Description
ar1wals computes the WALS estimates of a linear regression model with stationary AR(1) errors, using a feasible generalized least squares (FGLS) strategy. In the first step, we estimate the autocorrelation coefficient from the unrestricted model, using one of the six methods supported by the prais command. In the second step, we estimate a WALS regression based on the Cochrane-Orcutt or Prais-Winsten transformations of the outcome variable and the regressors.
Help files
After installation, you can view the estimation options by typing in Stata
help ar1wals
and the post-estimation options by typing
help ar1wals postestimation
Key references (in chronological order)
Magnus, J. R., Wan, A. T. K., and Zhang, X. (2011). Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market. Computational Statistics & Data Analysis, 55, 1331-1341.
De Luca, G., and Magnus, J. R. (2025c). Weighted-average least squares: Beyond the simple linear regression model. The Stata Journal, 25(4), 1-40.