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Abstract:
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Rational expectations models make stringent assumptions on the agent's
knowledge about the true model. This paper introduces a model in which the
rational agent realizes that using a given model involves approximation errors,
and adjusts behavior accordingly. If the researcher accounts for this empirical
rationality on part of the agent, the resulting empirical model assigns
likelihood to the data actually observed, unlike in the unmodified rational expectations
case. A Lucas (1978)-type asset pricing model which incorporates
empirical rationality is constructed and estimated using U.S. stock data. The
equilibrium asset pricing function is seriously affected by the existence of approximation
errors and the descriptive properties and normative implications
of the model are significantly improved. This suggests that investors do not
| and should not | ignore approximation errors.
Keywords: Approximation errors, model uncertainty, estimation of structural
models, rational expectations, asset pricing. |
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