Finite normal mixture copulas for multivariate discrete data modeling

A new family of copulas is introduced that provides flexible dependence structure while being tractable and simple to use for multivariate discrete data modeling. The construction exploits finite mixtures of uncorrelated normal distributions. Accordingly, the cumulative distribution function is simply the product of univariate normal distributions. At the same time, however, the mixing operation introduces association. The properties of the new family of copulas are examined and a concrete application is used to show its applicability.     

COUPLING COUPLES WITH COPULAS: ANALYSIS OF ASSORTATIVE MATCHING ON RISK ATTITUDE

We investigate patterns of assortative matching on risk attitude, using self‐reported (ordinal) data on risk attitudes for males and females within married couples, from the German Socio‐Economic Panel over the period 2004–2012. We apply a novel copula‐based bivariate panel ordinal model. Estimation is in two steps: first, a copula‐based Markov model is used to relate the marginal distribution of the response in different time periods, separately for males and females; second, another copula is used to couple the males' and females' conditional (on the past) distributions. We find positive dependence, both in the middle of the distribution, and in the joint tails, and we interpret this as positive assortative matching (PAM). Hence we reject standard assortative matching theories based on risk‐sharing assumptions, and favor models based on alternative assumptions such as the ability of agents to control income risk. We also find evidence of “assimilation”; that is, PAM appearing to increase with years of marriage. (JEL C33, C51, D81)     

On composite likelihood in bivariate meta-analysis of diagnostic test accuracy studies

The composite likelihood is amongst the computational methods used for estimation of the generalized linear mixed model (GLMM) in the context of bivariate meta-analysis of diagnostic test accuracy studies. Its advantage is that the likelihood can be derived conveniently under the assumption of independence between the random effects, but there has not been a clear analysis of the merit or necessity of this method. For synthesis of diagnostic test accuracy studies, a copula mixed model has been proposed in the biostatistics literature. This general model includes the GLMM as a special case and can also allow for flexible dependence modelling, different from assuming simple linear correlation structures, normality and tail independence in the joint tails. A maximum likelihood (ML) method, which is based on evaluating the bi-dimensional integrals of the likelihood with quadrature methods, has been proposed, and in fact it eases any computational difficulty that might be caused by the double integral in the likelihood function. Both methods are thoroughly examined with extensive simulations and illustrated with data of a published meta-analysis. It is shown that the ML method has no non-convergence issues or computational difficulties and at the same time allows estimation of the dependence between study-specific sensitivity and specificity and thus prediction via summary receiver operating curves.     

On the estimation of normal copula discrete regression models using the continuous extension and simulated likelihood

The continuous extension of a discrete random variable is amongst the computational methods used for estimation of multivariate normal copula-based models with discrete margins. Its advantage is that the likelihood can be derived conveniently under the theory for copula models with continuous margins, but there has not been a clear analysis of the adequacy of this method. We investigate the asymptotic and small-sample efficiency of two variants of the method for estimating the multivariate normal copula with univariate binary, Poisson, and negative binomial regressions, and show that they lead to biased estimates for the latent correlations, and the univariate marginal parameters that are not regression coefficients. We implement a maximum simulated likelihood method, which is based on evaluating the multidimensional integrals of the likelihood with randomized quasi-Monte Carlo methods. Asymptotic and small-sample efficiency calculations show that our method is nearly as efficient as maximum likelihood for fully specified multivariate normal copula-based models. An illustrative example is given to show the use of our simulated likelihood method.     

Regression in a copula model for bivariate count data

In many cases of modeling bivariate count data, the interest lies on studying the association rather than the marginal properties. We form a flexible regression copula-based model where covariates are used not only for the marginal but also for the copula parameters. Since copula measures the association, the use of covariates in its parameters allow for direct modeling of association. A real-data application related to transaction market basket data is used. Our goal is to refine and understand whether the association between the number of purchases of certain product categories depends on particular demographic customers’ characteristics. Such information is important for decision making for marketing purposes.     

Decisive FDI obstacles as an explanatory reason for limited FDI inflows in an EMU member state: The case of Greece

A questionnaire survey covering MNCs that had invested in Greece during the period 1995–2003 is carried out in order to determine the barriers of inward FDI in Greece through a phase of increased expectations on foreign investment from hosting the 2004 Olympiad. The underlying assumption made is that the views of the local managers reflect the views of the mother company when deciding to invest. Although the findings represent the time that the investment took place, the influence of the conditions shaping the economic environment at the time the survey was carried out is also discussed. The findings indicate that the primary barriers to foreign investors are bureaucracy, taxation, corruption, and the labor market structure and support those of previous quantitative studies, leading to the conclusion that there is no progress made regarding these factors, in order to enhance FDI attractiveness. It becomes crucial for Greece to modernize and upgrade state mechanisms, through a more effective organization and administrative policies.     

The impact of adverse initial conditions and the resulting speed and sequence of reforms in determining the delayed EU membership of Bulgaria

The subject of the paper is the analysis of the speed, sequence and path of a formerly centrally administered economy, Bulgaria, to a market economy. Comparing the macroeconomic developments and transition reforms of Central and Eastern European (CEE) countries, Bulgaria is lagging behind. It is concluded that the stop-and-go nature of the conducted Bulgarian reforms and the lack of commitment to deep-seated reforms by successive governments were due to the adverse initial economic conditions that the country experienced during transition, as well as the intense external shocks the country endured. These factors were the main barriers to attaining sustained growth, and contributed to Bulgaria's delayed entrance to the EU.