具有自回归项的GLM可以校正序列相关性 [英] GLM with autoregressive term to correct for serial correlation

问题描述

我有一个固定的时间序列，我想使用自回归项来拟合线性模型以校正序列相关性，即使用公式At = c1 * Bt + c2 * Ct + ut，其中ut = r * ut -1 +等

I have a stationary time series to which I want to fit a linear model with an autoregressive term to correct for serial correlation, i.e. using the formula At = c1*Bt + c2*Ct + ut, where ut = r*ut-1 + et

(ut是一个AR(1)项，用于纠正错误项中的序列相关性)

(ut is an AR(1) term to correct for serial correlation in the error terms)

有人知道在R中使用什么来建模吗?

Does anyone know what to use in R to model this?

谢谢 卡尔

推荐答案

GLMMarp 软件包将适合这些模型.如果只想使用具有高斯误差的线性模型，则可以使用arima()函数(通过xreg参数指定协变量)来实现.

The GLMMarp package will fit these models. If you just want a linear model with Gaussian errors, you can do it with the arima() function where the covariates are specified via the xreg argument.

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