econometrics. what are the potential implications of including stationary variables as both dependant and independant variables...

econometrics. what are the potential implications of including stationary variables as both dependant and independant variables in a regression model. 4 marks

describing how it might affect forecasting.

describing the potential confusion causes by spurious regressions.

Expert Solution

If a time series is stationary,it's mean, variance and autocovariance (at lags ) remain the same no matter at what point we measure them;that is, they are time invariant.lf any of these conditions are violated, time series is non-stationary.ln other words non-stationary time series will have a time varying mean or time-varying variance or both.

Including non-stationary variables in the regression model will lead to the following consequences.

* if time series isn't stationary,we can study it's behaviour only for the time period under consideration.

* Each set of time series data will be for a purticular episode.

* It's not possible to generalize it to other time periods.Therefor the purpose of forecasting will be of little value .

* Another disadvantage is the emergence of spurious regression or non-sensical regression . It was discovered by Yule.

* Yule shouwed that spurious correlation could persist in non-stationary time series even if the sample is very large . If coefficient of determination is greater than d statistic , we should suspect the existence of spurious regression.

* Coefficient of determination as well as the t statistic from spurious regression will be misleading.

Rahul Sunny answered 2 years ago

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