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...

How wrong could policymakers be when using linearized solutions to their macroeconomic models instead of nonlinear global solutions? This question became of much practical interest during the Great Recession and the recent zero lower bound crisis. We assess the importance of nonlinearities in a...

We consider a class of infinite-horizon dynamic Markov economic models in which the parameters of utility function, production function, and transition equations change over time. In such models, the optimal value and decision functions are time-inhomogeneous: they depend not only on state but...

We introduce a numerical algorithm for solving dynamic economic models that merges stochastic simulation and projection approaches: we use simulation to approximate the ergodic measure of the solution, we cover the support of the constructed ergodic measure with a fixed grid, and we use...

There is a substantial amount of microeconomic evidence documenting diferential responses oflabor supply across productivity groups. In partic-ular, more productive individuals: (i) enjoy ahigher employment rate, (ii) have a lower volatility of employment and (iii) spend less time workingat...

This paper presents an algorithm for solving nonlinear dynamic stochastic models that computes value function by simulations. We argue that the proposed algorithm can be a useful alternative to the existing methods in some applications.

This paper studies a complete-market version of the neoclassical growth model, where agents face idiosyncratic shocks to earnings. We show that if agents possess identical preferences of either the CRRA or the addilog type, then the heterogeneous-agent economy behaves as if there was a...

Euler-equation methods for solving nonlinear dynamic models involve parameterizing some policy functions. We argue that in the typical macroeconomic model with valuable leisure, labor function is particularly convenient for parameterizing. This is because under the labor-function...

Parametrized Expectation Algorithm (PEA) is a powerful tool for solving nonlinear stochastic dynamic models. However, it has an important shortcoming: it is not a contraction mapping technique and thus, does not guarantee finding the solution. We suggest a simple modification that enhances the...