| Abstract: | The Box-Jenkins approach to time series analysis, which is an efficient way of analyzing stationary time series, recommends differencing as a general method for transforming a nonstationary time series into a stationary one. This paper gives a methodological discussion of some other ways of transforming a nonstationary series, in particular removing linear trends. It is argued that in many cases removing trends is superior to differencing in several respects. For example, when the process generating the time series is an ARMA(p,q) process added to a linear trend, differencing will produce an ARMA(p,q + 1) process that violates the invertibility conditions and is therefore difficult to estimate. The discussion is extended to time series with seasonal patterns. |
