The semiparametric asymmetric stochastic volatility model with time-varying parameters: The case of US inflation

We propose a semiparametric extension of the time-varying parameter regression model with asymmetric stochastic volatility. For parameter estimation we use Bayesian methods. We illustrate our methods with an application to US inflation. •A semiparametric asymmetric stochastic volatility model with t...

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Bibliographic Details
Published inEconomics letters Vol. 155; pp. 14 - 18
Main Author Dimitrakopoulos, Stefanos
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.06.2017
Elsevier Science Ltd
Subjects
Asymmetric stochastic volatility
Bayesian analysis
Bayesian theory
Dirichlet process
Inflation
Markov chain Monte Carlo
Parameter estimation
regression analysis
Time-varying parameters
United States
Volatility
United States > US
United States
C11
C22
Inflation
Time-varying parameters
Asymmetric stochastic volatility
C15
Markov chain Monte Carlo
C14
Dirichlet process
Online AccessGet full text
ISSN0165-1765
1873-7374
DOI10.1016/j.econlet.2017.02.039

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Summary:We propose a semiparametric extension of the time-varying parameter regression model with asymmetric stochastic volatility. For parameter estimation we use Bayesian methods. We illustrate our methods with an application to US inflation. •A semiparametric asymmetric stochastic volatility model with time-varying parameters is considered.•An efficient Markov Chain Monte Carlo estimation algorithm is developed.•The proposed model is applied to inflation modeling.•The proposed model shows positive correlation between inflation and volatility.•The proposed model forecasts better that competing models.
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ISSN:0165-1765
1873-7374
DOI:10.1016/j.econlet.2017.02.039