Unobserved-component time-series models with Markov-switching heteroskedasticity
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Unobserved-component time-series models with Markov-switching heteroskedasticity changes in regime and the link between inflation rates and inflation uncertainty by Kim, Chang-Jin.

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Published by York University in Toronto .
Written in English

Subjects:

  • Inflation (Finance) -- Mathematical models,
  • Time series analysis,
  • Markov processes

Book details:

Edition Notes

StatementChang-Jin Kim.
SeriesWorking paper -- no. 92-1, Working paper series (York University (Toronto, Ont.). Dept. of Economics) : -- no. 92-1
Classifications
LC ClassificationsHG229 .K49 1992
The Physical Object
Pagination18 leaves. --
Number of Pages18
ID Numbers
Open LibraryOL18646171M

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In this article, I first extend the standard unobserved-component time series model to include Hamilton's Markov-switching heteroscedasticity. This will provide an alternative to the unobserved-component model with autoregressive conditional heteroscedasticity, as developed by Harvey, Ruiz, and Sentana and by Evans and Wachtel. I then apply a generalized version of the model . In this article, I first extend the standard unobserved-component time series model to include Hamilton’s Markov-switching heteroscedasticity. This will provide an alternative to the unobserved- component model with autoregressive conditional heteroscedasticity, as developed by Harvey, Ruiz, and Sentana and by Evans and by: PDF | On Feb 1, , C.J. Kim and others published Unobserved-Component Time-Series Models with Markov- Switching Heteroskedasticity: Changes in Regimes and the Link between Inflation Rates and Author: Chang-Jin Kim. Unobserved-component time Series Models With Markov-Switching Heteroscedasticity: Changes in Regime and the Link Between Inflation Rates and Inflation Uncertainty. Localización: Journal of business & economic statistics, ISSN , Vol. .

  Journals & Books; Help Download PDF The more conventional way of testing for financial time series heteroskedasticity is to consider ARCH-type volatility models, which allow constant unconditional volatility but time-varying conditional volatility. We consider an appropriately specified Markov switching unobserved component model as a. We specify a time-series model for real GNP and consumption in which the two share a common stochastic trend and transitory component, and Markov-regime switching is used to model business cycle. unobserved component model with Markov-switching heteroscedasticity (UC-MS model). The U.S market crash in was unusual from different aspects. This crash was the biggest one in indexes; only in one day, there was such a crash, unprecedented from , and it suddenly increased stock price fluctuations. Later, Schwert () showed.   This paper uses an unobserved component model with heteroskedastic disturbances based on Harvey et al. () to measure the time-varying importance of permanent and transitory components in the U.S. house prices. Our findings show that the cyclical component in the U.S. housing market is highly persistent and house prices were more than .

Unobserved-component time series models with Markov-switching heteroscedasticity: Changes in regime and the link between ination rates and ination uncertainty. Journal of Business & Economic Statistics, 11(3), [14] Kim, C. J. (b). Sources of monetary growth uncertainty and economic activity: The time-varying-parameter model with. Unobserved-component time series models with Markov-switching heteroskedasticity: Changes in regime and the link between inflation rates and inflation uncertainty, Journal of Business and Economic Statistics 11(3), Google Scholar. Kim, C.J. (). Markov switching time series models with application to a daily runoff series Zhan-Qian Lu Hong Kong University of Science and Technology L. Mark Berliner • Department of Statistics, Ohio State University, Columbus Abstract. We consider a class of Bayesian dynamic models that involve switching among various regimes. Exact formulae are provided for the calculation of multivariate skewness and kurtosis of Markov-switching Vector Auto-Regressive (MS VAR) processes as .