- Main
Forecasting Inflation in Real Time
- Jia, Mingyuan
- Advisor(s): Chauvet, Marcelle
Abstract
This dissertation is intended to model the dynamics of inflation and forecast short-run
and long-run inflation using high frequency data. It first proposes a mixed-frequency
unobserved component model in which the common permanent and transitory inflation
components have time-varying stochastic volatilities. The key aspects of the model are its
flexibility to describe the changing inflation over time, and its ability to represent distinct
time series properties across price indices at mixed frequencies. More importantly, the
model is applied to builds short-run and long-run coincident indicators of US inflation at
the weekly frequency. The dynamics of the latent inflation factor shows that the persistence
of US inflation has reduced since 1990s due to different components over time. Next, it
proposes a nowcasting model for headline and core inflation of US CPI. The final selected
variables include daily energy price, commodity price, dollar index, weekly gas price,
money stock and monthly survey index. The model’s nowcasting accuracy improves as
information accumulates over the course of a month, and it easily outperforms a variety of
statistical benchmarks. Moreover, it uses a Nelson-Siegel Dynamic Factor model to fit the
monthly term structure of inflation expectation and describes its dynamics over time. The
extracted inflation factors correspond the level, slope and curvature of the term structure
of inflation expectation. It shows that a decomposition of the yield curve spread into
its expectation and risk premia components helps disentangle the channels that connect
fluctuations in Treasury rates and the future state of the economy. In particular, a change
in the yield curve slope due to expected real interest path and inflation expectation path, is
associated with future industrial production growth and probability of recession.
This dissertation adds to the literature by building a mixed-frequency model that can
track inflation in real time and produce better nowcasting results than the existing method,
by fitting the inflation expectation with a dynamic factor model that can describe the
dynamics of the whole term structure and by proving the usefulness of both inflation
expectation slope and real yield spread in predicting future economic activity.
Main Content
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