请教：如何用r语言做格兰杰因果关系检验

[New宽客]

怎么就是找不到这个主题的文章呢

[Cindy730]

vars包里用grangertest函数

[qq_43445388]

可以参考var()，使用其中的causality(x, cause = NULL, vcov.=NULL, boot=FALSE, boot.runs=100)

[唐更]

楼主直接下个vars程序包里面就都有了 ur.df(y, type = c("none", "drift", "trend"), lags = 1, selectlags = c("Fixed", "AIC", "BIC")) 单位根检验 ur.df(diff()) 差分后进行单位根检验 causality(x, cause = NULL, vcov.=NULL, boot=FALSE, boot.runs=100) 因果关系分析， 结果里有格兰杰因果检验 ——摘自经管之间

[ipchanwang]

@exia1zZ ## Which came first: the chicken or the egg? data(ChickEgg) ## chickens granger-cause eggs? grangertest(egg ~ chicken, order = 3, data = ChickEgg) ## eggs granger-cause chickens? grangertest(chicken ~ egg, order = 3, data = ChickEgg) 应该后面那个才是预期的格兰杰原因吧

[ipchanwang]

@exia1zZ a formula specification of a bivariate series like y ~ x. Currently, the methods for the generic function grangertest only perform tests for Granger causality in bivariate series. The test is simply a Wald test comparing the unrestricted model—in which y is explained by the lags (up to order order) of y and x—and the restricted model—in which y is only explained by the lags of y.

[exia1zZ]

@liuliu663 是不是说反了，GDP~consumption 结果p为0.221>0.05, 则GDP不是consumption的原因，~号前面的是预期的原因

[liuliu663]

lmtest包即可。 > library(lmtest) > grangertest(GDP ~ consumption, order = 2, data =ts.data) Granger causality test Model 1: GDP ~ Lags(GDP, 1:2) + Lags(consumption, 1:2) Model 2: GDP ~ Lags(GDP, 1:2) Res.Df Df F Pr(>F) 1 18 2 20 -2 1.6437 0.221 > grangertest(consumption ~ GDP, order = 2, data =ts.data) Granger causality test Model 1: consumption ~ Lags(consumption, 1:2) + Lags(GDP, 1:2) Model 2: consumption ~ Lags(consumption, 1:2) Res.Df Df F Pr(>F) 1 18 2 20 -2 13.411 0.0002717 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 检验结果表示，当原假设为“consumption不是引起GDP变化的Granger原因”，P值为0.221>0.05，我们无法拒绝原假设；而当原假设为“GDP不是引起consumption变化的Granger原因”时，P值为0.0002717<0.05，我们可以拒绝原假设。因此，可以证明：GDP是consumption的Granger原因。