# 爱尔兰代写论文变量

| 4-8月-2013 | 英国留学常识

Autocorrelation can be either +ve or –ve. One of the popular way to remove autocorrelation is to use ARIMA. By setting variables AR and MA in ARIMA, we can deal with autocorrelation. If autocorrelation of regression model is small, then 1st order lag is created by setting AR =1 or MA =1 and if autocorrelation is high, then 2nd or some higher degree lag is created. Negative autocorrelation might arise because of over-differenced variables. Please note that AR =1 adds a lag dependent variable to the forecasting equation and MA =1 adds a lag forecast error.