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.
Examples
from openbb import obb
# Perform Granger causality test to determine if X 'causes' y.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.econometrics.causality(data=stock_data, y_column="close", x_column="open")
# Example with mock data.
obb.econometrics.causality(y_column='close', x_column='open', lag=1, data=[{'date': '2023-01-02', 'open': 110.0, 'high': 120.0, 'low': 100.0, 'close': 115.0, 'volume': 10000.0}, {'date': '2023-01-03', 'open': 165.0, 'high': 180.0, 'low': 150.0, 'close': 172.5, 'volume': 15000.0}, {'date': '2023-01-04', 'open': 146.67, 'high': 160.0, 'low': 133.33, 'close': 153.33, 'volume': 13333.33}, {'date': '2023-01-05', 'open': 137.5, 'high': 150.0, 'low': 125.0, 'close': 143.75, 'volume': 12500.0}, {'date': '2023-01-06', 'open': 132.0, 'high': 144.0, 'low': 120.0, 'close': 138.0, 'volume': 12000.0}])
Parameters
- standard
| Name | Type | Description | Default | Optional |
|---|---|---|---|---|
| data | List[Data] | Input dataset. | False | |
| y_column | str | Target column. | False | |
| x_column | str | Columns to use as exogenous variables. | False | |
| lag | PositiveInt | Number of lags to use in the test. | False |
Returns
OBBject
Data