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58
Stochastic volatility: likelihood inference and comparison with ARCH models
 Review of Economic Studies
, 1998
"... In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihoodbased framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offse ..."
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Cited by 592 (40 self)
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In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihoodbased framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulationbased methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using nonnested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail. 1.
An omnibus test for univariate and multivariate normality. Nuffield College Discussion Paper W4&91.
, 1994
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Particle Methods for Bayesian Modelling and Enhancement of Speech Signals
, 2000
"... This paper applies timevarying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diusion processes. The main aim of the paper is to perform online estimation ..."
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Cited by 49 (6 self)
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This paper applies timevarying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diusion processes. The main aim of the paper is to perform online estimation of the clean speech and model parameters, and to determine the adequacy of the chosen statistical models. Ecient particle methods are developed to solve the optimal ltering and xedlag smoothing problems. The algorithms combine sequential importance sampling (SIS), a selection step and Markov chain Monte Carlo (MCMC) methods. They employ several variance reduction strategies to make the best use of the statistical structure of the model. It is also shown how model adequacy may be determined by combining the particle lter with frequentist methods. The modelling and enhancement performance of the models and estimation algorithms are evaluated in simulation studies on both synthetic and re...
Regression Models with DataBased Indicator Variables
 Oxford Bulletin of Economics and Statistics
, 2005
"... OLS estimation of an impulseindicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a tdistribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte ..."
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Cited by 18 (7 self)
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OLS estimation of an impulseindicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a tdistribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitting the use of many dummies in a generaltospecific framework. Although White’s (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an improvement. Finally, a
HIGH MOMENT PARTIAL SUM PROCESSES OF RESIDUALS IN GARCH MODELS AND THEIR APPLICATIONS 1
, 2006
"... In this paper we construct high moment partial sum processes based on residuals of a GARCH model when the mean is known to be 0. We consider partial sums of kth powers of residuals, CUSUM processes and selfnormalized partial sum processes. The kth power partial sum process converges to a Brownian p ..."
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Cited by 14 (0 self)
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In this paper we construct high moment partial sum processes based on residuals of a GARCH model when the mean is known to be 0. We consider partial sums of kth powers of residuals, CUSUM processes and selfnormalized partial sum processes. The kth power partial sum process converges to a Brownian process plus a correction term, where the correction term depends on the kth moment µk of the innovation sequence. If µk = 0, then the correction term is 0 and, thus, the kth power partial sum process converges weakly to the same Gaussian process as does the kth power partial sum of the i.i.d. innovations sequence. In particular, since µ1 = 0, this holds for the first moment partial sum process, but fails for the second moment partial sum process. We also consider the CUSUM and the selfnormalized processes, that is, standardized by the residual sample variance. These behave as if the residuals were asymptotically i.i.d. We also study the joint distribution of the kth and (k + 1)st selfnormalized partial sum processes. Applications to changepoint problems and goodnessoffit are considered, in particular, CUSUM statistics for testing GARCH model structure change and the Jarque– Bera omnibus statistic for testing normality of the unobservable innovation distribution of a GARCH model. The use of residuals for constructing a kernel density function estimation of the innovation distribution is discussed.
Quantitive evaluation of pairs and RS steganalysis
 in Security, Steganography, and Watermarking of Multimedia Contents
, 2004
"... We give initial results from a new project which performs statistically accurate evaluation of the reliability of image steganalysis algorithms. The focus here is on the Pairs and RS methods, for detection of simple LSB steganography in grayscale bitmaps, due to Fridrich et al. Using libraries total ..."
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Cited by 12 (4 self)
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We give initial results from a new project which performs statistically accurate evaluation of the reliability of image steganalysis algorithms. The focus here is on the Pairs and RS methods, for detection of simple LSB steganography in grayscale bitmaps, due to Fridrich et al. Using libraries totalling around 30,000 images we have measured the performance of these methods and suggest changes which lead to significant improvements. Particular results from the project presented here include notes on the distribution of the RS statistic, the relative merits of different “masks ” used in the RS algorithm, the effect on reliability when previously compressed cover images are used, and the effect of repeating steganalysis on the transposed image. We also discuss improvements to the Pairs algorithm, restricting it to spatially close pairs of pixels, which leads to a substantial performance improvement, even to the extent of surpassing the RS statistic which was previously thought superior for grayscale images. We also describe some of the questions for a general methodology of evaluation of steganalysis, and potential pitfalls caused by the differences between uncompressed, compressed, and resampled cover images.
OnLine Bayesian Modelling and Enhancement of Speech Signals
, 2000
"... This paper applies timevarying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diusion processes. The main aim of the paper is to perform online estimation ..."
Abstract

Cited by 9 (8 self)
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This paper applies timevarying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diusion processes. The main aim of the paper is to perform online estimation of the clean speech and model parameters, and to determine the adequacy of the chosen statistical models. Ecient simulationbased methods are developed to solve the optimal ltering and xedlag smoothing problems. The algorithms combine sequential importance sampling (SIS), a selection step and Markov chain Monte Carlo (MCMC) methods. They employ of several variance reduction strategies to make the best use of the statistical structure of the model. It is also shown how model adequacy may be determined by combining the simulationbased optimal lter with frequentist methods. The modelling and enhancement performance of the models and estimation algorithms are evaluated in simulation studi...
Parallel computation in econometrics: A simplified approach
 College, University of Oxford
, 2004
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Does oilrig activity react to oil price changes? An
"... In this paper, we analyze how oilrig activity in different nonOPEC regions is affected by the crude oil price. We estimate relationships between oilrig activity and crude oil prices using dynamic regression models augmented with latent components capturing trend and seasonality. The results general ..."
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Cited by 5 (1 self)
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In this paper, we analyze how oilrig activity in different nonOPEC regions is affected by the crude oil price. We estimate relationships between oilrig activity and crude oil prices using dynamic regression models augmented with latent components capturing trend and seasonality. The results generally show a positive relationship between oilrig activity and the crude oil price, but the strength of the relationship differs across regions. Overall, there seems to be a clear relationship between the oil industry structure in the region and the oilrig activity's reaction to price changes. On average, the longrun price elasticity for oilrig activity in nonOPEC countries is around unity.