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

Published online by Cambridge University Press:  04 January 2017

Justin Esarey
Justin Esarey*
Affiliation:
Rice University, e-mail: justin@justinesarey.com
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Abstract

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.

Type
Time Series Symposium
Information
Political Analysis , Volume 24 , Issue 1 , Winter 2016 , pp. 42 - 49
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

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References

Beck, Nathaniel, and Katz, Jonathan N. 1996. Nuisance vs. substance: Specifying and estimating time-series-cross-section models. Political Analysis 6(1): 1–36.Google Scholar
De Boef, Suzanna, and Keele, Luke. 2008. Taking time seriously. American Journal of Political Science 52:184–200.Google Scholar
Enders, Walter. 2015. Applied Econometric Time Series, 4th edition. Hoboken, NJ: Wiley.Google Scholar
Esarey, Justin. 2016. Replication data for: Fractionally integrated data and the autodistributed lag model: Results from a simulation study. http://dx.doi.org/10.7910/DVN/DH1IUI, Harvard Dataverse, V1.Google Scholar
Grant, Taylor, and Lebo, Matthew. 2016. Error correction methods with political time series. Political Analysis 24:3–30.Google Scholar
Keele, Luke, Linn, Suzanna, and McLaughlin Webb, Clayton. 2016. Treating time with all due seriousness. Political Analysis 24:31–41.Google Scholar
Shumway, Robert H., and Stoffer, David S. 2010. Time Series Analysis and Its Applications, 3rd edition. New York, NY: Springer.Google Scholar