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Equation Balance and Dynamic Political Modeling

Published online by Cambridge University Press:  04 January 2017

Matthew J. Lebo  and
Taylor Grant
Matthew J. Lebo*
Affiliation:
Department of Political Science, Stony Brook University, Stony Brook, NY 11794
Taylor Grant
Affiliation:
Department of Political Science, Stony Brook University, Stony Brook, NY 11794
*
e-mail: matthew.lebo@stonybrook.edu (corresponding author)
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Abstract

The papers in this symposium agree on several points. In this article, we sort through some remaining areas of disagreement and discuss some of the practical issues of time series modeling we think deserve further explanation. In particular, we have five points: (1) clarifying our stance on the general error correction model in light of the comments in this issue; (2) clarifying equation balance and discussing how bounded series affects our thinking about stationarity, balance, and modeling choices; (3) answering lingering questions about our Monte Carlo simulations and exploring potential problems in the inferences drawn from long-run multipliers; (4) reviewing and defending fractional integration methods in light of the questions raised in this symposium and elsewhere; and (5) providing a short practical guide to estimating a multivariate autoregressive fractionally integrated moving average model with or without an error correction term.

Type
Time Series Symposium
Information
Political Analysis , Volume 24 , Issue 1 , Winter 2016 , pp. 69 - 82
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: We are grateful to the Editors of Political Analysis for the opportunity to respond to the comments in this issue. We also thank Janet Box-Steffensmeier for her help throughout this project. Replication materials are available online as Lebo and Grant (2016).

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