r - ARIMA modeling with panel data

Question

I am doing a fixed effects regression and am having a problem with autocorrelation, to deal with this I am doing ARIMA modeling using the forecast, lmtest, and plm packages. My data is general panel data, looks like this, I am trying to do some ARIMA modeling but am having a hard time incorporating autoregressive terms and moving averages into a fixed effects regression using the plm package. Here is my attempt.

world_hour_fix = 
    plm(WBGDPhour ~ broadband + resourcerents + education, 
        data = hourframe, model = "within")

auto.arima(world_hour_fix$residuals)

# Series: world_hour_fix$residuals 
# ARIMA(1,0,1) with zero mean     
# 
#     Coefficients:
#       ar1     ma1
#       0.403  0.3135
# s.e.  0.138  0.1586
# 
# sigma^2 estimated as 0.4901:  log likelihood=-175.54
# AIC=357.09   AICc=357.23   BIC=366.4

auto.arima(world_fix$residuals)

My question is: how do I incorporate one autoregressive term and a moving average of one into my regression?

Answer 1

在经济学方面，我们通常不会尝试使用面板数据进行 ARIMA 建模。相反，我们使用（准）差中差估计。如果您不担心非平稳性（听起来您并不担心），那么Bertrand、Duflo 和 Mullainathan 撰写的这篇论文“我们应该在多大程度上相信差异中的差异估计？”，比较了不同的方法考虑到面板数据的自相关。他们发现块引导程序和 HAC 标准错误往往运作良好。