sae2-package: Small Area Estimation: Time-Series Models.

In sae2: Small Area Estimation: Time-Series Models

Small Area Estimation: Time-Series Models.

Description

Time series are-level models for small area estimation. The package supplements the functionality of the package sae. Specifically, it includes EBLUP fitting of the original Rao-Yu model, which did not have a spatial component. It also offers a modified ("dynamic") version of the Rao-Yu model, replacing the assumption of stationarity. Both univariate and multivariate applications are supported. Of particular note is the allowance for covariance of the area-level sample estimates over time, as encountered in rotating panel designs such as the U.S. National Crime Victimization Survey or present in a time-series of 5-year estimates from the American Community Survey.

Details

Package:	sae2
Type:	Package
Version:	1.2-1
Date:	2023-08-22
License:	GPL-2

The package provides two primary functions, eblupRY and eblupDyn, to fit non-spatial time-series small area models to area-level data. The function mvrnormSeries provides simulated data under either model. Functions geo_ratios and vcovgen can assist in preparing the input.

Author(s)

Robert E. Fay, Mamadou S. Diallo

Maintainer: Robert E. Fay <[e-mail]>

References

- Fay, R.E. and Herriot, R.A. (1979). Estimation of income from small places: An application of James-Stein procedures to census data. Journal of the American Statistical Association 74, 269-277.

- Fay, R.E., Planty, M. and Diallo, M.S. (2013). Small area estimates from the National Crime Victimization Survey. Proceedings of the Joint Statistical Meetings. American Statistical Association, pp. 1544-1557.

- Rao, J.N.K. and Molina, I. (2015). Small Area Estimation, 2nd ed. Wiley, Hoboken, NJ.

- Rao, J.N.K. and Yu, M. (1994). Small area estimation by combining time series and cross-sectional data. Canadian Journal of Statistics 22, 511-528.

See Also

sae

sae2 documentation built on Aug. 23, 2023, 5:07 p.m.