Inference
on Stochastic Time-varying coefficient models
Liudas Giraitis (Queen Mary)
4/03/11
Recently there has been considerable work on stochastic time-varying
coefficient models as vehicles for modelling structural change in the
macroeconomy with a focus on the estimation of the unobserved sample
path of time series of coefficient processes. The dominant estimation
methods, in this context, are based on various filters, such as the
Kalman filter, that are applicable when the models are cast in state
space representations. This paper examines, in a rigorous manner,
alternative kernel based estimation approaches for such models in a
nonparametric framework and derives their basic properties. The use of
such estimation methods for stochastic time-varying coefficient models,
or any persistent stochastic process for that matter, is novel and has
not been suggested previously in the literature. The proposed inference
methods have desirable properties such as consistency and asymptotic
normality and allow a tractable studentisation. In extensive Monte
Carlo and empirical studies, we fi nd that the methods exhibit very
good small sample properties and can shed light on important empirical
issues such as the evolution of inflation persistence and the PPP
hypothesis.
Joint work with G. Kapetanios and T. Yates
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