Modelos para previsão de receitas tributárias: o ICMS do Estado do Espírito Santo
The main objective of this dissertation is the research of a formal model for the monthly forecast of the Value Added Taxes on sales and services (ICMS) collected by the State of Espírito Santo, derived from the term series data analysis of the tax revenue from January 2000 to December 2009 and from...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | Brasil |
| Institución: | Universidade Federal do Espírito Santo (UFES) |
| Repositorio: | Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufes.br:10/5981 |
| Acceso en línea: | http://repositorio.ufes.br/handle/10/5981 |
| Access Level: | acceso abierto |
| Palabra clave: | Forecast Tax revenue Econometric model ICMS Previsão Arrecadação de Impostos Modelo Econométrico Imposto sobre circulação de mercadorias e serviços Impostos - Arrecadação Modelos econométricos Teoria Econômica 330 |
| Sumario: | The main objective of this dissertation is the research of a formal model for the monthly forecast of the Value Added Taxes on sales and services (ICMS) collected by the State of Espírito Santo, derived from the term series data analysis of the tax revenue from January 2000 to December 2009 and from the composition basis of the taxation incidence of the tax. The statistical characteristics of the ICMS series were identified and forecasts were drawn up with the use of Holt-Winters exponential smoothing models, of Box-Jenkins methodology, with intervention analysis for structural change detection and of a causal econometric model with dynamic structure. For the specification of the econometric model, the most relevant sectors of the economy that compose the ICMS tax basis and directly influence the tax revenue have been identified. It was chosen the mixed econometric model with multiple regression and only one behavioral equation.The predictive performance of the models was compared through the mean absolute percentage error (MAPE), in order to choose the one that shows the best estimate for the year 2010, used as basis of efficiency evaluation of the generated ex-post forecast. |
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