Abstract:Previous comparisons of ordinary least squares with Newey-West standard errors (OLS-NW) and Prais-Winsten (PW) regression in multiple-group interrupted time series analysis have been limited to first-order autoregressive (AR[1]) errors because PW estimation for higher-order AR[k] processes was previously unavailable. We conducted the first systematic evaluation of OLS-NW and PW under AR[2] and AR[3] error structures using Monte Carlo simulation. Simulations examined mild positive, oscillatory, and high persistent autocorrelation across varying series lengths and effect sizes. OLS-NW generally showed higher apparent power but substantially inflated Type I error and poor coverage, particularly under persistent autocorrelation, where inferential performance worsened with increasing AR order and series length. PW maintained substantially better inferential calibration across nearly all conditions. Both methods were approximately unbiased.
| Subjects: | Applications (stat.AP) |
| Cite as: | arXiv:2603.24814 [stat.AP] |
| (or arXiv:2603.24814v3 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2603.24814 arXiv-issued DOI via DataCite |
From: Ariel Linden [view email]
[v1]
Wed, 25 Mar 2026 20:52:35 UTC (2,386 KB)
[v2]
Sun, 26 Apr 2026 19:45:18 UTC (1,133 KB)
[v3]
Sat, 23 May 2026 22:17:17 UTC (7,335 KB)
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