Missing data estimates related to soybean production in the state of Mato Grosso, Brazil, from 1990 to 2018

João Gabriel Ribeiro and Sônia Maria de Stefano Piedade

China Agricultural Economic Review, 2022, vol. 15, issue 1, 46-65

Abstract: Purpose - The state of Mato Grosso represents the largest producer and exporter of soybeans in Brazil; given this importance, it was aimed to propose to use the univariate imputation tool for time series, through applications of splines interpolations, in 46 of its municipalities that had missing data in the variables soybean production in thousand tons, production value and soy derivatives in R$ thousand, and also to assess the differences between the observed series and those with imputed values, in each of these municipalities, in these variables. Design/methodology/approach - The proposed methodology was based on the use of the univariate imputation method through the application of cubic spline interpolation in each of the 46 municipalities, for each of the 3 variables. Then, for each municipality, the original series were compared with each observed series plus the values imputed in these variables by the Quenouille test of correlation of time series. Findings - It was observed that, after imputation, all series were compared with those observed and are equal by the Queinouille test in the 46 municipalities analyzed, and the Wilcoxon test also showed equality for the accumulated total of the three variables involved with the production of soybeans. And there were increases of 5.92%, 3.58% and 2.84% for soy production, soy production value and soy derivatives value accumulated in the state after imputation in the 46 municipalities. Originality/value - The present research and its results facilitate the process of estimates and monitoring the total soy production in the state of Mato Grosso and its municipalities from 1990 to 2018.

Keywords: Comparison of estimates; Imputation; Production (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eme:caerpp:caer-01-2022-0014

DOI: 10.1108/CAER-01-2022-0014

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