Login Register

VARProcess.forecast_interval()

statsmodels.tsa.vector_ar.var_model.VARProcess.forecast_interval

VARProcess.forecast_interval(y, steps, alpha=0.05) [source]

Construct forecast interval estimates assuming the y are Gaussian

Returns:	(lower, mid, upper) : (ndarray, ndarray, ndarray)

Notes

Lutkepohl pp. 39-40

Links:

http://statsmodels.sourceforge.net/stable/generated/statsmodels.tsa.vector_ar.var_model.VARProcess.forecast_interval.html

doc_statsmodels

2017-01-18 16:21:42

Comments

Leave a Comment

Please login to continue.

Popular Articles

VARProcess.forecast() statsmodels.tsa.vector_ar.var_model.VARProcess.forecast VARProcess.forecast(y, steps) [source] Produce linear minimum MSE forecasts for desired nu

VARProcess.forecast_cov() statsmodels.tsa.vector_ar.var_model.VARProcess.forecast_cov VARProcess.forecast_cov(steps) Compute theoretical forecast error variance matrices Pa

VARResults.forecast_interval() statsmodels.tsa.vector_ar.var_model.VARResults.forecast_interval VARResults.forecast_interval(y, steps, alpha=0.05) Construct forecast interval es

VARProcess.mean() statsmodels.tsa.vector_ar.var_model.VARProcess.mean VARProcess.mean() [source] Mean of stable process Lutkepohl eq. 2.1.23

VARProcess.acorr() statsmodels.tsa.vector_ar.var_model.VARProcess.acorr VARProcess.acorr(nlags=None) [source] Compute theoretical autocorrelation function Returns: