Bayesian Model Uncertainty In Smooth Transition Autoregressions

Lopes, Hedibert F.; Salazar, Esther

doi:10.1111/j.1467-9892.2005.00455.x

Cited by 39 publications

(26 citation statements)

References 45 publications

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“…Many papers report very high standard errors compared to estimates (see Chelley-Steeley 2005;Akram et al 2005 andLubrano 2001); or report large estimates, essentially giving a sharp threshold transition function (see Nam et al 2001;Lopes andSalazar 2006 andAkram et al 2005). Lubrano (2001) discusses Bayesian inference in an ST-GARCH model and suggests some informative prior distributions on the smoothing parameter.…”

Section: Introductionmentioning

confidence: 96%

Bayesian inference and model comparison for asymmetric smooth transition heteroskedastic models

Gerlach

Chen

2008

Stat Comput

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Inference, quantile forecasting and model comparison for an asymmetric double smooth transition heteroskedastic model is investigated. A Bayesian framework in employed and an adaptive Markov chain Monte Carlo scheme is designed. A mixture prior is proposed that alleviates the usual identifiability problem as the speed of transition parameter tends to zero, and an informative prior for this parameter is suggested, that allows for reliable inference and a proper posterior, despite the non-integrability of the likelihood function. A formal Bayesian posterior model comparison procedure is employed to compare the proposed model with its two limiting cases: the double threshold GARCH and symmetric ARX GARCH models. The proposed methods are illustrated using both simulated and international stock market return series. Some illustrations of the advantages of an adaptive sampling scheme for these models are also provided. Finally, Bayesian forecasting methods are employed in a Value-at-Risk study of the international return series. The results generally favour the proposed smooth transition model and highlight explosive and smooth nonlinear behaviour in financial markets.

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Section: Introductionmentioning

confidence: 96%

Bayesian inference and model comparison for asymmetric smooth transition heteroskedastic models

Gerlach

Chen

2008

Stat Comput

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“…This advantage will prove decisive in Section 8, where several thousand MCMC estimations will be needed. Lopes and Salazar (2006) also present a reversible jump MCMC method where the autoregressive lag order and transition delay parameter are included in the parameter space. By contrast, our method treats p, and the transition variable s t , as fixed; it is proposed to investigate the choice of p and s t by estimating marginal likelihoods for a range of candidate models.…”

Section: A Posterior Simulator For Smooth Transition Autoregressionsmentioning

confidence: 99%

“…The number of degrees of freedom in (9) can be chosen by experimentation; in the empirical part of this paper, a value of D 3 was chosen and led to acceptance rates of approximately 0.80. Lopes and Salazar (2006) parameterize equation (2) in terms of rather that 2 , and ensure the positivity of by specifying a prior with positive support (such as a Gamma distribution) for this parameter. They use a random walk Metropolis-Hastings chain, where two tuning parameters must be specified.…”

Section: A Posterior Simulator For Smooth Transition Autoregressionsmentioning

confidence: 99%

Comparing smooth transition and Markov switching autoregressive models of US unemployment

Deschamps

2008

J of Applied Econometrics

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SUMMARYLogistic smooth transition and Markov switching autoregressive models of a logistic transform of the monthly US unemployment rate are estimated by Markov chain Monte Carlo methods. The Markov switching model is identified by constraining the first autoregression coefficient to differ across regimes. The transition variable in the LSTAR model is the lagged seasonal difference of the unemployment rate. Out-of-sample forecasts are obtained from Bayesian predictive densities. Although both models provide very similar descriptions, Bayes factors and predictive efficiency tests (both Bayesian and classical) favor the smooth transition model.

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“…First, we follow Lopes and Salazar (2005) and jointly draw the transition function parameters and c from gamma and uniform 9 proposal distributions, respectively.…”

Section: Estimation and Possible Identi…cation Issuesmentioning

confidence: 99%

“…Given the prior, the posterior is not a standard form; , however, can be drawn using a Metropolis-inGibbs step (Lopes and Salazar, 2005 …”

mentioning

confidence: 99%

Financial Stress Regimes and the Macroeconomy

Galvão¹,

Owyang²

2014

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Some …nancial stress events lead to macroeconomic downturns, while others appear to be isolated to …nancial markets. We identify …nancial stress regimes using a model that explicitly links …nancial variables to macroeconomic outcomes. The stress regimes are identi…ed using an unbalanced panel of …nancial variables with an embedded method for variable selection. Our identi…ed stress regimes are associated with corporate credit tightening and with NBER recessions. An exogenous deterioration in our …nancial condition index has strong negative e¤ects in economic activity, and negative ampli…cation e¤ects on in ‡ation in the stress regime. We employ a novel factor-augmented vector autoregressive model with smooth regime changes (FAST-VAR).

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Bayesian Model Uncertainty In Smooth Transition Autoregressions

Cited by 39 publications

References 45 publications

Bayesian inference and model comparison for asymmetric smooth transition heteroskedastic models

Bayesian inference and model comparison for asymmetric smooth transition heteroskedastic models

Comparing smooth transition and Markov switching autoregressive models of US unemployment

Financial Stress Regimes and the Macroeconomy

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