Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts

Business cycle analysis faces the challenge of high-dimensional databases with a time-irregular span. This study addresses these issues to estimate the Brazilian agricultural business cycle by proposing the use of an entropic test of relative information and the estimation of a generalized dynamic factor model in the context of a time-irregular span. In addition, we assess the co-movements between the estimated cycle and a wide range of weather, macroeconomic, and sectoral variables. The main results are: (i) the sharpest crises of the agricultural cycle are associated more with weather events than with Brazil’s economic crises, reinforcing that agriculture has a stabilizing role in the aggregated cycles; (ii) among all the variables analyzed, those related to weather (minimum and maximum temperature and precipitation) present the greatest commonalities with the agricultural cycle and are leading indicators; (iii) minimum temperatures and precipitation in states are essentially pro-cyclical, while maximum temperatures are pro-cyclical in typically colder states and counter-cyclical in warmer states; (iv) following the weather variables, credit variables had the highest average commonalities, with phases of expansion in the agricultural cycle leading to contractions in the indicators of arrears and defaults and in the interest rate of rural credit, with some time lag; (v) the behavior of the Brazilian economy and global demand for imports are also antecedent and pro-cyclical, but with relatively smaller commonalities; and (vi) expansions of the agricultural cycle lead to contractions in the unemployment rate of the economy. The contribution of this study is twofold: (1) its methodological innovation and (2) its application to the Brazilian agricultural business cycle, both of which are unprecedented.