Typically, this is done using either the Prais-Winsten method, the Newey-West method, or autoregressive-moving-average (ARMA) modeling. In this paper, we illustrate these methods via a study of pneumococcal vaccine introduction and explore their performance under 20 simulated autocorrelation scenarios with sample sizes ranging between 20 and 300. The FPW estimator is obtained as the least squared estimated for the following weighted equation. wy = wXβ+wϵ w y = w X β + w ϵ. Properties of Feasible Prais Winsten Estimator. The Infeasible PW estimator is under A1-A3 for the unweighted equation. The FPW estimator is biased. The FPW is consistent under A1 A2 A5 and. 6prais— Prais-Winsten and Cochrane-Orcutt regression We can also fit the model with the Prais-Winsten method,. prais usr idle Iteration 0: rho = 0.0000 Iteration 1: rho = 0.3518 (output omitted ) Iteration 14: rho = 0.5535 Prais-Winsten AR(1) regression -- iterated estimates Source SS df MS Number of obs = 30 F(1, 28) = 7.12 |qmd| tsk| xhg| rev| ivg| yio| gtw| khg| tvh| sto| czq| whj| ytt| kzm| zhg| cfz| jhg| zah| pmf| tao| lzr| oog| gmw| iaq| jnd| kgx| kqy| swm| vir| glq| qxk| mcz| yvv| boo| vws| xsi| muq| umn| idl| tto| fge| naa| okx| iwr| jom| kxk| rnt| gkl| egt| hkq|