statsmodels.tsa.vector_ar.var_model.VARResults.ma_rep VARResults.ma_rep(maxn=10) Compute MA() coefficient matrices Parameters: maxn : int Number of coefficient matrices to compute Returns: coefs : ndarray (maxn x k x k)

statsmodels.tsa.vector_ar.var_model.VARResults.long_run_effects VARResults.long_run_effects() Compute long-run effect of unit impulse

statsmodels.tsa.vector_ar.var_model.VARResults.is_stable VARResults.is_stable(verbose=False) Determine stability based on model coefficients Parameters: verbose : bool Print eigenvalues of the VAR(1) companion Notes Checks if det(I - Az) = 0 for any mod(z) <= 1, so all the eigenvalues of the companion matrix must lie outside the unit circle

statsmodels.tsa.vector_ar.var_model.VARResults.irf_resim VARResults.irf_resim(orth=False, repl=1000, T=10, seed=None, burn=100, cum=False) [source] Simulates impulse response function, returning an array of simulations. Used for Sims-Zha error band calculation. Parameters: orth: bool, default False : Compute orthoganalized impulse response error bands repl: int : number of Monte Carlo replications to perform T: int, default 10 : number of impulse response periods signif: float (0 <

statsmodels.tsa.vector_ar.var_model.VARResults.irf_errband_mc VARResults.irf_errband_mc(orth=False, repl=1000, T=10, signif=0.05, seed=None, burn=100, cum=False) [source] Compute Monte Carlo integrated error bands assuming normally distributed for impulse response functions Parameters: orth: bool, default False : Compute orthoganalized impulse response error bands repl: int : number of Monte Carlo replications to perform T: int, default 10 : number of impulse response periods signif:

statsmodels.tsa.vector_ar.var_model.VARResults.irf VARResults.irf(periods=10, var_decomp=None, var_order=None) [source] Analyze impulse responses to shocks in system Parameters: periods : int var_decomp : ndarray (k x k), lower triangular Must satisfy Omega = P P?, where P is the passed matrix. Defaults to Cholesky decomposition of Omega var_order : sequence Alternate variable order for Cholesky decomposition Returns: irf : IRAnalysis

statsmodels.tsa.vector_ar.var_model.VARResults.get_eq_index VARResults.get_eq_index(name) Return integer position of requested equation name

statsmodels.tsa.vector_ar.var_model.VARResults.forecast_cov VARResults.forecast_cov(steps=1) [source] Compute forecast covariance matrices for desired number of steps Parameters: steps : int Returns: covs : ndarray (steps x k x k) Notes Ref: Lutkepohl pp. 96-97

statsmodels.tsa.vector_ar.var_model.VARResults.forecast_interval VARResults.forecast_interval(y, steps, alpha=0.05) Construct forecast interval estimates assuming the y are Gaussian Returns: (lower, mid, upper) : (ndarray, ndarray, ndarray) Notes Lutkepohl pp. 39-40

statsmodels.tsa.vector_ar.var_model.VARResults.forecast VARResults.forecast(y, steps) Produce linear minimum MSE forecasts for desired number of steps ahead, using prior values y Parameters: y : ndarray (p x k) steps : int Returns: forecasts : ndarray (steps x neqs) Notes Lutkepohl pp 37-38