PRAISK: Stata module to perform Iterated Prais-Winsten regression with AR(k) errors

Ariel Linden

Statistical Software Components from Boston College Department of Economics

Abstract: praisk fits a linear regression model with autoregressive errors AR(k) using the iterated Prais-Winsten (generalized least squares) estimator. For AR(k>1), praisk follows the methods described in Vougas (2021). The transformation uses exact initialization of the first k observations following Galbraith and Galbraith (1974) and Hamilton (1994). AR(k) parameters are estimated by solving the pooled Yule-Walker cross-product system jointly across all panels and segments, matching the behavior of official prais. After estimation, praisk displays a table of residual autocorrelations at lags 1 through k. The untransformed row shows autocorrelations of the OLS residuals (before AR filtering) and the transformed row shows autocorrelations of the AR(k) innovation residuals (after filtering). A successful transformation is indicated by transformed autocorrelations close to zero. For panel data, autocorrelations are computed using Stata's lag operator, which restricts all pairs to within-panel consecutive observations, matching the approach used by prais for the Durbin-Watson statistic.

Language: Stata
Requires: Stata version 14
Keywords: autoregressive errors; AR(p); time series (search for similar items in EconPapers)
Date: 2026-03-22
Note: This module should be installed from within Stata by typing "ssc install praisk". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/p/praisk.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/p/praisk_p.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/p/praisk.sthlp help file (text/plain)
http://fmwww.bc.edu/repec/bocode/p/praisk_methods.pdf documentation (application/pdf)

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