We introduce a computational technique- precomputation of integrals - that makes it possible to construct conditional expectation functions in dynamic stochastic models in the initial stage of a solution procedure. This technique is very general: it works for a broad class of approximating...

This paper develops a framework for analyzing the outcome of experiments carried out on forward-looking subjects. Natural experiments, unexpected policy changes, and true experiments are all included in the framework as special cases. These concepts are defined in conjunction with explicit...

The computational time required to solve and estimate dynamic economic models is one of the main constraints in empirical research. The Endogenous Grid Method (EGM) proposed by Carroll (2006) is known to offer impressive speed gains over more traditional stochastic dynamic programming methods,...

Facing an hypothetical, but increasingly, case of default risk on a mortgage or a fall in interest rates, an important issue raised by the borrower is the possibility of minimizing that risk by selecting the best refinancing option. In this paper, a mortgage refinancing model is presented,...

I introduce and evaluate a new stochastic simulation method for dynamic economic models. It is based on recent work in the operations research and engineering literatures (Van Roy et. al, 1997; Powell, 2007; Bertsekas, 2011). The baseline method involves rewriting the household's dynamic program in...

These notes provide an intuitive introduction to dynamic programming. The first two Sections, which can be skipped, present the standard deterministic Ramsey model using the Lagrangian approach. Section 3 reformulates the Ramsey problem by means of a Bellman equation, while Section 4 shows how...

Real options present a wide topic in investment litterature nowadays. However, despite bigadvances in the single asset investment pricing, the theory is miser of informations aboutproblems involving more than one asset. We show in this paper that using dynamic programming,one can find an...

We introduce the market resources method (MRM) for solving dynamic optimization problems. MRM extends Carroll’s (2006) endogenous grid point method (EGM) for problems with more than one control variable using policy function iteration. The MRM algorithm is simple to implement and provides...

In this paper, I propose a dynamic programming approach with value function iteration to solve Bellman equations in discrete time using spatially adaptive sparse grids. In doing so, I focus on Bellman equations used in finance, specifically to model dynamic portfolio choice over the life cycle....