Date of Award
Doctor of Philosophy (PhD)
This dissertation consists of two chapters that study substantive and methodological issues in business cycle research.
In the first chapter, I study a business cycle model where agents learn about the state of the economy through accumulating capital. During recessions, agents invest less, and this generates noisier estimates of macroeconomic conditions and an increase in uncertainty. The endogenous increase in aggregate uncertainty further reduces economic activity, which in turn leads to more uncertainty, and so on. Thus, through changes in uncertainty, learning gives rise to a multiplier effect that amplifies business cycles. I calibrate the model to measure the size of this uncertainty multiplier and find that it is large. Moreover, the model quantitatively replicates the VAR relationship between output and uncertainty.
In the second chapter, I evaluate the common practice of estimating dynamic stochastic general equilibrium (DSGE) models using seasonally adjusted data. The simulation experiment shows that the practice leads to sizeable distortions in estimated parameters. This is because the effects of seasonality, which are magnified by the model's capital accumulation and labor market frictions, are not restricted to the so-called seasonal frequencies but instead are propagated across the entire frequency domain.
Saijo, Hikaru, "Essays on Dynamic Macroeconomics" (2013). Publicly Accessible Penn Dissertations. 690.