Inference on stochastic time-varying coefficient models

L. Giraitis, George Kapetanios and Anthony Yates ()

Journal of Econometrics, 2014, vol. 179, issue 1, 46-65

Abstract: 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 paths of random 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 introduces a new class of autoregressive bounded processes that decompose a time series into a persistent random attractor, a time varying autoregressive component, and martingale difference errors. The paper examines, rigorously, alternative kernel based, nonparametric estimation approaches for such models and derives their basic properties. These estimators have long been studied in the context of deterministic structural change, but their use in the presence of stochastic time variation is novel. The proposed inference methods have desirable properties such as consistency and asymptotic normality and allow a tractable studentization. In extensive Monte Carlo and empirical studies, we find 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 purchasing power parity (PPP) hypothesis.

Keywords: Time-varying coefficient models; Random coefficient models; Nonparametric estimation; Kernel estimation; Autoregressive processes (search for similar items in EconPapers)
JEL-codes: C10 C14 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (71)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:179:y:2014:i:1:p:46-65

DOI: 10.1016/j.jeconom.2013.10.009

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