The `neecdf()` function enables to implement the naive 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")`.
neecdf(data, acov_order = 0, acor_order = 1, R = 1000, ci = TRUE)A list that contains the following elements.
A plot of the corresponding CDF
A plot of the corresponding CDF
A plot of the corresponding CDF
A function that returns the corresponding CDF
A function that returns the corresponding CDF
A function that returns the corresponding CDF
A function that returns the 95 percent confidence interval for the corresponding CDF
A function that returns the 95 percent confidence interval for the corresponding CDF
A function that returns the 95 percent confidence interval for the corresponding CDF
A matrix of the estimated heterogeneous quantities
The order of autocovariance
The order of autocorrelation
The number of cross-sectional units
The length of time series
The number of bootstrap repetitions
A matrix of panel data. Each row corresponds to individual time series.
A non-negative integer of the order of autocovariance. Default is 0.
A positive integer of the order of autocorrelation. Default is 1.
A positive integer of the number of bootstrap repetitions. Default is 1000.
A logical whether to estimate the confidence interval. Default is TRUE.
Okui, R. and Yanagi, T., 2019. Panel data analysis with heterogeneous dynamics. Journal of Econometrics, 212(2), pp.451-475.
data <- panelhetero::simulation(N = 300, S = 50)
panelhetero::neecdf(data = data, R = 50)
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