Model Uncertainty, State Uncertainty, and State-Space Models

State-space models have been increasingly used to study macroeconomic and financial problems. A state-space representation consists of two equations, a measurement equation which links the observed variables to unobserved state variables and a transition equation describing the dynamics of the state variables. In this paper, we show that a classic linear- quadratic macroeconomic framework which incorporates two new assumptions can be analytically solved and explicitly mapped to a state-space representation. The two assumptions we consider are the model uncertainty due to concerns for model misspecification (robustness) and the state uncertainty due to limited information constraints (rational inattention). We show that the state-space representation of the observable and unobservable can be used to quantify the key parameters on the degree of model uncertainty. We provide examples on how this framework can be used to study a range of interesting questions in macroeconomics and international economics