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

By:

Judd, Kenneth

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w16708.Publication details: Cambridge, Mass. National Bureau of Economic Research 2011.Description: 1 online resource: illustrations (black and white)Subject(s):

C63 - Computational Techniques • Simulation Modeling

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Abstract: 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.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w16708 (Browse shelf(Opens below))	Not For Loan

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January 2011.

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.