SUITABILITY OF COINTEGRATION TESTS ON DATA STRUCTURE OF DIFFERENT ORDERS

Abstract

When selecting a method to evaluate theories about the relationship between two variables that have a
unit root or, it is necessary to consider the potential existence of cointegration. If the relationship exists
between the two variables, it should be able to forecast one variable based on the other, which is why
cointegration is significant for time series data including many variables. Using the three approaches, this
research investigates the cointegration processes and integration level. Determine whether the time series
is stationary and if there is a seasonal effect before looking at cointegration in a combination of variables.
A time series plot is used to monitor patterns and the time series data's behaviors. Applying the log
transformation and differencing approach will make the data stationar. The data was then subjected to the
Augmented Dickey Fuller (ADF) test, which verifies whether or not a unit -root exists by following a
unit-root procedure. In the event that the series lacks a unit root process, the data may be considered
stationary. The analysis techniques used in the research include the Granger Causality Test, Johansen test,
Phillips-Ouliarisco integration test, Engle–Granger two-step method, and simple correlation and
regression analysis. R statistical software was used for all of the analyses on a time series data set
containing these variables. In conclusion, the results of the three tests indicate cointegration, with the
Phillips–Ouliaris test being the most effective whether the sample size is small, medium, or big,
respectively, for both normal and gamma distributions. Engle–Granger and Johansen tests are then
optimal. Additionally, it was noted that as correlation confidence levels rose, so did the strength of the
determination of the cointegration across the correlation.