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This study examines the mixture hypothesis of conditional geometric distributions using a likelihood ratio (LR) test statistic based on that used for unconditionalgeometricdistributions. Assuch,wederivethenulllimitdistribution of the LR test statistic and examine its power performance. In addition, we examine the interrelationship between the LR test statistics used to test the geometric and exponential mixture hypotheses. We also examine the performance of the LR test statistics under various conditions and confirm the main claims of the study using Monte Carlo simulations.

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We build a general equilibrium model of industry dynamics and conduct policy experiments. The model is designed to match the entry and exit patterns in the U.S. manufacturing sector. We analyze two policies. First, we consider imposing a firing tax. Both a constant firing tax and a countercyclical firingtaxincreasethevolatilityoftheentryrateandaggregateoutput. Thisfindingcontrastswiththestabilizationeffectsoffiringtaxesinpreviousmodelswith exogenous entry and exit. Second, we consider subsidies to entry costs. Countercyclical entry subsidies stabilize the entry rate and are effective in stabilizing the aggregate output over the business cycle.

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We proposes a new Bayesian MCMC algorithm for dynamic stochastic copula models with dependence parameters as unobserved state variables and presents the performance of the proposed MCMC algorithm through simulations. Our MCMC algorithm draws the state variables with an acceptancerejection Metropolis-Hastings algorithm using the candidate generating probability density function obtained by approximating the probability density function of the observed variables to the normal distribution of the dependence parameter.As an empirical example,weanalyzedthe stochasticcopulamodels for the KOSPI index and the HSCE index (Hang Seng China enterprise index) returnsfromJanuary3,2003toDecember30,2014usingtheproposedalgorithm. The Bayesian inference and model comparison results of the stochastic copula models of Gaussian copula, Student t-copula, Clayton copula, Frank copula, rotated Gumbel copula, and Plackett copula showed that Student t-copula model couldbeselectedasthebestmodel.Thesemodelcomparisonsresultsimplythat even though Gaussian stochastic copula model can capture ��near asymptotic dependence��, there may exist extreme tail dependence that can not be captured by the Gaussian stochastic copula model.