Unobserved-Component Time Series Models with Markov-Switching Heteroscedasticity: Changes in Regime and the Link between Inflation Rates and Inflation Uncertainty

1993 ◽  
Vol 11 (3) ◽  
pp. 341 ◽  
Author(s):  
Chang-Jin Kim
Keyword(s):  
Time Series ◽  
Markov Switching ◽  
Time Series Models ◽  
Inflation Uncertainty ◽  
Unobserved Component ◽  
Inflation Rates ◽  
Component Time

scholarly journals Unobserved component time series models with Arch disturbances

1992 ◽  
Vol 52 (1-2) ◽  
pp. 129-157 ◽  
Author(s):  
Andrew Harvey ◽  
Esther Ruiz ◽  
Enrique Sentana
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Unobserved Component ◽  
Component Time

scholarly journals Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Nima Nonejad
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Time Series Models ◽  
Unobserved Component ◽  
Volatility Model ◽  
Component Time

AbstractThis paper details particle Markov chain Monte Carlo (PMCMC) techniques for analysis of unobserved component time series models using several economic data sets. The objective of this paper is to explain the basics of the methodology and provide computational applications that justify applying PMCMC in practice. For instance, we use PMCMC to estimate a stochastic volatility model with a leverage effect, Student-t distributed errors or serial dependence. We also model time series characteristics of monthly US inflation rate by considering a heteroskedastic ARFIMA model where heteroskedasticity is specified by means of a Gaussian stochastic volatility process.

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