This paper investigates the situation in Merton (1969) model that volatility is a constant rather than stochastic process, then points out this misspecification since it doesn't match real market. Next, HJB equation with derived through control, thereby calibrate misspecification.

We propose a framework for estimation and inference when the model may be misspecified. rely on local asymptotic approach where degree of misspecification is indexed by sample size. construct estimators whose mean squared error minimax in neighborhood reference model, based one?step adjustments. In addition, we provide confidence intervals that contain true parameter under misspecification. As ...

The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from policy pi. To do this, we need model how pi relates R. In the current literature, most common models are optimality, Boltzmann rationality, and causal entropy maximisation. One primary motivations behind IRL human preferences behaviour. However, true relationship between behaviour much more complex than any cur...

Appropriate imputation inference requires both an unbiased imputation estimator and an unbiased variance estimator. The commonly used variance estimator, proposed by Rubin, can be biased when the imputation and analysis models are misspecified and/or incompatible. Robins and Wang proposed an alternative approach, which allows for such misspecification and incompatibility, but it is considerably...

We explore methods for confronting model misspeci cation in macroeconomics. We construct dynamic equilibria in which private agents and policy makers recognize that models are approximations. We explore two generalizations of rational expectations equilibria. In one of these equilibria, decision-makers use dynamic evolution equations that are imperfect statistical approximations, and in the oth...