Fit linear regression model with AR(1) errors , for references on Cochrane Orcutt model: See https://onlinecourses.science.psu.edu/stat501/node/357 See : Applied Linear Statistical Models - Fifth Edition - Michael H.
Fit linear regression model with AR(1) errors , for references on Cochrane Orcutt model: See https://onlinecourses.science.psu.edu/stat501/node/357 See : Applied Linear Statistical Models - Fifth Edition - Michael H. Kutner , page 492 The method assumes the time series to have the following model
Y_t = B.X_t + e_t e_t = rho*e_t-1+w_t e_t has autoregressive structure , where w_t is iid ~ N(0,&sigma 2)
Outline of the method : 1) OLS Regression for Y (timeseries) over regressors (X) 2)Apply auto correlation test (Durbin-Watson test) over residuals , to test whether e_t still have auto-regressive structure 3)if test fails stop , else update update coefficients (B's) accordingly and go back to step 1)
: Vector of size N for time series data to create the model for
Matrix N X K for the timed values for K regressors over N time points
maximum number of iterations in iterative cochrane-orchutt estimation
instance of class RegressionARIMAModel
This model is basically a regression with ARIMA error structure see https://onlinecourses.science.psu.edu/stat510/node/53 https://www.otexts.org/fpp/9/1 http://robjhyndman.com/talks/RevolutionR/11-Dynamic-Regression.pdf The basic idea is that for usual regression models Y = B*X + e e should be IID ~ N(0,sigma2), but in time series problems, e tends to have time series characteristics.