Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/1766
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dc.contributor.authorCreel, Michael-
dc.date.accessioned2007-10-31T12:23:23Z-
dc.date.available2007-10-31T12:23:23Z-
dc.date.issued2005-01-10-
dc.identifier.urihttp://hdl.handle.net/10261/1766-
dc.description.abstractThis paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of parallelization is done in a way such that an investigator may use the programs without any knowledge of parallel programming. A bootable CD that allows rapid creation of a cluster for parallel computing is introduced. Examples show that parallelization can lead to important reductions in computational time. Detailed discussion of how the Monte Carlo problem was parallelized is included as an example for learning to write parallel programs for Octave.en_US
dc.language.isoengen_US
dc.relation.ispartofseriesUFAE and IAE Working Papersen_US
dc.relation.ispartofseries637.05en_US
dc.rightsopenAccessen_US
dc.subjectParallel computingen_US
dc.subjectKernel regressionen_US
dc.subjectMonte Carloen_US
dc.subjectBootstrappingen_US
dc.subjectMaximum likelihooden_US
dc.subjectGMMen_US
dc.titleUser-Friendly Parallel Computations with Econometric Examplesen_US
dc.typedocumento de trabajoen_US
dc.type.coarhttp://purl.org/coar/resource_type/c_8042es_ES
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.openairetypedocumento de trabajo-
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