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dc.contributor.advisorQuintana-Díaz, Julio C.
dc.contributor.authorVillacencio-Mattos, John
dc.description.abstractEconomic cycles provide the best monthly economic indicators of a country, both on the long and short term scale. For this reason it is necessary that the cycles model the economy to the most optimal degree possible. This research project proposes a new model for the economic cycles in Puerto Rico. This model, based on the findings of our study, works better than the previous system because it eliminates the problems of multicollinearity and autocorrelation. This study does a comparative analysis of different techniques such as Discriminant Analysis and Logistic Regression to examine which of these methods better predicts economic cycles. According to the Discriminant Analysis method the variables “Motor Vehicle” and “Salary of the Manufacturer” are those that contribute significantly to discrimination of other groups, while with Logistic Regression we found that the variables “Motor Vehicle”, “Sale of Cement” and “Salary of the Manufacturer” were those that contributed to the discrimination. In conclusion, this study would seem to indicate that the Discriminant Analysis method is preferable to the other because of the fact that this technique results in a lower rate of poor classification.
dc.description.abstractLos ciclos económicos son los mejores indicadores económicos mensuales, tanto a corto y largo plazo para un País, por ello se requiere que estos expliquen la economía de manera óptima, es por ello que en este trabajo de investigación se plantea un nuevo modelo para estudiar los ciclos económicos de Puerto Rico, modelo que no presenta problemas de multicolinealidad y autocorrelación. En el presente trabajo se hizo análisis comparativo de las técnicas multivariadas como Análisis Discrimante y Regresión Logística para ver cuál de estos métodos predice mejor los Ciclos Económicos de Puerto Rico. Según el Análisis Discriminante las variables Vehículo de Motor y Nómina de Manufactura, son las que contribuyen de manera significativa en la discriminación de grupos, mientras que en la Regresión Logística las variables Vehículo de Motor, Venta de Cemento y Nómina de Manufactura, son las que contribuyen de manera significativa en la discriminación de grupos. El Análisis Discriminante resultó ser mejor ya que esta técnica arroja una menor tasa de mala clasificación.
dc.subjectDiscriminant analysisen_US
dc.subjectLogistic regressionen_US
dc.subjectMultivariate analysisen_US
dc.subject.lcshBusiness cycles -- Puerto Rico -- Mathematical modelsen_US
dc.subject.lcshMutivariate analysisen_US
dc.subject.lcshLogistic regression analysisen_US
dc.subject.lcshDiscriminant analysisen_US
dc.subject.lcshVariables (Mathematics)en_US
dc.titleCiclos económicos de Puerto Rico: uso de modelos y técnicas estadísticas multivariadasen_US
dc.rights.licenseAll rights reserveden_US
dc.rights.holder(c)2010 John Villacencio Mattosen_US
dc.contributor.committeeLorenzo González, Edgardo
dc.contributor.committeeAlameda, José
dc.contributor.representativeHunt, Shawn Statisticsen_US
dc.contributor.collegeCollege of Arts and Sciences - Sciencesen_US
dc.contributor.departmentDepartment of Mathematicsen_US

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