Title

phecdf -- Empirical CDF Estimation for Heterogeneous Panel Data

Syntax

phecdf panelvar [if] [in][, options]

Order
acov_order(#) set order of the autocovariance; default is 0.

acor_order(#) set order of the autocorrelation; default is 1.

boot(#) set number of bootstrap replication; default is 200.

Method
method(strings:string) string must be one of three estimation methods "naive", "hpj", "toj".

Graph
graph(strings:string) string must be a list consisting of mean, acov, acor; default is "mean acov acor".

ci(strings:string) string must be either "on" or "off"; default is "on".

panelvar must be xtset and strongly balanced.

Description

phecdf performs estimation of empirical distribution function when the panel data exhibits heterogeneity across its cross-sectional units.

Dependencies

phecdf requires the moremata
package. Type

. ssc describe moremata

Options

Order

acov_order non-negative integer k for the order of autocovariance. The default is 0.

acor_order positive integer k for the order of autocorrelation. The default is 1.

boot positive interger k for the number of bootstrap replication. The default is 200.

Method

method(strings:string) specifies how the empirical CDFs of moments are estimated.
"naive" stands for naive estimation without bias-correction, "hpj" for half panel jackknife and "toj" for third order jackknife.

Graph

graph(strings:string) specifies which graphs of empirical CDFs to be plotted.

ci(strings:string) specifies whether to present confidence intervals on graphs or not

Results

phecdf gives plots of empirical CDFs of mean, autocovariance and autocorrelation chosen by users.

Examples: empirical CDF estimation

Setup

. webuse pig

. xtset id week

Estimate the empirical CDFs of the variable weight about autocovariance of order 1 and autocorrelation of order 2 using naive estimation.

. phecdf weight, method("naive") acov_order(1) acor_order(2) ci("on") boot(300) graph("acov acor")

Reference

Ryo Okui. and Takahide Yanagi. 2019.
Panel Data Analysis with Heterogeneous Dynamics.
Journal of Econometrics.