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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)
How do I incorporate one autoregressive term and a moving average of one into my regression?
688
asked Apr 28 '12 04:04
Potential cons of using ARIMA modelsComputationally expensive. Poorer performance for long term forecasts. Cannot be used for seasonal time series. Less explainable than exponential smoothing.
What is the Difference Between Time Series and Panel Data? Time series data is a dataset consist of observations of one individual at multiple time intervals. Panel data is a dataset consist of observations of multiple individuals obtained at multiple time intervals.
Autoregressive Integrated Moving Average, or ARIMA, is one of the most widely used forecasting methods for univariate time series data forecasting. Although the method can handle data with a trend, it does not support time series with a seasonal component.
Autoregressive integrated moving average (ARIMA) models predict future values based on past values. ARIMA makes use of lagged moving averages to smooth time series data. They are widely used in technical analysis to forecast future security prices.
I economics, we often don't try to do ARIMA modeling with panel data. Instead, we use (quasi-) difference-in-difference estimation. If you aren't worried about non-stationarity, which it sounds like you aren't, then this paper by Bertrand, Duflo, and Mullainathan, "How Much Should We Trust Differences-in-Differences Estimates?", compares different means of taking autocorrelation into account for panel data. They find that the block bootstrap and HAC standard errors tend to work well.
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