Skip to main content Accessibility help

Fractionally Integrated Data and the Autodistributed Lag Model: Results from a Simulation Study

  • Justin Esarey (a1)


Two contributions in this issue, Grant and Lebo and Keele, Linn, and Webb, recommend using an ARFIMA model to diagnose the presence of and estimate the degree of fractional integration, then either (i) fractionally differencing the data before analysis or, (ii) for cointegrated variables, estimating a fractional error correction model. But Keele, Linn, and Webb also present evidence that ARFIMA models yield misleading indicators of the presence and degree of fractional integration in a series with fewer than 1000 observations. In a simulation study, I find evidence that the simple autodistributed lag model (ADL) or equivalent error correction model (ECM) can, without first testing or correcting for fractional integration, provide a useful estimate of the immediate and long-run effects of weakly exogenous variables in fractionally integrated (but stationary) data.



Hide All
Beck, Nathaniel, and Katz, Jonathan N. 1996. Nuisance vs. substance: Specifying and estimating time-series-cross-section models. Political Analysis 6(1): 136.
De Boef, Suzanna, and Keele, Luke. 2008. Taking time seriously. American Journal of Political Science 52:184200.
Enders, Walter. 2015. Applied Econometric Time Series, 4th edition. Hoboken, NJ: Wiley.
Esarey, Justin. 2016. Replication data for: Fractionally integrated data and the autodistributed lag model: Results from a simulation study., Harvard Dataverse, V1.
Grant, Taylor, and Lebo, Matthew. 2016. Error correction methods with political time series. Political Analysis 24:330.
Keele, Luke, Linn, Suzanna, and McLaughlin Webb, Clayton. 2016. Treating time with all due seriousness. Political Analysis 24:3141.
Shumway, Robert H., and Stoffer, David S. 2010. Time Series Analysis and Its Applications, 3rd edition. New York, NY: Springer.
MathJax is a JavaScript display engine for mathematics. For more information see

Related content

Powered by UNSILO

Fractionally Integrated Data and the Autodistributed Lag Model: Results from a Simulation Study

  • Justin Esarey (a1)


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.