Libirary usage tutorial:
- Beta, Exponential Almon, and Hyperbolic scheme polynomial weighting methods.
- Lagged matrix generator function to project higher frequency data onto lower frequency.
- Flexible MIDAS ordinary least squares regressor model and results wrapper classes.
- Basic statisical summary methods like R^2 score and variable significance (t-test p-values).
- Improve efficiency of exogenous lagged projection generator function.
- Enable horizon to be set for each exogenous variable separately.
- Enable prelagged variables to be used for faster model fitting.
- More comprehensive statistical summary method simialr to statsmodels.api.OSL().fit().summary().
- Create a Flexible MIDAS logistic classifier model class.