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Predictive Quantile Regressions Under Persistence and Conditional Heteroskedasticity
Fan, Rui - 2019
parameters in the limit theory, whose estimation errors can be another source of distortion. We propose a size …
Persistent link: https://www.econbiz.de/10012901370
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Bootstrapping autoregressions with conditional heteroskedasticity of unknown form
Gonçalves, Sílvia; Kilian, Lutz - 2002
Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity....
Persistent link: https://www.econbiz.de/10001727625
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An introduction to univariate GARCH models
Teräsvirta, Timo (contributor) - 2006
This paper contains a survey of univariate models of conditional heteroskedasticity. The classical ARCH model is mentioned, and various extensions of the standard GARCH model are highlighted. This includes the Exponential GARCH model. Stochastic volatility models remain outside this review. --...
Persistent link: https://www.econbiz.de/10003394988
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Limit Theory for Explosive Autoregression Under Conditional Heteroskedasticity
Lee, Ji Hyung - 2017
This paper studies an explosive autoregression with conditionally heteroskedastic innovations. The asymptotic distributions of LS, GLS, t-statitiscs, heteroskedasticity-consistent t-statistics and GLS t-statistics are derived for nonstationary local-to-unity and mildly explosive roots, in which...
Persistent link: https://www.econbiz.de/10012950101
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Mildly Explosive Autoregression Under Stationary Conditional Heteroskedasticity
Arvanitis, Stelios - 2018
A limit theory is developed for mildly explosive autoregressions under stationary (weakly or strongly dependent …. These effects are cancelled out in least squares regression theory and thereby the Cauchy limit theory of Phillips and …
Persistent link: https://www.econbiz.de/10012927802
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Parameter Estimation in Nonlinear AR-GARCH Models
Meitz, Mika - 2008
This paper develops an asymptotic estimation theory for nonlinear autoregressive models with conditionally …
Persistent link: https://www.econbiz.de/10012723988
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Testing for Conditional Heteroscedasticity in the Components of Inflation
Broto, Carmen - 2008
residuals to identify heteroscedastic components. We show that conditional heteroscedasticity truly present in the data can be … power to detect conditional heteroscedasticity. Furthermore, the proposed statistics can help to decide which component is … experiments. Finally, we use auxiliary residuals to detect conditional heteroscedasticity in monthly inflation series of eight …
Persistent link: https://www.econbiz.de/10012724002
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Mixed normal conditional heteroskedasticity
Haas, Markus; Mittnik, Stefan; Paolella, Marc S. - 2002
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the...
Persistent link: https://www.econbiz.de/10009767120
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Bootstrapping autoregressions with conditional heteroskedasticity of unknown form
Gonçalves, Sílvia; Kilian, Lutz - 2002
Conditional heteroskedasticity is an important feature of many macroeconomic and financial time series. Standard residual-based bootstrap procedures for dynamic regression models treat the regression error as i.i.d. These procedures are invalid in the presence of conditional heteroskedasticity....
Persistent link: https://www.econbiz.de/10011431797
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Bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets
Cavalierea, Giuseppe; Ørregard Nielsen, Morten; … - 2013 - 12-2013
We propose bootstrap implementations of the asymptotic Wald, likelihood ratio and Lagrange multiplier tests for the order of integration of a fractionally integrated time series. Our main purpose in doing so is to develop tests which are robust to both conditional and unconditional...
Persistent link: https://www.econbiz.de/10009743847