normal_dgps

Calculates density and higher order derivatives for Gaussian models.

internal

Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.

Xinwei Ma

lpdensity

Local Polynomial Density Estimation and Inference

normal_dgps function

<dl><dt>x</dt>
<dd>Scalar, point of evaluation.</dd>
<dt>v</dt>
<dd>Nonnegative integer, the derivative order (0 indicates cdf, 1 indicates pdf, etc.).</dd>
<dt>mean</dt>
<dd>Scalar, the mean of the normal distribution.</dd>
<dt>sd</dt>
<dd>Strictly positive scalar, the standard deviation of the normal distribution.</dd></dl>

Arguments

Internal function. — normal_dgps

<dl>

<dt>x</dt>
<dd>Scalar, point of evaluation.</dd>


<dt>v</dt>
<dd>Nonnegative integer, the derivative order (0 indicates cdf, 1 indicates pdf, etc.).</dd>


<dt>mean</dt>
<dd>Scalar, the mean of the normal distribution.</dd>


<dt>sd</dt>
<dd>Strictly positive scalar, the standard deviation of the normal distribution.</dd>

</dl>

normal_dgps: Internal function.

Description

Usage

Value

Arguments