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Nonlinear Programming Method for Dynamic Programming

Cai, Yongyang - 2013

the linear approach to dynamic programming by using ideas from approximation theory to avoid inefficient discretization …

Persistent link: https://www.econbiz.de/10012459628

Optimal Dynamic R&D Programs

Grossman, Gene M. - 1985

We study the optimal pattern of outlays for a single firm pursuing an R&D program over time. In the deterministic case, (a) the amount of progress required to complete the project is known, and (b) the relationship between outlays and progress is known. In this case, it is optimal to increase...

Persistent link: https://www.econbiz.de/10012477422

A Model of Stochastic Process Switching

Flood, Robert P. - 1981

In this paper we develop a rational expectations exchange rate model which is capable of confronting explicitly agents' beliefs about a future switch in exogenous driving processes. In our set-up the agents know with certainty both the initial exogenous process and the new process to be adopted...

Persistent link: https://www.econbiz.de/10012478497

Algorithms for Solving Dynamic Models with Occasionally Binding Constraints

Christiano, Lawrence J. - 1997

We describe and compare several algorithms for approximating the solution to a model in" which inequality constraints occasionally bind. Their performance is evaluated and compared" using various parameterizations of the one sector growth model with irreversible investment. We" develop...

Persistent link: https://www.econbiz.de/10012472605

A Conic Algorithm for the Group Minimization Problem

Simeone, Bruno - 1976

A new algorithm for the group minimization problem (GP) is proposed. The algorithm can be broadly described as follows …-known algorithm of Glover, and checked for non-negativity. The first non-negative point is an optimal solution of (GP). Advantages and … disadvantages of the algorithm are discussed; in particular, the implementation of the algorithm (which can be easily extended so as …

Persistent link: https://www.econbiz.de/10012478970

The Method of Endogenous Gridpoints for Solving Dynamic Stochastic Optimization Problems

Carroll, Christopher D. - 2005

This paper introduces a method for solving numerical dynamic stochastic optimization problems that avoids rootfinding operations. The idea is applicable to many microeconomic and macroeconomic problems, including life cycle, buffer-stock, and stochastic growth problems. Software is provided

Persistent link: https://www.econbiz.de/10012467286

A Nonlinear Certainty Equivalent Approximation Method for Dynamic Stochastic Problems

Cai, Yongyang - 2015

This paper introduces a nonlinear certainty equivalent approximation method for dynamic stochastic problems. We first use a novel, stable and efficient method for computing the optimal policy functions for deterministic dynamic optimization problems, and then use them as certainty-equivalent...

Persistent link: https://www.econbiz.de/10012457079

It's Fourth Down and What Does the Bellman Equation Say? A Dynamic Programming Analysis of Football Strategy

Romer, David - 2002

This paper uses play-by-play accounts of virtually all regular season National Football League games for 1998-2000 to analyze teams' choices on fourth down between trying for a first down and kicking. Dynamic programming is used to estimate the values of possessing the ball at different points...

Persistent link: https://www.econbiz.de/10012469689

Dynamic Optimization in Two-Party Models

McKibbin, Warwick J. - 1987

problem we develop a numerical dynamic programming algorithm to solve for optimal policies of each party taking into account …

Persistent link: https://www.econbiz.de/10012476856

Dynamic Programming on a Quantum Annealer : Solving the RBC Model

Fernández-Villaverde, Jesús; Hull, Isaiah J. - National Bureau of Economic Research - 2023

We introduce a novel approach to solving dynamic programming problems, such as those in many economic models, on a quantum annealer, a specialized device that performs combinatorial optimization. Quantum annealers attempt to solve an NP-hard problem by starting in a quantum superposition of all...

Persistent link: https://www.econbiz.de/10014322714