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Resumo Jacques Henrique Dias

This work is concerned to the several ways to econometrically approach the asymmetric price transmission channels, namely APT literature. This research approaches the problem that might occur when confronting contrasting methodologies elaborated in the literature to preview the pattern of price series, by the aftermath of them several conclusions can be drawn, which heads to misleading conclusions on APT. The conflicting assertions due to each chosen treatment on the variables is the prelude towards distinct statements on the APT issues. There is a necessity of comparing the results based on the same data source thoroughly assessed by the mostly seen models in the literature, by doing it so, we were able to push a step further on the econometrical differences between the methodologies applied in this literature and then comprehend how to avoid and to recognize above suspicion errors according to the faced output. Therefore, the objective is to comprehend the methodologies on APT using the prices in the Brazilian rice market taken from producers to consumers in Metropolitan Areas of Brazil from April 2004 to June 2016. It was expected that non-differentiated and non-perishable staple food might be less liable to APT, once its intrinsic characteristics provide their dealers to avoid less favorable conditions to negotiate their products; this work found exactly the opposite sense throughout the methodologies studied in this case. Results using the Vector Error Correction Model (VECM) indicated that there is some asymmetry, but it is not possible to infer that it remains to the long run. Previous methodologies based on OLS (Ordinary Least Squares) also showed that upward and downward price movements among these markets occurred in a different level and/or speed, the variables are significantly different for both rising and falling price phases, which demonstrates that the more sophisticated models can indeed forecast results accurately, shedding light on the output of prior models, specially models containing error terms.

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