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This study proposes a data-based algorithm to select a subset of indicators from a large data set with a focus on forecasting recessions. The algorithm selects leading indicators of recessions based on the forecast encompassing principle and combines the forecasts. An application to U.S. data shows that forecasts obtained from the algorithm are consistently among the best in a large comparative forecasting exercise at various forecasting horizons. In addition, the selected indicators are reasonable and consistent with the standard leading indicators followed by many observers of business cycles. The suggested algorithm has several advantages, including wide applicability and objective variable selection.

Economic forecasting --- Financial crises --- Crashes, Financial --- Crises, Financial --- Financial crashes --- Financial panics --- Panics (Finance) --- Stock exchange crashes --- Stock market panics --- Crises --- Econometric models. --- Infrastructure --- Labor --- Macroeconomics --- Production and Operations Management --- Multiple or Simultaneous Equation Models --- Multiple Variables: General --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- Diffusion Processes --- State Space Models --- Model Evaluation and Selection --- Forecasting and Other Model Applications --- Business Fluctuations --- Cycles --- Prices, Business Fluctuations, and Cycles: Forecasting and Simulation --- Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data) --- Economic Development: Urban, Rural, Regional, and Transportation Analysis --- Housing --- Demand and Supply of Labor: General --- Macroeconomics: Production --- Economic growth --- Labour --- income economics --- Cyclical indicators --- Business cycles --- Labor markets --- Capacity utilization --- National accounts --- Production --- Saving and investment --- Labor market --- Industrial capacity --- United States