Comparação de previsões para a produção industrial brasileira considerando efeitos calendário e modelos agregados e desagregados

• Autor(a) principal: Nishida, Rodrigo
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
• Texto Completo: http://hdl.handle.net/10438/15633
• topic: Autometrics; Diebold-mariano; Model confidence set; Efeitos Calendário; Projeção; Agregação; Economia; Previsão econômica; Produtividade industrial; Modelos econométricos

The work aims to verify the existence and the relevance of Calendar Effects in industrial indicators. The analysis covers linear univariate models for the Brazilian monthly industrial production index and some of its components. Initially an in-sample analysis is conducted using state space structural models and Autometrics selection algorithm, which indicates statistically significance effect of most variables related to calendar. Then, using Diebold-Mariano (1995) procedure and Model Confidence Set, developed by Hansen, Lunde e Nason (2011), out-of-sample comparisons are realized between Autometrics derived models and a simple double difference device for a forecast horizon up to 24 months ahead. In general, forecasts of the Autometrics models that consider calendar variables are superior for 1-2 steps ahead and surpass the naive model in all horizons. The aggregation of the category of use components to form the general industry indicator shows evidence of a better perform in shorter term forecasts.