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In: Communications in statistics. Theory and methods, Band 46, Heft 15, S. 7409-7426
ISSN: 1532-415X
In: Bundesbank Discussion Paper No. 37/2013
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In: Discussion paper 37/2013
This paper presents a novel Bayesian method for estimating dynamic stochastic general equilibrium (DSGE) models subject to a constrained posterior distribution of the implied Sharpe ratio. We apply our methodology to a DSGE model with habit formation in consumption and leisure, using an estimate of the Sharpe ratio to construct the constraint. We show that the constrained estimation produces a quantitative model with both reasonable asset-pricing as well as business-cycle implications.
In: Journal of Time Series Analysis, Band 40, Heft 1, S. 151-157
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In: Journal of survey statistics and methodology: JSSAM, Band 11, Heft 2, S. 484-510
ISSN: 2325-0992
AbstractSurvey data are often collected under multistage sampling designs where units are binned to clusters that are sampled in a first stage. The unit-indexed population variables of interest are typically dependent within cluster. We propose a Fully Bayesian method that constructs an exact likelihood for the observed sample to incorporate unit-level marginal sampling weights for performing unbiased inference for population parameters while simultaneously accounting for the dependence induced by sampling clusters of units to produce correct uncertainty quantification. Our approach parameterizes cluster-indexed random effects in both a marginal model for the response and a conditional model for published, unit-level sampling weights. We compare our method to plug-in Bayesian and frequentist alternatives in a simulation study and demonstrate that our method most closely achieves correct uncertainty quantification for model parameters, including the generating variances for cluster-indexed random effects. We demonstrate our method in an application with NHANES data.KEY WORDS: Inclusion probabilities; Mixed-effects linear model; NHANES; Primary stage sampling unit; Sampling weights; Survey sampling.
In: Communications in statistics. Simulation and computation, Band 53, Heft 5, S. 2527-2553
ISSN: 1532-4141
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Working paper
In: Statistica Neerlandica: journal of the Netherlands Society for Statistics and Operations Research, Band 56, Heft 1, S. 23-33
ISSN: 1467-9574
This paper discusses some simple practical advantages of Markov chain Monte Carlo (MCMC) methods in estimating entry and exit transition probabilities from repeated independent surveys. Simulated data are used to illustrate the usefulness of MCMC methods when the likelihood function has multiple local maxima. Actual data on the evaluation of an HIV prevention intervention program among drug users are used to demonstrate the advantage of using prior information to enhance parameter identificaiton. The latter example also demonstrates an important strength of the MCMC approach, namely the ability to make inferences on arbitrary functions of model parameters.
In: Working paper 2003,15
In: Communications in statistics. Theory and methods, Band 46, Heft 4, S. 1606-1620
ISSN: 1532-415X
In: Progress in nuclear energy: the international review journal covering all aspects of nuclear energy, Band 150, S. 104291
ISSN: 0149-1970
In: Adv. Environ. Biol., 9(14) 2015
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Working paper