Trend-cycle decomposition has been problematic in equilibrium business cycle research. Many models are fundamentally based on the concept of balanced growth, and so have clear predictions concerning the nature of the multivariate trend that should exist in the data if the model is correct. But the multivariate trend that is removed from the data in this literature is not the same one that is predicted by the model. This is understandable, because unexpected changes in trends are difficult to model under a rational expectations assumption. A learning assumption is more appropriate here. We include learning in a standard equilibrium business cycle model with explicit growth. We ask how the economy might react to the important trend-changing events of the postwar era in industrialized economies, such as the productivity slowdown, increased labor force participation by women, and the "new economy" of the 1990s. This tells us what the model says about the trend that should be taken out of the data before the business cycle analysis begins. Thus we use learning to address the trend-cycle decomposition problem that plagues equilibrium business cycle research. We argue that a model-consistent approach, such as the one we suggest here, is necessary if the goal is to obtain an accurate assessment of an equilibrium business cycle model.
- Federal Reserve Bank of Minneapolis. Research Department.
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