bandwidth_normal

This function calculates the bandwidth for kernel smoothing using the Normal Reference Rule. The rule is based on Silverman's rule of thumb, which selects the bandwidth as a function of the standard deviation and interquartile range (IQR) of the data. The bandwidth is computed as: \(h = 1.06 \times \min(\mathrm{sd}(A), \mathrm{IQR}(A) / 1.34) / N^{0.2}\), where \(\mathrm{sd}(A)\) is the standard deviation of <code>A</code>, \(\mathrm{IQR}(A)\) is the interquartile range and <code>N</code> is the length of <code>A</code>.

Instrumental variable (IV) estimators for homogeneous and
heterogeneous treatment effects with efficient machine learning instruments.
The estimators are based on double/debiased machine learning allowing for
nonlinear and potentially high-dimensional control variables. Details can
be found in Scheidegger, Guo and Bühlmann (2025) "Inference for
heterogeneous treatment effects with efficient instruments and machine
learning" <doi:10.48550/arXiv.2503.03530>.

Cyrill Scheidegger

IVDML

Double Machine Learning with Instrumental Variables and
Heterogeneity

bandwidth_normal function

<dl><dt>A</dt>
<dd>Numeric vector. The data for which the bandwidth is to be computed.</dd></dl>

Arguments

Compute Bandwidth Using the Normal Reference Rule — bandwidth_normal

<dl>

<dt>A</dt>
<dd>Numeric vector. The data for which the bandwidth is to be computed.</dd>

</dl>

bandwidth_normal: Compute Bandwidth Using the Normal Reference Rule

Description

Usage

Value

Arguments

References

Examples