At the intersection of urban politics, fiscal federalism, and political economy in the United States, probably the most important theoretical development in political science in the past 15 years has been the argument put forth by Paul Peterson in City Limits (1981). Informed by Tiebout (1956), Musgrave (1959), and Lowi (1964), Peterson uses the incidence of local government taxes and spending to develop an interest-driven theory of federalism and local politics. Peterson proposes a typology of local government expenditures, based on the degree to which the expenditures tend to be directed toward above-average or below-average taxpayers. Peterson assumes that expenditures of the former kind are beneficial for local economic well-being, while expenditures of the latter sort are, in general, harmful. These two kinds of expenditures he refers to as, respectively, “developmental” and “redistributive.” Expenditures of neutral incidence, and according to Peterson also of neutral economic consequence, are referred to as “allocational” (1981, 34-46).

This article describes a nonparametric unidimensional unfolding model for dichotomous data (van Schuur 1984) and shows how it can be extended to multicategory data such as Likert-type rating data. This extension is analogous to Molenaar's (1982) application of Mokken's (1970) nonparametric unidimensional cumulative scaling model. The model is illustrated with an analysis of five-point preference ratings given in 1980 to five political presidential candidates by Democratic and Republican party activists in Missouri.

Social scientists often employ measures that tap correlated factors. This article explores the effects of these complex measures on OLS estimators and specifies some of the conditions under which researchers should or should not include them in a regression.

The derivations allow us to shed some new light on the debate about sociotropic and pocketbook voting. In a classic piece, Kramer (1983) criticized inferences about economic interests that were based on cross-sectional data. He also argued that aggregate data could be used to assess the net effects of both sociotropic interest and self-interest on the vote. In this article, by using a complex judgment measure of sociotropic and self-interested economic perceptions and a redundant measure of self-interested perceptions, we are able to produce a consistent estimate of the sociotropic effect with aggregate data. An estimate of the self-interest effect is also available, though it may not have any desirable asymptotic properties.

The analysis of time-series data is fraught with problems of specification uncertainty and dynamic instability. Vector autoregression (VAR) is one attempt to overcome specification problems in time-series analysis, but this methodology has been criticized for being unparsimonious and potentially unstable through time.1 This article describes an important extension of VAR, one using Bayesian methods and allowing for time-varying parameters. These extensions improve VAR, making analysis less vulnerable to these criticisms. These VAR methods, developed by Doan, Litterman, and Sims (1984), provide a reasonable method for dealing with general time variation when theory does not provide useful a priori specification restrictions.

Beginning with Mueller's (1970) seminal work, researchers have wrestled with explanations of the movement of presidential approval over time. In his initial argument, Mueller states that

For political scientists who engage in longitudinal analyses, the question of how best to deal with nonstationary time-series is anything but settled. While many believe that little is lost when the focus of empirical models shifts from the nonstationary levels to the stationary changes of a series, others argue that such an approach erases any evidence of a long-term relationship among the variables of interest. But the pitfalls of working directly with integrated series are well known, and post-hoc corrections for serially correlated errors often seem inadequate. Compounding (or perhaps alleviating, if one believes in the power of selective perception) the difficult question of whether to difference a time-series is the fact that analysts have been forced to rely on subjective diagnoses of the stationarity of their data. Thus, even if one felt strongly about the superiority of one modeling approach over another, the procedure for determining whether that approach is even applicable can be frustrating.

At one time, the lag time for the implementation of methods from economics and other disciplines in political science was quite long, reflecting the newness of political methodology as well as a lack of statistical training. The articles by Ostrom and Smith and by Durr (in this volume) represent a departure from this longstanding lag time associated with political methodology. These articles, as well as others (e.g., Beck 1992), are using the methodology of unit root econometrics and error correction models with much smaller lag time. The Ostrom and Smith article represents little lag time at all, as some of their results are using methods not as yet printed in econometrics journals!

It is hardly surprising that I applaud the fine work of both Durr and Ostrom and Smith. I am on record in favor of the utility of the error correction model (e.g., Beck 1985) and it is impossible to obtain a visa to visit the economics department at UCSD without swearing an oath of loyalty to the methodology of cointegration. The two works here are notable for their methodological sophistication, their exposition of a relatively unknown and highly technical area, and, most important, their substantive contributions. Both articles show that political attitudes (approval and policy mood) adjust, in the long run, to changes in objective and subjective economic circumstance. Both articles are good examples of the synergy of methods and theory, since it is the methodology of cointegration that leads to this type of theorizing, and this type of theorizing can most easily be tested in the context of cointegration or error correction.

I am pleased to participate in this symposium because I agree that mutual criticism of our theories and of the methods used to test them helps to make social science objective and rational (Popper 1976). Space limitations preclude me from responding to all of the comments and suggestions of Beck and Williams (in this volume), but my interpretation of their key points is as follows: (1) estimating multiequation error correction models (ECMs) is unnecessary either because many theories provide us with exogeneity restrictions that imply single equation ECMs (Beck) or because statistical inference is unaffected by integration and cointegration in vector autoregressive systems (Williams); (2) using OLS in one step to estimate a single equation ECM is statistically superior to using the Engle-Granger two-step estimator (Beck); (3) commonly used classical hypothesis tests for nonstationarity favor the null hypothesis of a unit root, and therefore cannot be believed, but Bayesian inference with a flat prior solves this and other problems of inference (Williams); and (4) presidential approval is neither statistically nor conceptually an integrated random walk, but is either long memoried (Beck) or stationary (Williams). These are thought-provoking comments, and to one of them I will reply mea culpa. However, some of them need to be qualified, and others are incorrect. I begin by responding to comments (1) and (2) assuming that two or more time-series are integrated and cointegrated and then address comments (3) and (4), which question assumptions of and tests for integration and cointegration.

My aim in this note is not to rejoin the useful comments of Beck and Williams (in this volume) in this symposium, but rather to speculate about the future place of cointegration and error correction in the discipline of political science. Stopping somewhere short of the pejorative “hegemony” Williams anticipates for the ECM framework, I agree that this modeling approach is likely to enjoy wide play in a variety of substantive arenas. As such, I believe it is critical that analysts concerned with advancing cointegration methodology do so with an eye toward not just technical issues, but also questions regarding the theory of cointegration, error correction, and time-series stationarity. Furthermore, if methodological advancements are to be of any real value, they must be accessible to political scientists who, for inexplicable reasons, care not to make econometrics the center of their beings. In short, I am concerned that we will produce a method (1) dominated by questions of measurement and measurement strategy (at the expense of theory) and (2) inaccessible to (and therefore ignored by) most analysts. Such an evolution would be tantamount to losing the error correction forest for its trees.