Essays on Macroeconomics in Mixed Frequency Estimations

This dissertation asks whether frequency misspecification of a New Keynesian model

results in temporal aggregation bias of the Calvo parameter. First, when a

New Keynesian model is estimated at a quarterly frequency while the true

data generating process is the same but at a monthly frequency, the Calvo

parameter is upward biased and hence implies longer average price duration.

This suggests estimating a New Keynesian model at a monthly frequency may

yield different results. However, due to mixed frequency datasets in macro

time series recorded at quarterly and monthly intervals, an estimation

methodology is not straightforward. To accommodate mixed frequency datasets,

this paper proposes a data augmentation method borrowed from Bayesian

estimation literature by extending MCMC algorithm with

"Rao-Blackwellization" of the posterior density. Compared to two alternative

estimation methods in context of Bayesian estimation of DSGE models, this

augmentation method delivers lower root mean squared errors for parameters

of interest in New Keynesian model. Lastly, a medium scale New Keynesian

model is brought to the actual data, and the benchmark estimation, i.e. the

data augmentation method, finds that the average price duration implied by

the monthly model is 5 months while that by the quarterly model is 20.7

months.