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dc.contributor.authorWang, Yini
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2012-07-26 23:11:58.86en
dc.date2012-07-30 14:22:57.867en
dc.date2012-07-30 15:20:38.253en
dc.date.accessioned2012-08-01T19:37:53Z
dc.date.available2012-08-01T19:37:53Z
dc.date.issued2012-08-01
dc.identifier.urihttp://hdl.handle.net/1974/7340
dc.descriptionThesis (Ph.D, Economics) -- Queen's University, 2012-07-30 15:20:38.253en
dc.description.abstractThis dissertation considers quantile regression models with nonstationary or nearly nonstationary time series. The first chapter outlines the thesis and discusses its theoretical and empirical contributions. The second chapter studies inference in quantile regressions with cointegrated variables allowing for multiple structural changes. The unknown break dates and regression coefficients are estimated jointly and consistently. The conditional quantile estimator has a nonstandard limit distribution. A fully modified estimator is proposed to remove the second-order bias and nuisance parameters and the resulting limit distribution is mixed normal. A simulation study shows that the fully modified quantile estimator has good finite sample properties. The model is applied to stock index data from the emerging markets of China and several mature markets. Financial market integration is found in some quantiles of the Chinese stock indices. The third chapter considers predictive quantile regression with a nearly integrated regressor. We derive nonstandard distributions for the quantile regression estimator and t-statistic in terms of functionals of diffusion processes. The critical values are found to depend on both the quantile of interest and the local-to-unity parameter, which is not consistently estimable. Based on these critical values, we propose a valid Bonferroni bounds test for quantile predictability with persistent regressors. We employ this new methodology to test the ability of many commonly employed and highly persistent regressors, such as the dividend yield, earnings price ratio, and T-bill rate, to predict the median, shoulders, and tails of the stock return distribution. Chapter Four proposes a cumulated sum (CUSUM) test for the null hypothesis of quantile cointegration. A fully modified quantile estimator is adopted for serial correlation and endogeneity corrections. The CUSUM statistic is composed of the partial sums of the residuals from the fully modified quantile regression. Under the null, the test statistic converges to a functional of Brownian motions. In the application to U.S. interest rates of different maturities, evidence in favor of the expectations hypothesis for the term structure is found in the central part of the distributions of the Treasury bill rate and financial commercial paper rate, but in the tails of the constant maturity rate distribution.en_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectstructural changeen_US
dc.subjectlocal-to-unityen_US
dc.subjectBonferroni methoden_US
dc.subjectpredictabilityen_US
dc.subjectquantile regressionen_US
dc.subjectcointegrationen_US
dc.subjectfully modified estimatoren_US
dc.titleThree Essays on Time Series Quantile Regressionen_US
dc.typethesisen_US
dc.description.degreePh.Den
dc.contributor.supervisorNielsen, Morten Ørregaarden
dc.contributor.supervisorShimotsu, Katsumien
dc.contributor.departmentEconomicsen


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