Results 1  10
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21
Time varying structural vector autoregressions and monetary policy
 Review of Economic Studies
, 2005
"... Monetary policy and the private sector behavior of the US economy are modeled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations. The paper develops a new, simple modeling strategy f ..."
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Cited by 296 (9 self)
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Monetary policy and the private sector behavior of the US economy are modeled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations. The paper develops a new, simple modeling strategy for the law of motion of the variance covariance matrix and proposes an efficient Markov chain Monte Carlo algorithm for the model likelihood/posterior numerical evaluation. The main empirical conclusions are: 1) both systematic and nonsystematic monetary policy have changed during the last forty years. In particular, systematic responses of the interest rate to inflation and unemployment exhibit a trend toward a more aggressive behavior, despite remarkable oscillations; 2) this has had a negligible effect on the rest of the economy. The role played by exogenous nonpolicy shocks seems more important than interest rate policy in explaining the high inflation and unemployment episodes in recent US economic history.
Continuous Record Asymptotics for Rolling Sample Variance Estimators
 Econometrica
, 1996
"... It is widely known that conditional covariances of asset returns change over time. ..."
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Cited by 128 (0 self)
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It is widely known that conditional covariances of asset returns change over time.
Bayesian Dynamic Factor Models and Portfolio Allocation
 Journal of Business and Economic Statistics
, 2000
"... This article is available in electronic form on the ISDS web site, http://www.stat.duke.edu ..."
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Cited by 100 (7 self)
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This article is available in electronic form on the ISDS web site, http://www.stat.duke.edu
Bayesian vectorautoregressions with stochastic volatility
 Econometrica
, 1997
"... This paper proposes a Bayesian approach to a vector autoregression with stochastic volatility, where the multiplicative evolution of the precision matrix is driven by a multivariate beta variate. Exact updating formulas are given to the nonlinear filtering of the precision matrix. Estimation of the ..."
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Cited by 42 (2 self)
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This paper proposes a Bayesian approach to a vector autoregression with stochastic volatility, where the multiplicative evolution of the precision matrix is driven by a multivariate beta variate. Exact updating formulas are given to the nonlinear filtering of the precision matrix. Estimation of the autoregressive parameters requires numerical methods: an importancesampling based approach is explained here.
Bayesian Regression Analysis With Scale Mixtures of Normals
, 1999
"... This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals. Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and scale parameters. ..."
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Cited by 21 (7 self)
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This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals. Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and scale parameters. We find that whereas existence of the posterior distribution does not depend on the choice of the design matrix or the mixing distribution, both of them can crucially intervene in the existence of posterior moments. We identify some useful characteristics that allow for an easy verification of the existence of a wide range of moments. In addition, we provide full characterizations under sampling from finite mixtures of Normals, Pearson VII or certain Modulated Normal distributions. For empirical applications, a numerical implementation based on the Gibbs sampler is recommended.
Inference for adaptive time series models: stochastic volatility and conditionally Gaussian state space form
, 2003
"... In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSFSV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by reparameteri ..."
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Cited by 18 (5 self)
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In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSFSV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by reparameterising the model. We illustrate our results on an example from financial economics and one from the nonparametric regression model. We also develop an effective particle filter for this model which is useful to assess the fit of the model.
Bayesian Option Pricing Using Asymmetric Garch
 CORE DP 9759, LouvainlaNeuve
, 1997
"... This paper shows how one can compute option prices from a Bayesian inference viewpoint, using an econometric model for the dynamics of the return and of the volatility of the underlying asset. The proposed evaluation of an option is the predictive expectation of its payoff function. The predictive d ..."
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Cited by 12 (1 self)
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This paper shows how one can compute option prices from a Bayesian inference viewpoint, using an econometric model for the dynamics of the return and of the volatility of the underlying asset. The proposed evaluation of an option is the predictive expectation of its payoff function. The predictive distribution of this function provides a natural metric with respect to which the predictive option price, or other option evaluations, can be gauged. The proposed method is compared to the Black and Scholes evaluation, in which a predictive mean volatility is plugged, but which does not provide a natural metric. The methods are illustrated using an asymmetric GARCH model with a data set on a stock index in Brussels. The persistence of the volatility process is linked to the prediction horizon and to the option maturity.
Bayesian StateSpace Modelling of SpatioTemporal NonGaussian Radar Returns
, 1998
"... ly reported work on target detection in coherent radar systems, a complex autoregressive process is proposed as the basis for characterisation of high resolution sea clutter spectra in incoherent radar systems. As no phase information is available in incoherent radar returns, the Gibbs sampler is us ..."
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Cited by 7 (0 self)
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ly reported work on target detection in coherent radar systems, a complex autoregressive process is proposed as the basis for characterisation of high resolution sea clutter spectra in incoherent radar systems. As no phase information is available in incoherent radar returns, the Gibbs sampler is used to facilitate sampling from the autoregressive process parameter posterior distribution, conditional on the observed amplitudes. To this end, the Hybrid Monte Carlo algorithm is employed to conditionally sample for the missing phases. Based on birthdeath migration arguments for the evolution of a population of scattering centres on an ocean surface, a conditional heteroscedastic (i.e. nonconstant prediction error variance) model is proposed for the modulating component of high resolution sea clutter in the logarithm domain. However, based on the results obtained for a large database of sea clutter range profiles, it is shown that there appears to be no strong evidence fo
Subjective expectations and assetreturn puzzles
 American Economic Review
"... any number of past empirical observations must ..."
Modeling Time Series Count Data: A StateSpace Approach toEvent Counts
, 1998
"... Time series count data is prevalent in political science. We argue that political scientists should employ time series methods to analyze time series count data. A simple statespace model is presented that extends the Kalman lter to count data. The properties of this model are outlined and further ..."
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Time series count data is prevalent in political science. We argue that political scientists should employ time series methods to analyze time series count data. A simple statespace model is presented that extends the Kalman lter to count data. The properties of this model are outlined and further evaluated by a Monte Carlo study. Wethen show howtime series of counts present special problems by turning to two replications: the number of hospital deaths that are the subject of a recent criminal court case, and Pollins (1996) MIDs data from international relations.