DEMAND FOR TRUCKS IN BRAZIL: AN ECONOMETRIC APPROACH

• Autor(a) principal: Gonçalves, Carlos Aurélio Bustamante
• Data de Publicação: 2022
• Outros Autores: Manfredini-Oliveira, Antonio Carlos
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Análise Econômica (Online)
• Texto Completo: https://seer.ufrgs.br/index.php/AnaliseEconomica/article/view/87271
• topic: demanda veicular; mercado de caminhões; estudo econométrico de demanda; demanda de caminhões no Brasil; vendas de caminhões; demanda de veículos comerciais de carga; modelo econométrico de demanda de caminhões no Brasil; L62; E27; vehicular demand; truck market; econometric study of demand; truck demand in Brazil; truck sales; commercial cargo vehicles demand; econometric model of truck demand in Brazil; L62; E2

The demand for trucks in Brazil has historically been an unusual choice for econometric analysis, resulting in a scarcity of studies on this subject, inconsistent with the economic relevance of road freight transport and its historical dominance over any other means of transport in the country. As a consequence, there is no study published in Brazil exclusively addressing the demand for trucks. This paper addresses the demand for trucks in Brazil, making extensive use of secondary data in multiple linear regression models. Besides the choice of the subject itself, it innovates by including expectationalvariables to the analysis. In comparison to the surveyed studies on passenger car demand, further improvements were undertaken, as the split of GDP in sectors (agriculture, industry and services), the use of lagged variables, and a twenty three years long timeframe (from 1996 to 2018). Results show that variables typically adopted on automotive demand studies are also relevant to demand for trucks as price, credit and income (represented by three components of GDP). Expectational variables (represented by GDP forecast, industry surveys and even Ipovespa performance) were a valid addition to the model. The results deepen the knowledge about the truck market behavior and allow an improvement of the quality of decision making in the truck industry.