Linear Regression Model With Ar Errors Python

Is there a python package (statsmodels/scipy/pandas/etc...) with functionality for estimating coefficients for a linear regression model with autoregressive errors in python, such

Solution 1:

statsmodels http://www.statsmodels.org/dev/index.html has ARMA, ARIMA and SARIMAX models that take explanatory variables to model the mean. This corresponds to a linear model, y = X b + e, where the error term e follows an ARMA or seasonal ARMA process. AR errors are a special case when the moving average term has no lags.

statsmodels also has an autoregressive AR class but it does not allow for explanatory variables.

In these time series models, prediction is a conditional prediction that takes the history into account for forecasting.

statsmodels also has a GLSAR class which is a linear model that removes the effect of autocorrelated AR residuals. This uses feasible generalized least squares estimation and can only predict the unconditional term X b.

http://www.statsmodels.org/dev/generated/statsmodels.tsa.arima_model.ARMA.html#statsmodels.tsa.arima_model.ARMA

http://www.statsmodels.org/dev/statespace.html#seasonal-autoregressive-integrated-moving-average-with-exogenous-regressors-sarimax