L2. | -.0102 .1688987 -0.06 0.952 -.3412354 .3208353 |

_cons | .0157672 .0043746 3.60 0.000 .0071932 .0243412 -------------+---------------------------------------------------------------- dln_consump | dln_inv |

L1. | -.002423 .0256763 -0.09 0.925 -.0527476 .0479016 L2. | .0338806 .0255638 1.33 0.185 -.0162235 .0839847 | dln_inc |

L1. | .2248134 .1116778 2.01 0.044 .005929 .4436978 L2. | .3549135 .1094069 3.24 0.001 .1404798 .5693471 | dln_consump |

L1. | -.2639695 .1359595 -1.94 0.052 -.5304451 .0025062 L2. | -.0222264 .1361204 -0.16 0.870 -.2890175 .2445646 |

_cons | .0129258 .0035256 3.67 0.000 .0060157 .0198358 ------------------------------------------------------------------------------

例子2：（包含外生变量的VAR模型）

. use http://www.stata-press.com/data/r11/lutkepohl2,clear

(Quarterly SA West German macro data, Bil DM, from Lutkepohl 1993 Table E.1)

. var dln_inv dln_inc , exog(dln_consump) lag(1/2) small

Vector autoregression

Sample: 1960q4 - 1982q4 No. of obs = 89 Log likelihood = 443.8226 AIC = -9.70388 FPE = 2.09e-07 HQIC = -9.568631 Det(Sigma_ml) = 1.60e-07 SBIC = -9.368333

Equation Parms RMSE R-sq F P > F ---------------------------------------------------------------- dln_inv 6 .042777 0.1553 3.271408 0.0096 dln_inc 6 .010022 0.3152 8.194719 0.0000 ----------------------------------------------------------------

------------------------------------------------------------------------------ | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- dln_inv | dln_inv |

L1. | -.2105219 .1014099 -2.08 0.041 -.4122222 -.0088217 L2. | -.1769163 .1041204 -1.70 0.093 -.3840076 .0301749 | dln_inc |

L1. | .5344701 .3849959 1.39 0.169 -.2312712 1.300211 L2. | .1769331 .3982704 0.44 0.658 -.6152107 .9690769

|

dln_consump | 1.207425 .4456638 2.71 0.008 .3210171 2.093832 _cons | -.0128275 .0115282 -1.11 0.269 -.0357567 .0101017 -------------+---------------------------------------------------------------- dln_inc | dln_inv |

L1. | .0674255 .0237579 2.84 0.006 .0201719 .1146791 L2. | .0286516 .0243929 1.17 0.244 -.019865 .0771681 | dln_inc |

L1. | -.0589614 .0901954 -0.65 0.515 -.2383564 .1204336 L2. | -.0702845 .0933053 -0.75 0.453 -.2558649 .115296 |

dln_consump | .5487083 .1044084 5.26 0.000 .3410441 .7563725 _cons | .0097651 .0027008 3.62 0.001 .0043934 .0151369 ------------------------------------------------------------------------------

例子3：（正交脉冲响应图形）

. use http://www.stata-press.com/data/r11/lutkepohl2,clear

(Quarterly SA West German macro data, Bil DM, from Lutkepohl 1993 Table E.1)

. varbasic dln_inv dln_inc dln_consump if qtr<=tq(1978q4)

Vector autoregression

Sample: 1960q4 - 1978q4 No. of obs = 73 Log likelihood = 606.307 AIC = -16.03581 FPE = 2.18e-11 HQIC = -15.77323 Det(Sigma_ml) = 1.23e-11 SBIC = -15.37691

Equation Parms RMSE R-sq chi2 P>chi2 ---------------------------------------------------------------- dln_inv 7 .046148 0.1286 10.76961 0.0958 dln_inc 7 .011719 0.1142 9.410683 0.1518 dln_consump 7 .009445 0.2513 24.50031 0.0004 ----------------------------------------------------------------

------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- dln_inv | dln_inv |

L1. | -.3196318 .1192898 -2.68 0.007 -.5534355 -.0858282 L2. | -.1605508 .118767 -1.35 0.176 -.39333 .0722283 | dln_inc |

L1. | .1459851 .5188451 0.28 0.778 -.8709326 1.162903 L2. | .1146009 .508295 0.23 0.822 -.881639 1.110841 | dln_consump |

L1. | .9612288 .6316557 1.52 0.128 -.2767936 2.199251 L2. | .9344001 .6324034 1.48 0.140 -.3050877 2.173888 |

_cons | -.0167221 .0163796 -1.02 0.307 -.0488257 .0153814 -------------+---------------------------------------------------------------- dln_inc | dln_inv |

L1. | .0439309 .0302933 1.45 0.147 -.0154427 .1033046 L2. | .0500302 .0301605 1.66 0.097 -.0090833 .1091437 | dln_inc |

L1. | -.1527311 .131759 -1.16 0.246 -.4109741 .1055118 L2. | .0191634 .1290799 0.15 0.882 -.2338285 .2721552 | dln_consump |

L1. | .2884992 .1604069 1.80 0.072 -.0258926 .6028909 L2. | -.0102 .1605968 -0.06 0.949 -.3249639 .3045639 |

_cons | .0157672 .0041596 3.79 0.000 .0076146 .0239198 -------------+---------------------------------------------------------------- dln_consump | dln_inv |

L1. | -.002423 .0244142 -0.10 0.921 -.050274 .045428 L2. | .0338806 .0243072 1.39 0.163 -.0137607 .0815219 | dln_inc |

L1. | .2248134 .1061884 2.12 0.034 .0166879 .4329389 L2. | .3549135 .1040292 3.41 0.001 .1510199 .558807 | dln_consump |

L1. | -.2639695 .1292766 -2.04 0.041 -.517347 -.010592 L2. | -.0222264 .1294296 -0.17 0.864 -.2759039 .231451 |

_cons | .0129258 .0033523 3.86 0.000 .0063554 .0194962 ------------------------------------------------------------------------------

varbasic, dln_consump, dln_consump.06.04.020-.02varbasic, dln_consump, dln_incvarbasic, dln_consump, dln_invvarbasic, dln_inc, dln_consump.06.04.020-.02varbasic, dln_inc, dln_incvarbasic, dln_inc, dln_invvarbasic, dln_inv, dln_consump.06.04.020-.02024680varbasic, dln_inv, dln_incvarbasic, dln_inv, dln_inv246802468step95% CIGraphs by irfname, impulse variable, and response variableorthogonalized irf . irf graph fevd,lstep(1)

varbasic, dln_consump, dln_consump1varbasic, dln_consump, dln_incvarbasic, dln_consump, dln_inv.50varbasic, dln_inc, dln_consump1varbasic, dln_inc, dln_incvarbasic, dln_inc, dln_inv.50varbasic, dln_inv, dln_consump1varbasic, dln_inv, dln_incvarbasic, dln_inv, dln_inv.50024680246802468step95% CIfraction of mse due to impulseGraphs by irfname, impulse variable, and response variable

例子4：脉冲响应图形（未正交化）

. use http://www.stata-press.com/data/r11/lutkepohl2,clear

(Quarterly SA West German macro data, Bil DM, from Lutkepohl 1993 Table E.1)

. varbasic dln_inv dln_inc dln_consump , irf