Class represents a known VAR(p) process
| Parameters: | coefs : ndarray (p x k x k) intercept : ndarray (length k) sigma_u : ndarray (k x k) names : sequence (length k) |
|---|---|
| Returns: | **Attributes:** : |
Methods
| acf([nlags]) | Compute theoretical autocovariance function |
| acorr([nlags]) | Compute theoretical autocorrelation function |
| forecast(y, steps) | Produce linear minimum MSE forecasts for desired number of steps |
| forecast_cov(steps) | Compute theoretical forecast error variance matrices |
| forecast_interval(y, steps[, alpha]) | Construct forecast interval estimates assuming the y are Gaussian |
| get_eq_index(name) | Return integer position of requested equation name |
| is_stable([verbose]) | Determine stability based on model coefficients |
| long_run_effects() | Compute long-run effect of unit impulse |
| ma_rep([maxn]) | Compute MA(\infty) coefficient matrices |
| mean() | Mean of stable process |
| mse(steps) | Compute theoretical forecast error variance matrices |
| orth_ma_rep([maxn, P]) | Compute Orthogonalized MA coefficient matrices using P matrix such that \Sigma_u = PP^\prime. |
| plot_acorr([nlags, linewidth]) | Plot theoretical autocorrelation function |
| plotsim([steps]) | Plot a simulation from the VAR(p) process for the desired number of |