Under the rational expectations (RE) hypothesis, the expectations of agents are consistent with and always confirmed by equilibrium outcomes. This hypothesis often significantly simplifies the analysis of complicated economic problems. However, the RE hypothesis is silent on how agents form their expectations and, hence, provides no guidance on selecting an equilibrium when multiple RE equilibria occur. In contrast, a learning model specifies the agent's learning rule for forming expectations

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... d is used to select one from multiple RE equilibria. The advantage of using the learning approach for equilibrium selection is noted by Evans and Honkapohja (2001) . 1 The standard criterion for equilibrium selection in the learning approach is stability of the learning dynamics. This consideration is quite intuitive: If a learning equilibrium is not stable, then it is unlikely to be the long-run equilibrium outcome. Examples of using stability of the learning dynamics to eliminate some RE equilibria include Lucas (1986), Marcet and Sargent (1989) , Woodford (1990), and Bullard and Mitra (2002) . Lucas (1986) argues that adaptive behavior of economic agents may narrow the set of equilibria in some economic models. By using the stability criterion under learning, Marcet Relative to rational expectations models, learning models provide a theory of expectation formation where agents use observed data and a learning rule. Given the possibility of multiple equilibria under rational expectations, the learning literature often uses stability as a criterion to select an equilibrium. This article uses a monetary economy to illustrate that equilibrium selection based on stability is sensitive to specifications of the learning rule. The stability criterion selects qualitatively different equilibria even when the differences in learning specifications are small. (JEL C60, D84)