Progress in the Study of Nonstationary Political Time Series: A Comment

Author:

Freeman John R.

Abstract

Cointegration was introduced to our discipline by Renée Smith and Charles Ostrom Jr. and by Robert Durr more than two decades ago at political methodology meetings in Washington University�St. Louis and Florida State University. Their articles, along with comments by Neal Beck and John T. Williams, were published in a symposium like this one in the fourth volume of Political Analysis. Keele, Lin, and Webb (2016; hereafter KLW) and Grant and Lebo (2016; hereafter GL) show how, in the years that followed, cointegration was further evaluated by political scientists, and the related idea of error correction subsequently was applied.Have the last twenty-plus years witnessed significant progress in modeling nonstationary political time series? In some respects, the answer is yes. The present symposium represents progress in understanding equation balance, analyzing bounded variables, and decomposing short- and longterm causal effects. In these respects KLW's and GL's articles deserve wide dissemination. But KLW and GL leave important methodological issues unresolved. They do not address some critical methodological challenges. From a historical perspective, the present symposium shows that we have made relatively little progress in modeling nonstationary political time series.

Publisher

Cambridge University Press (CUP)

Subject

Political Science and International Relations,Sociology and Political Science

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5. BK state that “whether the [panel] series are integrated or stationary but slowly moving, they may be well modeled by the [panel] EC[M] specification (Equation 8), which, as we have seen, is just an alternative parameterization of the [panel] ADL model.” They continue, “… if the series are integrated, either the EC[M] model (the series are said to be co-integrated) or the residuals will be highly correlated. Because our preferred methodology chooses specifications with almost uncorrelated residuals, it should never lead to choosing an incorrect EC[M] (or ADL)specification” (2011, 343–44). They then argue that since, as political economists, we usually use proportions, it is unlikely our series are integrated, economists have little or no theoretical justification for ECMs, and we know which variables are exogenous to others so there is no need to test for this aspect of our specifications. In a cryptic footnote (2011, fn. 6), BK say “Dickey–Fuller type distributions” can be used to test the statistical significance of (panel) ECM adjustment parameter. And, even though earlier in their article (2011, 338) they say most political economy data sets have 20–40 units and twenty annual observations, BK argue that “given the large n and T of TSCS data, in many cases it is clear that the EC[M] model is adequate or not, and if we incorrectly assume stationarity, consistent application of appropriate standard methods will indicate the problem.” It is not clear whether the “Dickey–Fuller type distributions” they refer to are those used in an Engle–Granger multiple-step procedure (to test for unit root residuals and which employ nonstandard distributions) or whether the tests they have in mind employ a panel ADL setup and hence a different nonstandard distribution (which uses critical values like those produced by Ericsson and McKinnon).

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