panelhetero (version 1.0.1)

hpjkd: The HPJ bias-corrected kernel density estimation

Description

The `hpjkd()` function enables to implement the HPJ bias-corrected kernel density estimation for the heterogeneous mean, the autocovariance, and the autocorrelation. The method is developed by Okui and Yanagi (2020). For more details, see the package vignette with `vignette("panelhetero")`.

Usage

hpjkd(
  data,
  acov_order = 0,
  acor_order = 1,
  mean_bw = NULL,
  acov_bw = NULL,
  acor_bw = NULL
)

Value

A list that contains the following elements:

mean

A plot of the corresponding density

acov

A plot of the corresponding density

acor

A plot of the corresponding density

mean_func

A function that returns the corresponding density

acov_func

A function that returns the corresponding density

acor_func

A function that returns the corresponding density

bandwidth

A Vector of the bandwidths

quantity

A matrix of the estimated heterogeneous quantities

acov_order

The order of autocovariance

acor_order

The order of autocorrelation

N

The number of cross-sectional units

S

The length of time series

Arguments

data

A matrix of panel data. Each row corresponds to individual time series.

acov_order

A non-negative integer of the order of autocovariance. Default is 0.

acor_order

A positive integer of the order of autocorrelation. Default is 1.

mean_bw

A scalar of bandwidth used for the estimation of the denisty of mean. Default is NULL, and the plug-in bandwidth is used.

acov_bw

A scalar of bandwidth used for the estimation of the denisty of autocovariance. Default is NULL, and the plug-in bandwidth is used.

acor_bw

A scalar of bandwidth used for the estimation of the denisty of autocorrelation. Default is NULL, and the plug-in bandwidth is used.

References

Okui, R. and Yanagi, T., 2020. Kernel estimation for panel data with heterogeneous dynamics. The Econometrics Journal, 23(1), pp.156-175.

Examples

data <- panelhetero::simulation(N = 300, S = 50)
panelhetero::hpjkd(data = data)