual | 87073.3154 197 441.996525 R-squared = 0.2996 -------------+------------------------------ Adj R-squared = 0.2854 Total | 124324.568 201 618.530189 Root MSE = 21.024

------------------------------------------------------------------------------

D.y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ly | .0417242 .0187553 2.22 0.027 .0047371 .0787112

lx | -.0318574 .0171217 -1.86 0.064 -.0656228 .001908

ly |

D1. | .1093189 .082368 1.33 0.186 -.0531173 .2717552

lx |

D1. | .0792758 .0566966 1.40 0.164 -.0325344 .1910861

_cons | 2.533504 3.757158 0.67 0.501 -4.875909 9.942916

,y,a，by，bx，p,y，p,x，,a这是的回归结果，其中=2.5335，

ty11t,112t,111t,112t,11ty

b=0.04172，b= -0.03186，p=0.10932，p=0.07928 11121112

同理可得的回归结果，见下 ,x,a，by，bx，p,y，

p,x，,tx21t,122t,121t,122t,12t

reg D.x ly lx D.ly D.lx

Source | SS df MS Number of obs = 202 -------------+------------------------------ F( 4, 197) = 11.18

Model | 36530.2795 4 9132.56988 Prob > F = 0.0000

Residual | 160879.676 197 816.648101 R-squared = 0.1850 -------------+------------------------------ Adj R-squared = 0.1685 Total | 197409.955 201 982.139082 Root MSE = 28.577

------------------------------------------------------------------------------

D.x | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ly | .037608 .0254937 1.48 0.142 -.0126676 .0878836

lx | -.0307729 .0232732 -1.32 0.188 -.0766694 .0151237

ly |

D1. | .4149475 .111961 3.71 0.000 .1941517 .6357434

lx |

D1. | -.1812014 .0770664 -2.35 0.020 -.3331825 -.0292203

_cons | 11.20186 5.10702 2.19 0.029 1.130419 21.27331

如果用vec 命令

vec y x, pi

Vector error-correction model

Sample: 3 - 204 No. of obs = 202

AIC = 18.29975 Log likelihood = -1839.275 HQIC = 18.35939

Det(Sigma_ml) = 277863.4 SBIC = 18.44715