trend 表示检验中包含趋势项。如果不包含趋势项,可以使用 notrend。 假设上述命令的输出结果如下: text Dickey-Fuller = -2.512 MacKinnon approximate p-value for Z(t) = 0.012 Dickey-Fuller 是检验统计量(t统计量)。 MacKinnon approximate p-value 是p值。 在这个例子中,p值为0.012,小于常用的显著性水平0...
lrwindow(#) [constant trend bootstrap(#) westerlund noisily mg] 语法2:Kao 检验 xtcointtest kao y x1 x2 x3 下方汇报了 5 种不同的检验统计量,其对应的 p 值均小于 0.01,故可在 1% 水平上强烈拒绝 “不存在协整关系” 的原假设,认为存在协整关系。 语法3:Pedroni 检验 下面进行更为灵活的 Pedron...
As we are verifying balancing on multiple covariates, for this analysis we report both the standard p-values and the ones adjusted for the problem of multiple hypothesis testing using the step-down method proposed by Romano and Wolf (2005) and implemented, among others, by Heckman et al. (20...
stata面板数据单..该看下面4个p 值哪个?我自己想的是4个p 值都挺大的,我就把lags(1)变成lags(2) 或者lags(3),结果p值越来越大,等到lags(4)时,变成再下面这个图了?是啥意思啊?
. estat trendplots 结果为 在政策实施之前,控制医院和治疗医院是平行的。我们可以使用带有统计趋势的平行趋势检验来进一步评估这个假设。 . estat ptrends 结果为 我们没有足够的证据来拒绝平行趋势的零假设。这个检验和图形分析支持平行趋势假设。 我们可能想要进行的另一项检验是,看看在预期治疗中,控制组或治疗组是否...
The “Chi2(1) for trend” is slightly different. It’s 4.546 rather than 4.515. Well,ptrendis just using N rather than N − 1 in the formula: Qtrend = Chi2(1) for trend = N * ray2 Let’s go back to data arranged for thecorrcomputation and show this. ...
Notes: For lower one-sided test, c = #{T <= T(obs)} and p = p_lower = c/n. For upper one-sided test, c = #{T >= T(obs)} and p = p_upper = c/n. For two-sided test, p = 2*min(p_lower, p_upper); SE and CI approximate. Jonckheere–Terpstra test for trend Numb...
aedot: stata module to produce dot plot for adverse event data, Rachel Phillips,Suzie Cro aefdr: stata module to perform false discovery rate p-value adjustment for adverse event data, Rachel Phillips,Suzie Cro aevolcano: stata module to produce volcano plot for adverse event data, Rachel Phil...
MacKinnon approximate p-value for Z(t) = 0.2511 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 和预期一样,yrwd2的检验结果显示不能拒绝该过程为带漂移的随机游走过程的原假设。接下来,我们对yt序列也做类似的检验。 . dfuller yt, trend
(noclean_intensity) pfilter ln_noclean_intensity, method(hp) trend (ln_noclean_intensity_trend) smooth(400) loc l = "logtreg ln_noclean_intensity_trend" loc k = ", kq(0.333)" `l' `k' forv x=1/7{ `l' if region == `x' `k' } forv x=1/4{ `l' if income == `x' ...