Previsão e análise do ICMS na Paraíba

• Ano de defesa: 2021
• Autor(a) principal: Azevêdo, Melquisedec Anselmo da Costa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Modelagem Matemática e computacional; UFPB
• Palavras-chave em Português: ICMS; Holt-Winters; SARIMA; Imposto-Paraíba; Tax-Paraíba; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/23345

The search to anticipate the facts is quite common throughout the ages, considering something as likely based on clues, be they scientific or popular beliefs. In the economic context, forecasts are necessary to plan actions in advance and conclude on the main interventions and their likely consequences, because if the budget is overestimated, will lead to over-spending, which may lead to a deficit or contingencing, which is the temporary reduction of expenditure to reach the fiscal target and if resources are underestimated, which may hinder urgent and/or extremely important actions. In this way the present dissertation presents a modeling methodology, forecasting and analysing the collection of the Transaction Tax on the Movement of Goods and on the Provision of Interstate and Inter-municipal Transport and Communication Services of the State of Paraíba (ICMS-PB)for representing more than 80% of the State's tax revenue. Data were collected from January 1997 to April 2021, which is truncated into distinct dates generating four series to verify if the dynamics of the series vary. So it is using, for the four series, the Holt-Winters exponential smoothing algorithms with additive and multiplicative seasonality, and Box-Jenkins models with the integrated seasonal auto-regressive models of moving averages (SARIMA) and SARIMAX with the variable dummy referent with the dummy variable referring to the COVID-19 pandemic, trend and seasonality as regressive variables. Comparing them between themselves and with the real values of the ICMS of Paraíba. Finally, considering the mean quadratic error and total error obtained through the relationship between collections and forecasts, the models that generated the best forecasts for each series were selected, displaying the graph with the real values, the forecasts and the 95% confidence interval, verifyng the circumstances that the models best fit to predict the ICMS of Paraíba.