Package: aersn 0.2.3

aersn: Affine-Equivariant Adjusted-Range Self-Normalization for Time-Series Inference

Tuning-free inference on fixed-dimensional parameters of dependent time series using affine-equivariant adjusted-range self-normalization. The centered partial-sum path of estimated influence contributions is normalized by its increment hull, the convex hull of all path increments. The gauge of the hull provides an asymptotically pivotal test statistic and an affine-equivariant confidence region without estimating the long-run covariance matrix, and its support function gives simultaneous confidence intervals for linear contrasts. For a single parameter the construction reduces exactly to adjusted-range self-normalization, whose limiting distribution is available in closed form. The Brownian reference law is simulated on a grid matched to the sample size or a supplied common variance-accumulation profile; inference for dependent observations remains asymptotic. Five further methods are provided for comparison on the same estimate and influence contributions: componentwise adjusted ranges after lag-zero partial prewhitening, quadratic self-normalization following Shao (2010) <doi:10.1111/j.1467-9868.2009.00737.x>, kernel long-run covariance estimation with automatic bandwidth selection following Andrews (1991) <doi:10.2307/2938229> and Newey and West (1994) <doi:10.2307/2297912>, Bartlett fixed-b inference following Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, and the equal-weighted cosine method of Lazarus, Lewis, Stock and Watson (2018) <doi:10.1080/07350015.2018.1506926>. Model interfaces are provided for sample means, linear regression, smooth generalized method of moments, and conditional likelihood scores; other estimators are handled through user-supplied influence contributions. The methods follow Hong, Lin, Linton, Newey and Sun (2026), Cambridge Working Papers in Economics No. 2678 <https://www.janeway.econ.cam.ac.uk/publication/affine-equivariant-adjusted-range-self-normalization> and, for the scalar case, Hong, Linton, McCabe, Sun and Wang (2024) <doi:10.1016/j.jeconom.2023.105603>.

Authors:Yongmiao Hong [aut], Zhuo Lin [aut], Oliver Linton [aut], Whitney K. Newey [aut], Jiajing Sun [aut, cre]