Weak and Strong Stationarity 

The following was implemented in Maple by Marcus Davidsson (2009) davidsson_marcus@hotmail.com 

1) Introduction 

Note that statistics assumes that the variable examined is strong stationary (serial independent).

If the variable is not strong stationary in levels we can induce strong stationarity by either:
1) Taking the first difference (unit root)
2) Removing time trend (trend stationary)
3) Removing serial dependence (i.e. MA components in error term) 

Note that a week stationary variable is serial dependent which means that it violates the statistical assumption of zero autocovariance. 

I think most statistical text books does not do a good job communicating the difference between and weak and strong stationarity. 

1) Strong Stationarity B=0.1 

Weak Stationarity B=0.9 

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