Package: aersn
Type: Package
Title: Affine-Equivariant Adjusted-Range Self-Normalization for
        Time-Series Inference
Version: 0.2.3
Authors@R: c(
    person("Yongmiao", "Hong", role = "aut"),
    person("Zhuo", "Lin", role = "aut"),
    person("Oliver", "Linton", role = "aut"),
    person(c("Whitney", "K."), "Newey", role = "aut"),
    person("Jiajing", "Sun", email = "jiajing.sun@gmail.com",
           role = c("aut", "cre")))
Description: 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>.
License: MIT + file LICENSE
URL:
        https://www.janeway.econ.cam.ac.uk/publication/affine-equivariant-adjusted-range-self-normalization
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: grDevices, graphics, sandwich (>= 3.0.0), lpSolve, stats,
        utils
Suggests: knitr, rmarkdown, testthat (>= 3.2.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
LazyData: true
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-10-07 22:18:04 UTC; root
Author: Yongmiao Hong [aut], Zhuo Lin [aut], Oliver Linton [aut],
        Whitney K. Newey [aut], Jiajing Sun [aut, cre]
Maintainer: Jiajing Sun <jiajing.sun@gmail.com>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-10-07 08:10:12 UTC
RemoteUrl: https://github.com/cran/aersn
RemoteRef: HEAD
RemoteSha: 5d961ed827a4181b43b8df0e00b39ca348bfae67
