| Abstract: |
In this paper, I evaluate the properties and performance of band spectral
estimators applied to business cycle models. Band spectral methods are widely
used to study frequency-dependentrelationships among time series. In business
cycle research, the Whittle likelihood approximation enables researchers to
estimate models using only the frequencies those models are best suited to
represent, such as the business cycle frequencies. Using the medium-scale
model of Angeletos et al. (2018) as a data-generating process, I conduct a
Monte Carlo study to assess the finite-sample properties of the band spectral
maximum likelihood estimator (MLE) and compare its performance with that of
the full-spectrum and exact time-domain MLEs. The results show that the band
spectral estimator exhibits considerable biases and efficiency losses for most
estimated parameters. Moreover, both the full-information and band spectral
Whittle estimators perform poorly in contrast to the time domain estimator,
which successfully recovers all model parameters. I demonstrate how these
findings can be understood through the theoretical properties of the
underlying model, and describe simple tools and diagnostics that can be used
to detect potential problems in band spectral estimation for a wide class of
macroeconomic models. |