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A New Stochastic Regime Switching Model with Time-varying Regression Coefficients and Error Variances.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A New Stochastic Regime Switching Model with Time-varying Regression Coefficients and Error Variances./
作者:	Dong, Xiaojin.
面頁冊數:	1 online resource (214 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9781339937977

A New Stochastic Regime Switching Model with Time-varying Regression Coefficients and Error Variances.
Dong, Xiaojin.

A New Stochastic Regime Switching Model with Time-varying Regression Coefficients and Error Variances. - 1 online resource (214 pages)

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2016.

Includes bibliographical references

Since the publication of Hamilton's (1989) seminal work on Markov switching model, a large number of applications have been found in economics and finance. The classical Markov switching models characterize the estimation of parameters in finite state, limited by the pre-specified number of regimes, thus it is restrictive in empirical studies. In this thesis, we develop a stochastic regime switching model, where the model parameters are both categorical and continuous. By assuming conjugate priors and defining stochastic regime switching variables, we derive recursive filtering and smoothing algorithms to estimate the regimes and develop closed-form recursive Bayes estimates of the regression parameters. Moreover, bounded complexity mixture (BCMIX), an approximation scheme, is derived to increase the computation efficiency substantially and yet this method is comparable to the Bayes estimates in statistical efficiency. Hyperparameters are estimated via expectation and maximization procedure and presented in closed form solutions. Intensive simulation studies show that lower order of bounded complexity mixture procedure is as efficient as Bayes estimates and that estimation performs well on moderate large transition probability scenarios. A comparative simulation study shows that classical Markov switching models have a tendency to overestimate the transition probabilities. We used our model to analyze several US economic data, such as unemployment rate, industrial production and manufacturing and trade inventory, to show our model is more suitable than classical regime switching models in analyzing business cycles of economic time series data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339937977Subjects--Topical Terms:

556824
Statistics.
Index Terms--Genre/Form:

554714
Electronic books.

A New Stochastic Regime Switching Model with Time-varying Regression Coefficients and Error Variances.
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245 1 2 $a A New Stochastic Regime Switching Model with Time-varying Regression Coefficients and Error Variances.
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500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
500 $a Adviser: Haipeng Xing.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2016.
504 $a Includes bibliographical references
520 $a Since the publication of Hamilton's (1989) seminal work on Markov switching model, a large number of applications have been found in economics and finance. The classical Markov switching models characterize the estimation of parameters in finite state, limited by the pre-specified number of regimes, thus it is restrictive in empirical studies. In this thesis, we develop a stochastic regime switching model, where the model parameters are both categorical and continuous. By assuming conjugate priors and defining stochastic regime switching variables, we derive recursive filtering and smoothing algorithms to estimate the regimes and develop closed-form recursive Bayes estimates of the regression parameters. Moreover, bounded complexity mixture (BCMIX), an approximation scheme, is derived to increase the computation efficiency substantially and yet this method is comparable to the Bayes estimates in statistical efficiency. Hyperparameters are estimated via expectation and maximization procedure and presented in closed form solutions. Intensive simulation studies show that lower order of bounded complexity mixture procedure is as efficient as Bayes estimates and that estimation performs well on moderate large transition probability scenarios. A comparative simulation study shows that classical Markov switching models have a tendency to overestimate the transition probabilities. We used our model to analyze several US economic data, such as unemployment rate, industrial production and manufacturing and trade inventory, to show our model is more suitable than classical regime switching models in analyzing business cycles of economic time series data.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
650 4 $a Applied mathematics. $3 1069907
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