One-node Quadrature Beats Monte Carlo: A Generalized Stochastic Simulation Algorithm / Kenneth Judd, Lilia Maliar, Serguei Maliar.

Format:

Book

Author/Creator:

Judd, Kenneth.

Contributor:

National Bureau of Economic Research.

Maliar, Lilia.

Maliar, Serguei.

Series:

Working Paper Series (National Bureau of Economic Research) no. w16708.

NBER working paper series no. w16708

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Other Title:

One-node Quadrature Beats Monte Carlo

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2011.

Summary:

In conventional stochastic simulation algorithms, Monte Carlo integration and curve fitting are merged together and implemented by means of regression. We perform a decomposition of the solution error and show that regression does a good job in curve fitting but a poor job in integration, which leads to low accuracy of solutions. We propose a generalized notion of stochastic simulation approach in which integration and curve fitting are separated. We specifically allow for the use of deterministic (quadrature and monomial) integration methods which are more accurate than the conventional Monte Carlo method. We achieve accuracy of solutions that is orders of magnitude higher than that of the conventional stochastic simulation algorithms.

Notes:

Print version record

January 2011.