statsmodels.tsa.vector_ar.var_model.VARProcess¶

class statsmodels.tsa.vector_ar.var_model.VARProcess(coefs, intercept, sigma_u, names=None)[source]¶

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

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