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Fully sterilized intervention, an operation that involves a pure swap of foreign and domestic assets which leaves the money supply unchanged, gained it popularity through the experience among developed countries in the early 1980s. Ideally, it provides an independent policy tool that allows monetary authorities to pursue internal and external stabilization objectives simultaneously. In this paper, we employ a vector error correction model (VECM) to examine the dynamic interactions between exchange rate condition and policy reactions by Taiwanese monetary authorities, as Taiwan is ready to join the WTO and to help businesses take full advantages of e-commerce. We found that little efforts were made to sterilize interventions in the short term after an exchange rate shock occurred. The intention of not to sterilize interventions is to alleviate the exchange rate pressure by drastically changing domestic short term interest rates to force private sectors instantaneously adjust shares between domestic and foreign assets in their portfolios where there are speculative positions. Presumably, this official effort to avoid great fluctuations of exchange rates would offer businesses more freedoms in dealing with challenges in e-commerce environment. The result also suggests that, unlike the experience in developed countries, an independent monetary policy may not always be preferred by policy markers in small open economies.

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.

AbstractCitation indices, originally designed to measure influence, in practice are often repurposed by administrators and funding agencies as proxies for quality. Suppose there are more researchers in the mainstream than in the fringe, and it takes a higher quality to have the same influence across groups than within groups. Then, controlling for quality, a mainstream researcher may receive more citations than a fringe researcher. To correct for this mainstream bias, a citation index may need to be increasing in direct citations but decreasing in indirect citations.

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.]

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.