Optimal consumption under uncertainty, liquidity constraints, and bounded rationality

I study how boundedly rational agents can learn the solution to an infinite horizon optimal consumption problem under uncertainty and liquidity constraints. I present conditions for the existence of an optimal linear consumption rule and characterize it. Additionally, I use an empirically plausible theory of learning to generate a class of adaptive learning algorithms that converges to the optimal rule. This provides an adaptive and boundedly rational foundation to neoclassical consumption theory.