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A Monte Carlo Study of Time Varyiing Coefficient (TVC) Estimation

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posted on 2019-08-19, 09:08 authored by Stephen G. Hall, Heather D. Gibson, G. S. Tavlas, Mike G. Tsionis
A number of recent papers have proposed a time-varying-coefficient (TVC) procedure that, in theory, yields consistent parameter estimates in the presence of measurement errors, omitted variables, incorrect functional forms, and simultaneity. The key element of the procedure is the selection of a set of driver variables. With an ideal driver set the procedure is both consistent and efficient. However, in practice it is not possible to know if a perfect driver set exists. We construct a number of Monte Carlo experiments to examine the performance of the methodology under (i) clearly-defined conditions and (ii) a range of model misspecifications. We also propose a new Bayesian search technique for the set of driver variables underlying the TVC methodology. Experiments are performed to allow for incorrectly specified functional form, omitted variables, measurement errors, unknown nonlinearity and endogeneity. In all cases except the last, the technique works well in reasonably small samples.

History

Citation

Computational Economics, 2018

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/School of Business

Version

  • VoR (Version of Record)

Published in

Computational Economics

Publisher

Springer (part of Springer Nature) for Society for Computational Economics

issn

0927-7099

Acceptance date

2018-12-06

Copyright date

2018

Available date

2019-08-19

Publisher version

https://link.springer.com/article/10.1007/s10614-018-9878-6

Language

en

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