ArfimaMLM-package: Arfima-MLM Estimation For Repeated Cross-Sectional Data And...

In pwkraft/ArfimaMLM: Arfima-MLM Estimation for Repeated Cross-Sectional Data

Arfima-MLM Estimation For Repeated Cross-Sectional Data And Pooled Cross-Sectional Time-Series Data

Description

This package provides functions to facilitate the estimation of Arfima-MLM models for repeated cross-sectional data and pooled cross-sectional time-series data (see Lebo and Weber 2015). The estimation procedure uses double filtering with Arfima methods to account for autocorrelation in longer repeated cross-sectional data followed by multilevel modeling (MLM) to estimate both aggregate- and individual-level parameters simultaneously.

Details

Package:	ArfimaMLM
Type:	Package
Version:	1.3.2
Date:	2015-06-23
License:	GPL-2

The main function of the package is arfimaMLM, which implements Arfima and multilevel models on a repeated cross-sectional dataset as described by Lebo and Weber (forthcoming). Furthermore, the function arfimaOLS uses the same initial procedures but estimates a simple linear model instead of the multilevel model. The package also includes arfimaPrep, which prepares a dataset for subsequent analyses according to the Arfima-MLM framework without estimating the final model itself. fd is a wrapper function to estimate the fractional differencing parameter using hurstSpec of the fractal-package as well as procedures provided by the fracdiff-package (via ML, GPH, and Sperio) and to differentiate the series accordingly (mainly for internal use in arfimaMLM,arfimaOLS, and arfimaPrep).

Author(s)

Patrick Kraft, with contributions from Christopher Weber and Taylor Grant

Maintainer: Patrick Kraft <[e-mail]>

References

Lebo, M. and Weber, C. 2015. “An Effective Approach to the Repeated Cross Sectional Design.” American Journal of Political Science 59(1): 242-258.

See Also

lme4, fracdiff, hurstSpec, arfimaMLM, arfimaOLS, arfimaPrep, fd

pwkraft/ArfimaMLM documentation built on March 29, 2022, 3:20 p.m.