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On the Estimation and Testing of Some Threshold Models

On the Estimation and Testing of Some Threshold Models


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About the Book

This dissertation, "On the Estimation and Testing of Some Threshold Models" by Xuan, Zhou, 周璇, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled ON THE ESTIMATION AND TESTING OF SOME THRESHOLD MODELS Submitted by ZHOU XUAN for the degree of Master of Philosophy at The University of Hong Kong in October 2007 The normality assumption of the conditional error distribution is often violated by real data. In the rst part of this study, the threshold autoregressive het- eroscedastic (ARCH) model is extended by replacing the usual Gaussian density by the Gram-Charlier (GC) density in order to obtain a more appropriate t- ting of the data. The two-regime threshold ARCH model with the GC density (TARCHSK) was examined in detail and a Lagrange-Multiplier (LM) test was developed. The test is similar to classical LM test on the threshold ARCH model butadjustedtotheregime-dependentkurtosis. Asaconsequence, ithasmoreac- curacyandrobustness. TARCHSKmodelswereappliedtosomeforeignexchange rateseries. Thekurtosis-adjustedtestismorelikelytoacceptthenullhypothesis while Wong and Li's test tends to reject it. The model was then extended to the threshold GARCH model with the GC density (TGARCHSK). The model wasapplied to the S&P 500 Index Series. Results show that the estimated model has smoother predicted variances than those obtained in the GARCH model. In the second part of this study, the Bayesian estimation was proposed as an alternative to the maximum likelihood estimate (MLE) for threshold time series models. The estimation considers parameters over the support of the whole pos- teriordistributionratherthanjustaroundthemode. MarkovChainMonteCarlo (MCMC)methodssuchasMetropolis-Hastingsalgorithmswereappliedtosample theposteriordistribution. SimulationsdemonstratethattheBayesianestimation greatly reduce the estimation time when compared with the classical maximum likelihoodestimationmethod. AnillustrativeexampleontheJPY/USDexchange rate series is presented. DOI: 10.5353/th_b3955706 Subjects: Autoregression (Statistics) Estimation theory


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Product Details
  • ISBN-13: 9781374672833
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 102
  • Weight: 535 gr
  • ISBN-10: 1374672831
  • Publisher Date: 27 Jan 2017
  • Binding: Hardback
  • Language: English
  • Spine Width: 8 mm
  • Width: 216 mm


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