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 chapter, 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.
KeywordsCurrent Account Model Uncertainty State Uncertainty Permanent Income Kalman Filter Equation
Luo thanks the Hong Kong GRF under grant No. 748209 and 749510 and HKU seed funding program for basic research for financial support. All errors are the responsibility of the authors. The views expressed here are the opinions of the authors only and do not necessarily represent those of the Federal Reserve Bank of Kansas City or the Federal Reserve System.
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