| Summary: | There is growing acknowledgement that changes in expectations are an important cause of the business cycle. Business cycles are characterized by positive co-movements between consumption, investment, output and hours, yet changes in expectations cannot generate such positive co-movements in the most standard neo-classical business cycle model. If one is willing to entertain a richer production technology, it is possible to obtain the kind of fluctuations typical of business cycles that are caused by expectation revisions. This thesis analyzes systematically such a production technology, characterized by a nonlinear transformation curve between consumption and investment at the aggregate level, and evaluate some of its macroeconomic implications.
This thesis comprises three essays. The first essay empirically investigates if the proposed change in the production technology improves the capacity of neo-classical business cycle models to account for the behavior of the aggregate labor market. It finds that the proposed change is a partial improvement over standard models. The second essay shows that while a nonlinear transformation curve helps in obtaining an economic expansion following good news about future productivity gains, it can do so only if the intertemporal elasticity of substitution in consumption is high. To obtain an expansion in the more general case, one has to allow for a sufficiently high degree of complementarity between capital and labor in production. The third essays estimates a version of the model to analyze its business cycle properties. In the model, the nonlinear transformation curve arises because some resources need to be spent to distribute goods to their final use. There, it is found that the estimated model reproduces well the dynamics of output and investment but produces too much consumption volatility. Moreover, it suggests that news about future productivity changes are a more important source of economic fluctuation than actual changes in productivity. Finally, the estimated model produces distribution costs that are quite in line with the data.
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