This chapter provides an introduction to basic stochastic dynamic programming.
To avoid the use of measure theory in the main body of the text, I will first focus on economies in which stochastic variables take finitely many values. This will enable us to use Markov chains, instead of general Markov processes, to represent uncertainty.
Since many commonly-used stochastic processes, such as those based on normal or uniform distributions, fall outside this class, I will then indicate how the results can be generalized to situations in which stochastic variables can be represented by continuous, or mixture of continuous and discrete, random variables. Throughout my purpose is to provide a basic understanding of the tools of stochastic dynamic programming and how they can be used in dynamic macroeconomic models. For this reason, I will make a number of judicious choices rather than attempting to provide the most general results.16.1.
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