While there is an extensive literature on identifying the asymptotic properties of adaptive learning algorithms, little is explicitly mentioned on how to actually implement these algorithms on the computer to analyze the quantitative effects of learning in dynamic macroeconomic models. The aim...

Two finite-difference methods are constructed and used for the solution of a class of endogenous growth model with physical and human capital. Although both the numerical methods to be developed are implicit by construction, each of the methods can be implemented explicitly. The first method is...

Recently, there has been many applications of perturbation methods for solving stochastic dynamic general equilibrium models. However, in standard applications of the perturbation method, the Taylor expansion is always computed around the deterministic steady state. Because of nonlinearities,...

This paper is concerned with the pricing of European continuous-installment options where the aim is to determine the initial premium given the installment payments schedule. The particular feature of this pricing problem is the determination, along with the initial premium, of an optimal...

We provide an overview of automatic differentiation (AD), a technique for the efficient computation of derivatives of functions defined in some programming language. We give a short explanation of how AD works, indicate the anticipated cost of derivatives computed using AD, and survey what AD...

This paper develops and illustrates the multi-step generalization of the standard single-step perturbation (SSP) method or MSP. In SSP, we can think of evaluating at x the computed approximate solution based on x0, as moving from x0 to x in "one big step" along the straight-line vector x-x0. By...

We present an algorithm and software routines for computing nth-order approximate solutions to dynamic, discrete-time rational expectations models around a nonstochastic steady state. We apply these routines to investigate the optimal monetary policy with commitment in an optimizing-agent model...