causality
Perform Granger causality test to determine if X 'causes' y.
The Granger causality test is a statistical hypothesis test to determine if one time series is useful in forecasting another. While 'causality' in this context does not imply a cause-and-effect relationship in the philosophical sense, it does test whether changes in one variable are systematically followed by changes in another variable, suggesting a predictive relationship. By specifying a lag, you set the number of periods to look back in the time series to assess this relationship. This test is particularly useful in economic and financial data analysis, where understanding the lead-lag relationship between indicators can inform investment decisions and policy making.
Parameters
- standard
data: list[openbb_core.provider.abstract.data.Data]
Input dataset.
• Optional: False
y_column: str
Target column.
• Optional: False
x_column: str
Columns to use as exogenous variables.
• Optional: False
lag: int
Number of lags to use in the test.
• Default: 3
• Optional: True
Returns
results: list[Data]
Serializable results.
provider: None
Provider name.
warnings: Optional[list[Warning_]]
list of warnings.
chart: Optional[Chart]
Chart object.
extra: dict[str, Any]
Extra info.