statsmodels.tsa.vector_ar.var_model.VARProcess

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

Class represents a known VAR(p) process

Parameters
  • coefs (ndarray (p x k x k)) – coefficients for lags of endog, part or params reshaped

  • coefs_exog (ndarray) – parameters for trend and user provided exog

  • sigma_u (ndarray (k x k)) – residual covariance

  • names (sequence (length k)) –

  • _params_info (dict) – internal dict to provide information about the composition of params, specifically k_trend (trend order) and k_exog_user (the number of exog variables provided by the user). If it is None, then coefs_exog are assumed to be for the intercept and trend.

Returns

Return type

**Attributes**

Methods

acf([nlags])

Compute theoretical autocovariance function

acorr([nlags])

Compute theoretical autocorrelation function

forecast(y, steps[, exog_future])

Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y

forecast_cov(steps)

Compute theoretical forecast error variance matrices

forecast_interval(y, steps[, alpha, exog_future])

Construct forecast interval estimates assuming the y are Gaussian

get_eq_index(name)

Return integer position of requested equation name

intercept_longrun()

Long run intercept of stable VAR process

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()

Long run intercept of stable VAR 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, offset, seed])

Plot a simulation from the VAR(p) process for the desired number of steps

simulate_var([steps, offset, seed])

simulate the VAR(p) process for the desired number of steps

to_vecm()