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: 2026-09-26 02:20:18 UTC; sunjiajing
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: CRAN
Date/Publication: 2026-10-07 08:10:12 UTC
Built: R 4.5.3; ; 2026-10-07 14:18:44 UTC; windows
