| Using a number of data sets and identification assumptions, I present empirical evidence of how the US economy responds to a technology shock. My empirical evidence suggests that the recent literature's emphasize of a sharp drop in hours worked is misdirected. Because the sharp drop is not present in several of the data series examined here, the emphasis rather should be on a small (perhaps negative) initial response followed by a subsequent large positive response. Investment, consumption, and output also respond only with a delay.; These delayed responses present a challenge to a standard flexible-price macroeconomic model. In response to a positive technology shock, a standard flexible-price model would have an immediate increase in hours worked. Therefore, such a model is inconsistent with the empirical dynamic responses. I show, however, that a flexible-price model with habit persistence and certain kinds of capital adjustment costs can match the data.; One particular capital adjustment cost model that I consider is a time-to-plan model. In joint work with Lawrence Christiano, in the dissertation's third chapter, we show that a time-to-plan model does better than a standard flexible price model in explaining output-growth dynamics and the lead-lag relationship between business investment and output. In doing so, we illustrate the use of various frequency domain tools for estimating and testing dynamic, stochastic general equilibrium models. We show how to exploit the well-known fact that the log, Gaussian density function has a linear decomposition in the frequency domain. We also propose a new resolution to the problem that the phase angle between two variables is not uniquely determined. |
