TY - JOUR
AU - Judd,Kenneth
AU - Maliar,Lilia
AU - Maliar,Serguei
TI - One-node Quadrature Beats Monte Carlo: A Generalized Stochastic Simulation Algorithm
JF - National Bureau of Economic Research Working Paper Series
VL - No. 16708
PY - 2011
Y2 - January 2011
DO - 10.3386/w16708
UR - http://www.nber.org/papers/w16708
L1 - http://www.nber.org/papers/w16708.pdf
N1 - Author contact info:
Kenneth L. Judd
Hoover Institution
Stanford University
Stanford, CA 94305-6010
Tel: 650/723-5866
Fax: 650/723-1687
E-Mail: JUDD@HOOVER.STANFORD.EDU
Lilia Maliar
Office T-24 Hoover Institution
Stanford University
CA 94305-6010, USA
Tel: 6507253416
Fax: 6507231687
E-Mail: maliarl@stanford.edu
Serguei Maliar
Office T-24 Hoover Institution
Stanford University
CA 94305-6010, USA
Tel: 6507253416
Fax: 6507231687
E-Mail: maliars@stanford.edu
AB - 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.
ER -