EngleGrangerCoint

Validates co-integration in pairs of time series data using the Engle-Granger test and classifies them as Cointegrated’ or ‘Not cointegrated’.

Purpose: The intent of this Engle-Granger cointegration test is to explore and quantify the degree of co-movement between pairs of time series variables in a dataset. This is particularly useful in enhancing the accuracy of predictive regressions whenever the underlying variables are co-integrated, i.e., they move together over time.

Test Mechanism: The test first drops any non-applicable values from the input dataset and then iterates over each pair of variables to apply the Engle-Granger cointegration test. The test generates a ‘p’ value, which is then compared against a pre-specified threshold (0.05 by default). The pair is labeled as ‘Cointegrated’ if the ‘p value is less than or equal to the threshold or ’Not cointegrated’ otherwise. A summary table is returned by the metric showing cointegration results for each variable pair.

Signs of High Risk: - A high risk might be indicated if a significant number of variables that were hypothesized to be cointegrated do not pass the test. - Another sign of high risk is if a considerable number of ‘p’ values are close to the threshold. This is a risk because minor fluctuations in the data can switch the decision between ‘Cointegrated’ and ‘Not cointegrated’.

Strengths: - The Engle-Granger cointegration test provides an effective way to analyze relationships between time series, particularly in contexts where it’s essential to check if variables are moving together in a statistically significant manner. - It is useful in various domains, especially finance or economics. Here, predictive models often hinge on understanding how different variables move together over time.

Limitations: - The Engle-Granger cointegration test assumes that the time series are integrated of the same order, which isn’t always true in multivariate time series datasets. - The presence of non-stationary characteristics in the series or structural breaks can result in falsely positive or negative cointegration results. - The test may not perform well for small sample sizes due to lack of statistical power. Therefore, it should be used with caution, and whenever possible, supplemented with other predictive indicators for a more robust model evaluation.