The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

aersn: affine-equivariant adjusted-range self-normalization

Version 0.2.3

aersn provides tests, confidence intervals and joint confidence regions for parameters estimated from dependent time series. Start with a sample mean, a fitted regression, or your own estimate and influence contributions. Use the same fitted object to compare adjusted-range inference with LDL partial prewhitening, Shao self-normalization, heteroskedasticity and autocorrelation consistent (HAC) covariance estimation, Bartlett fixed-b, and equal-weighted cosine (EWC) inference.

The main method, affine-equivariant adjusted-range self-normalization, constructs one joint region from the ranges of the centered influence path in all directions. Changing the units or applying a nonsingular linear transformation changes the region in the corresponding way. It needs no kernel or bandwidth and reduces to scalar adjusted-range inference when there is one parameter. The construction uses the convex hull of path increments; tests and simultaneous intervals use its gauge and support function. See Hong, Lin, Linton, Newey and Sun (2026), Cambridge Working Papers in Economics No. 2678 and Hong, Linton, McCabe, Sun and Wang (2024, Journal of Econometrics).

A supplied or consistently estimated common variance-accumulation profile can be used with the hull, LDL and Shao methods. For Shao, the profile option also changes the integration weights; the adjusted-range construction uses path ranges. These options require the common-profile conditions described in help("aersn_profile").

Installation

For a CRAN release, install with install.packages("aersn"). For a source archive supplied by the authors, use the following commands. Only lpSolve and sandwich are needed beyond the packages that ship with R.

install.packages(c("lpSolve", "sandwich"))
install.packages("/path/to/aersn_0.2.3.tar.gz", repos = NULL, type = "source")

The source tarball includes built tutorials. After installation, open them with vignette(package = "aersn"); no vignette-building step is needed. Only developers rebuilding tutorials from the repository need knitr and rmarkdown.

A minimal example

library(aersn)
set.seed(1)
n <- 300
e <- matrix(rnorm(2 * n), n, 2)
Y <- e
for (t in 2:n) Y[t, ] <- 0.5 * Y[t - 1, ] + e[t, ]   # bivariate AR(1)

fit <- aersn_mean(Y, names = c("m1", "m2"))          # psi_t = Y_t - Ybar
aersn_test(fit, null = c(0, 0))                      # increment-hull test
confint(fit)                                         # simultaneous intervals
aersn_contrast(fit, c(1, -1))                        # a linear contrast
plot(aersn_region(fit))                              # joint region

Six methods on one estimate

aersn_compare(fit, null = c(0, 0))

aersn_test(fit, method = "shao")
aersn_test(fit, method = "hac", kernel = "Parzen", bandwidth = "andrews")
aersn_test(fit, method = "fixedb", b = 0.5)
aersn_test(fit, method = "ewc", nu = 20)
confint(fit, method = "ldl")

Each result records the tuning values actually used, including bandwidths selected from the data and fixed-b fractions rounded to an integer lag bandwidth. Statistics and critical values are on different scales across methods and are not comparable as numbers; p-values, decisions and interval widths are. The vignette Comparing inference methods on one estimate works through this.

Estimators other than the mean

Supply an estimate and its observation-level influence contributions to aersn(), or use a model interface that computes them: aersn_lm() for least squares, aersn_gmm() for smooth generalized method of moments including instrumental variables, and aersn_mle() for conditional likelihood scores. For a parameter that is a function of a larger estimated vector, aersn_target() applies the Jacobian, so that jointly estimated nuisance coefficients keep their first-order effect. All interfaces feed the same core construction.

Documentation

Seven vignettes: scalar mean inference; multivariate mean inference and linear contrasts; supplied influence contributions; linear regression and smooth generalized method of moments; conditional likelihood scores and variance-accumulation profiles; reference distributions and reproducibility; and comparing inference methods. Use help(package = "aersn") for the function index and vignette(package = "aersn") for the installed tutorials.

What the methods assume

All six need the influence contributions to satisfy a functional central limit theorem with a nonsingular long-run covariance matrix, and the estimator to be asymptotically linear in them. The package cannot verify those conditions; each method’s help page states what else it needs. The componentwise method’s reference law requires the transformed long-run covariance to be diagonal in the limit, which diagonalizing the sample lag-zero covariance does not deliver, and its statistic is not affine equivariant. HAC inference relies on the conditions under which the estimate is consistent. Fixed-b and EWC reference laws are fixed-smoothing asymptotic laws, not exact finite-sample distributions. Strong persistence can cause finite-sample size distortion; a matched reference grid does not remove it.

Scope

Not included: the Kolmogorov-Smirnov type structural-break test of Hong et al. (2024) and the autocorrelation tests of Sun, Zhu and Linton (2025). The comparator methods here are not adjusted-range versions of those procedures.

License

MIT, held by the five authors. Maintainer: Jiajing Sun (jiajing.sun@gmail.com).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.