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


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.


Package: ArfimaMLM
Type: Package
Version: 1.3
Date: 2015-01-20
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).


Patrick Kraft, with contributions from Christopher Weber

Maintainer: Patrick Kraft <>


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

comments powered by Disqus