Please refer to the previous article on using Co-integration test and Fully Modified Ordinary Least Squares (FMOLS) **here**.

The characteristics of time series data make them not suitable for OLS directly, as such, the variables must be tested for stationarity that is, make their mean and variance equal in case they are not. Usually, a variable that is trending tends to have its mean and variance not equal (non-stationary). As such, the Augmented Dickey-Fuller test (ADF) is used to test for stationarity and make the variables to be stationary.

It is based on the result of the stationarity test, that we will know which method of analysis to go for. The following constitute the methods of analysis based on the stationarity test:

- If all the variables are stationary at level, it means the mean and variance are equal without doing anything to them. In this case, the researcher can proceed to using the normal Ordinary Least Squares (OLS) to estimate the model and the result will be valid.
- If some of the variables are stationary at level I(0) and some are stationary at first difference I(1), then the researcher will have to proceed to using ARDL bounds test to estimate the model.
- If all the variables are stationary at first difference I(1), then Fully Modified Ordinary Least Square (FMOLS) is the appropriate method of analysis. You can download the PDF where FMOLS was explained
**here**.

This article therefore explains the step by step methods of using ARDL bounds test to estimate the model if some of the variables are stationary at level I(0) while some are stationary at first difference I(1).

Download the PDF file below:

Using ARDL Bounds Test for Time Series Data Analysis