Abstract:
In this paper, we develop a classical moment-based inference framework with Bayesian properties. We prove that there exists an intensity distribution of the solutions to empirical moment conditions over the parameter space. We approximate it with the empirical saddlepoint (ESP) technique. We call the result the ESP intensity. A higher ESP intensity value indicates a higher estimated probability weight of being a solution to the empirical moment conditions. We propose to use the ESP intensity in the same way as posteriors are used in Bayesian inference to obtain point estimators, confidence regions, and tests. We call this the ESP approach, and elaborate the theory behind it. We prove consistency and asymptotic normality of the ESP intensity. The ESP approach provides a unique answer to multiple concerns especially acute in consumption-based asset pricing, such as lack of identification and multiple use of the same data set. It also sheds a new light on consumption-based asset pricing, and, in particular, indicates that consumption-based asset pricing theory is more consistent with data than existing inference approaches suggest.