datamsaeDB: Sample Data for Multivariate Small Area Estimation with...

In zazaperwira/msaeDB: Difference Benchmarking for Multivariate Small Area Estimation

Description

Dataset to simulate difference benchmarking of Multivariate Fay Herriot model

This data is generated base on multivariate Fay Herriot model by these following steps:

1. Generate explanatory variables X1 and X2. Take μX1 = μX1 = 10, σX11=1, σX22=2, and ρx= 1/2.
   Sampling error e is generated with the following σe11 = 0.15, σe22 = 0.25, σe33 = 0.35, and ρe = 1/2.
   For random effect u, we set σu11= 0.2, σu22= 0.6, and σu33= 1.8.
   For the weight we generate w1 w2 w3 by set the w1 ~ U(25,30) , w2 ~ U(25,30), w3 ~ U(25,30)
   Calculate direct estimation Y1 Y2 Y3 where Yi = Xβ+ui+ei

2. Then combine the direct estimations Y1 Y2 Y3, explanatory variables X1 X2, weights w1 w2 w3, and sampling varians covarians v1 v12 v13 v2 v23 v3 in a dataframe then named as datamsaeDB

Usage

datamsaeDB

Format

A data frame with 30 rows and 14 variables:

Y1

Direct Estimation of Y1

Y2

Direct Estimation of Y2

Y3

Direct Estimation of Y3

X1

Auxiliary variable of X1

X2

Auxiliary variable of X2

w1

Known proportion of units in small areas of Y1

w2

Known proportion of units in small areas of Y2

w3

Known proportion of units in small areas of Y3

v1

Sampling Variance of Y1

v12

Sampling Covariance of Y1 and Y2

v13

Sampling Covariance of Y1 and Y3

v2

Sampling Variance of Y2

v23

Sampling Covariance of Y2 and Y3

v3

Sampling Variance of Y3

zazaperwira/msaeDB documentation built on April 5, 2021, 6:26 p.m.