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Socioeconomic indicators have long been used by official statistical agencies to analyze and assess the current stage at which the economy stands via the application of linear filters used in conjunction with seasonal adjustment procedures. In this study, we propose a new set of symmetric and asymmetric weights that offer substantial gains in real-time by providing timely and more accurate information for detecting short-term trends with respect to filters commonly applied by statistical agencies. We compare the new filters to the classical ones through application to indicators of the US economy, which remains the linchpin of the global economic system. To assess the superiority of the proposed filters, we develop and evaluate explicit tests of the null hypothesis of no difference in revision accuracy of two competing filters. Furthermore, asymptotic and exact finite-sample tests are proposed and illustrated to assess if two compared filters have equal probabilities of failing to detect turning points at different time horizons after their occurrence.

Monitoring the direction of the short-term trend of economic indicators