This C++ code provides a tool for performing Granger causality analysis on multivariate statistical data. Granger causality is a statistical technique used to assess whether one time series variable can help predict another, which can provide valuable insights into potential causal relationships.
- Multivariate Granger Causality: Evaluate Granger causality for multiple time series variables simultaneously.
- Statistical Testing: Conduct statistical tests to determine the presence of predictive relationships between variables.
- Time Series Analysis: Handle time series data and assess temporal patterns to identify potential causal links.
- Causal Inference: While Granger causality assesses predictiveness rather than strict causation, the results can offer insights into causal relationships within your dataset.
- Clone this repository to your local machine.
- Compile the C++ code using your preferred compiler.
- Prepare your multivariate time series data as input (format and data requirements may vary depending on your implementation).
- Run the compiled executable with your data to perform Granger causality analysis.
This project is licensed under the MIT License.
Note: Granger causality analysis, as implemented in this code, can help identify predictive relationships in multivariate time series data, which can be a valuable step in causal inference. However, it is essential to interpret the results carefully and consider other evidence and methods when establishing causal relationships more rigorously.
For a deeper understanding of causal inference, you may want to explore additional causal inference techniques and approaches beyond Granger causality.