| hpjecdf {panelhetero} | R Documentation |
The HPJ bias-corrected empirical CDF estimation
Description
The 'hpjecdf()' function enables to implement the HPJ bias-corrected estimation of the cumulative distribution function (CDF) of the heterogeneous mean, the heterogeneous autocovariance, and the heterogeneous autocorrelation. The method is developed by Okui and Yanagi (2019). For more details, see the package vignette with 'vignette("panelhetero")'.
Usage
hpjecdf(data, acov_order = 0, acor_order = 1, R = 1000, ci = TRUE)
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. |
R |
A positive integer of the number of bootstrap repetitions. Default is 1000. |
ci |
A logical whether to estimate the confidence interval. Default is TRUE. |
Value
A list that contains the following elements.
mean |
A plot of the corresponding CDF |
acov |
A plot of the corresponding CDF |
acor |
A plot of the corresponding CDF |
mean_func |
A function that returns the corresponding CDF |
acov_func |
A function that returns the corresponding CDF |
acor_func |
A function that returns the corresponding CDF |
mean_ci_func |
A function that returns the 95 percent confidence interval for the corresponding CDF |
acov_ci_func |
A function that returns the 95 percent confidence interval for the corresponding CDF |
acor_ci_func |
A function that returns the 95 percent confidence interval for the corresponding CDF |
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 |
R |
The number of bootstrap repetitions |
References
Okui, R. and Yanagi, T., 2019. Panel data analysis with heterogeneous dynamics. Journal of Econometrics, 212(2), pp.451-475.
Examples
data <- panelhetero::simulation(N = 300, S = 50)
panelhetero::hpjecdf(data = data, R = 50)