Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332072
Title: Construction and forecasting performance of varying coefficient inflation models for the UK and China.
Author: Song, Haiyan.
Awarding Body: Glasgow Caledonian University
Current Institution: Glasgow Caledonian University
Date of Award: 1992
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Abstract:
Although various theoretical and applied papers have appeared in the field of Varying Coefficient (VC) modelling, little is available in the literature on inflation. The aim of this thesis is to evaluate the performance of the VC model and to fill the gap in applications of the VC approach to inflation processes both in the UK and China. Statistical analyses suggest that the inflation processes are unstable for both countries and the instabilities are mainly associated with major economic and institutional changes. The State Space (SS) model, in conjunction with the Kalman Filter algorithm, is then introduced to simulate the structural change of inflation models in the two different types of economies. The performance of a number of existing constant coefficient inflation models in the UK and that of their VC alternatives are compared and the advantages of the VC approach are revealed. A general VC inflation model for the UK, that nests both the Keynesian and monetarist ideas and a VC inflation model based on the excess demand hypothesis for China are developed. These VC inflation models are considered to be more accurate and robust in inflation forecasting because they take account of structural instability in the inflation generating processes. The empirical analysis gives results broadly consistent with the VC models developed. This confirms the author's initial assumption that the VC inflation specification will be superior to its alternatives.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.332072  DOI: Not available
Keywords: Econometrics Economics Mathematical statistics Operations research
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