Abstract
We examine the usefulness of various financial and real sector variables to forecast recessions in Brazil between one and eight quarters ahead. We estimate probabilistic models of recession and select models based on their outof-sample forecasts, using the Receiver Operating Characteristic (ROC) function. We find that the predictive out-of-sample ability of several models vary depending on the numbers of quarters ahead to forecast and on the number of regressors used in the model specification. The models selected seem to be relevant to give early warnings of recessions in Brazil.
Highlights
The most recent financial crises showed, once again, the relevance of forecasting the downturns of business cycles
Variables with potential predictive content are selected from a broad array of candidates and are examined by themselves and in some plausible and parsimonious combinations
Economic forecasting that allows for structural breaks and misspecified models has radically different implications from one that considers stationary and well-specified ones
Summary
The most recent financial crises showed, once again, the relevance of forecasting the downturns of business cycles. Economists in general did not anticipate the recessions that took place worldwide. Economies evolve over time and are subject, sometimes, to large unanticipated structural breaks. Such breaks can be precipitated by sudden changes in economic policy, major scientific and technological discoveries and innovations, political turmoil or permanent macroeconomic shocks. Economists often use complex mathematical models to forecast the path of the GDP and the likelihood of a recession (Bank of England, 2000; Hatch, 2001). The models used to understand and forecast processes as complicated as GDP are far from perfect representations of their behavior.
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