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This paper uses the empirical implications of theoretical models (EITM) framework to examine the consequences of the asymmetric diffusion of expectations. In the spirit of the traditional two-step flow model of communication, less-informed agents learn the expectations of more-informed agents. We find that when there is misinterpretation in the information acquisition process, a boomerang effect exists. In this equilibrium the less-informed agents' forecasts confound those of more-informed agents. We apply the EITM approach to a key economic variable known to have a relation to economic fluctuations -- inflation expectations. Using surveyed inflation expectations data for the period, 1978-2000, we find the boomerang effect exists. One implication of this finding pertains to economic policy and economic volatility: because policymakers have more information than the public, the boomerang effect can lead policymakers to make inaccurate forecasts of economic conditions and conduct erroneous policies which contribute to economic instability. [Copyright Elsevier Ltd.]

Part of a symposium on the future direction of political science research begins by citing some problems with formal models, case studies, & applied statistical modeling despite the fruit they have borne. In this light, the initiative to link formal & empirical analysis -- empirical implications of theoretical models (EITM) -- is described. The lax manner in which researchers typically operationalize their concepts & establish causation is addressed in terms of the scientific & social impacts of common political science research practices. Examples of EITM-type research include party identification, policy making & the Phillips curve, strategic interaction in international relations, & regulatory policy delay. 1 Appendix, 45 References. J. Zendejas

This special issue is devoted to original articles that reflect recent progress in one of the most exciting developments in Political Science, the National Science Foundation's (NSF) initiative called Empirical Implications of Theoretical Models (EITM). This initiative reflects the ideas and hard work of the Political Science team there, Jim Granato and Frank Scioli, backed up by the contributions of an EITM panel that assembled at NSF in July 2001, some of whose observations we mention below. The challenge set by the EITM program is straightforward: to improve our theoretical work so that it yields more testable hypotheses and to improve our methodological work so that testing is made more effective and informative about theories. It is hard to object to this, but it also turns out to be hard to meet fully. The EITM initiative contains several components designed to close the gap between theoretical derivation and empirical test. This issue represents one component, presenting some of the most innovative work in the discipline on the current research frontier in EITM.

We provide a framework for the Empirical Implications of Theoretical Models (EITM) initiative. The objective of EITM is to encourage political and social scientists to test empirical models that are directly connected to a formal model. As scholars merge formal and empirical analysis they minimize non-falsifiable research practices and lay the foundation for social scientific cumulation. Our EITM framework involves three steps. The first step is for researchers to unify theoretical mechanisms and applied statistical concepts. Step two is to develop measurable devices ("analogues") for these mechanisms and concepts. The final step is to unify the analogues. The significance of the EITM framework is that it encourages scholars to use a set of plausible facts or axioms and then model them in a rigorous mathematical manner to identify causal relations that explain empirical regularities. Adapted from the source document.

Tension has long existed in the social sciences between quantitative and qualitative approaches on one hand, and theory-minded and empirical techniques on the other. The latter divide has grown sharper in the wake of new behavioural and experimental perspectives which draw on both sides of these modelling schemes. This book works to address this disconnect by establishing a framework for methodological unification: empirical implications of theoretical models (EITM). This framework connects behavioural and applied statistical concepts, develops analogues of these concepts, and links and evaluates these analogues. The authors offer detailed explanations of how these concepts may be framed, to assist researchers interested in incorporating EITM into their own research. They go on to demonstrate how EITM may be put into practice for a range of disciplines within the social sciences, including voting, party identification, social interaction, learning, conflict and cooperation to macro-policy formulation.

The Empirical Implications of Theoretical Models (EITM) initiative in political science aims to improve empirical inquiry by strengthening its theoretical foundations. We review the central features of the initiative, especially its emphasis on the development of empirically useful formal theory and the establishment of a direct link between theory and statistical estimation. We then illustrate the EITM method with an application to a public administration topic. Specifically, we develop a game-theoretic model of policy implementation in order to examine the role of leadership commitment to reform in a staff's decision to use performance information. We then discuss some of the empirical implications of the theoretical model and examine a couple of them by analyzing data from a 2007 Government Accountability Office survey of federal agency managers. Consistent with the theoretical model, the statistical analysis reveals that leadership commitment is not positively related to performance information use when one focuses on low-to-moderate levels of commitment. We conclude with a discussion of our example and the potential benefits of using the EITM method in public administration research. Adapted from the source document.

An important disconnect exists between the current use of formal modeling and applied statistical analysis. In general, a lack of linkage between the two can produce statistically significant parameters of ambiguous origin that, in turn, fail to assist in falsifying theories and hypotheses. To address this scientific challenge, a framework for unification is proposed. Methodological unification leverages the mutually reinforcing properties of formal and applied statistical analysis to produce greater transparency in relating theory to test. This framework for methodological unification, or what has been referred to as the empirical implications of theoretical models (EITM), includes (1) connecting behavioral (formal) and applied statistical concepts, (2) developing behavioral (formal) and applied statistical analogues of these concepts, and (3) linking and evaluating the behavioral (formal) and applied statistical analogues. The elements of this EITM framework are illustrated with examples from voting behavior, macroeconomic policy and outcomes, and political turnout.