Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models.     

On model selection in Bayesian regression

We discuss the problem of constructing a suitable regression model from a nonparametric Bayesian viewpoint. For this purpose, we consider the case when the error terms have symmetric and unimodal densities. By the Khintchine and Shepp theorem, the density of response variable can be written as a scale mixture of uniform densities. The mixing distribution is assumed to have a Dirichlet process prior. We further consider appropriate prior distributions for other parameters as the components of the predictive device. Among the possible submodels, we select the one which has the highest posterior probability. An example is given to illustrate the approach.     

The median of the poisson distribution

The purpose of this paper is twofold: first, to provide a closed form expression for the median of the Poisson distribution and, second, to improve the known estimates of the difference between the median and the mean of the Poisson distribution. We use elementary techniques based on the monotonicity of certain sequences involving tail probabilities of the Poisson distribution and the Central Limit TheoremReceived November 2002     

BAYESIAN INFERENCE METHODS FOR UNIVARIATE AND MULTIVARIATE GARCH MODELS: A SURVEY

This survey reviews the existing literature on the most relevant Bayesian inference methods for univariate and multivariate GARCH models. The advantages and drawbacks of each procedure are outlined as well as the advantages of the Bayesian approach versus classical procedures. The paper makes emphasis on recent Bayesian non‐parametric approaches for GARCH models that avoid imposing arbitrary parametric distributional assumptions. These novel approaches implicitly assume infinite mixture of Gaussian distributions on the standardized returns which have been shown to be more flexible and describe better the uncertainty about future volatilities. Finally, the survey presents an illustration using real data to show the flexibility and usefulness of the non‐parametric approach.     

Rank tests for testing randomness of a regression coefficient in a linear regression model

We propose a class of nonparametric tests for testing non-stochasticity of the regression parameterβ in the regression modely _i =βx _i +ɛ _i ,i=1, ...,n. We prove that the test statistics are asymptotically normally distributed both underH ₀ and under contiguous alternatives. The asymptotic relative efficiencies (in the Pitman sense) with respect to the best parametric test have also been computed and they are quite high. Some simulation studies are carried out to illustrate the results. Research was supported by the University Grants Commission, India.     

Dispersion of growth paths of macroeconomic models in thermodynamic limits: two-parameter Poisson–Dirichlet models

This paper discusses dispersion of growth patterns of macroeconomic models in thermodynamic limits. More specifically, the paper shows that the coefficients of variations of the total numbers of clusters and the numbers of clusters of specific sizes of one- and two-parameter Poisson–Dirichlet models behave qualitatively differently in the thermodynamic limits. The coefficients of variations of the numbers of clusters in the former class of distributions are all self-averaging, while the those in the latter class are all non-self averaging. In other words, dispersions or variations of growth rates about the means do not vanish in the two-parameter version of the model, while they do in the one-parameter version in the thermodynamic limits. The paper ends by pointing out other models, such as triangular urn models, may converge to Mittag–Leffler distributions which exhibit non-self-averaging behavior for certain parameter combinations. The author is grateful for many helps he received from H. Yoshikawa, and M. Sibuya.     

Depletion or expansion? Understanding the effects of support policy use on employee work and family outcomes

Past research on the effects of employees' use of work–family support policies tends to draw on a depletion perspective suggesting that using these policies may reduce work–family conflict. The emphasis on depletion fails to consider the expansion perspective that assumes that using work resources may enrich family functioning. Using a sample of 113 matched employee–supervisor pairs and a 1‐month separation between predictor and criterion measurement, we found support for the expansion rather than the depletion perspective. Specifically, the relationships between support policy use and employee job satisfaction and family efficacy (but not organisational citizenship behaviour) were mediated by work‐to‐family enrichment; these effects were realised only for employees with high levels of family identity. In contrast, no support was found for family‐to‐work conflict as a mediator of the model.     

Catching-up and inflation in Europe: Balassa–Samuelson, Engel's Law and other culprits

This study analyses the impact of economic catching-up on annual inflation rates in the European Union with a special focus on the new member countries of Central and Eastern Europe. Using an array of estimation methods, we show that the Balassa-Samuelson effect is not an important driver of inflation rates. By contrast, we find that the initial price level and regulated prices strongly affect inflation outcomes in a nonlinear manner and that the extension of Engel's Law may hold during periods of very fast growth. We interpret these results as a sign that price level convergence comes from goods, market and non-market service prices. Furthermore, we find that the Phillips curve flattens with a decline in the inflation rate, that inflation is more persistent and that commodity prices have a stronger effect on inflation in a higher inflation environment.     

Semiparametric analysis of clustered survival data under nonparametric frailty

In this article a novel approach to analyze clustered survival data that are subject to extravariation encountered through clustering of survival times is proposed. This is accomplished by extending the Cox proportional hazard model to a frailty model where the cluster-specific shared frailty is modeled nonparametrically. We assume a nonparametric Dirichlet process for the distribution of frailty. In such a semiparametric setup, we propose a hybrid method to draw model-based inferences. In the framework of the proposed hybrid method, the estimation of parameters is performed by implementing Monte Carlo expected conditional maximization algorithm. A simulation study is conducted to study the efficiency of our methodology. The proposed methodology is, thereafter, illustrated by a real-life data on recurrence time to infections in kidney patient.     

Roles, resources and benefits of intermediate organizations supporting triple helix collaborative R&D: The case of Precarn

Triple helix collaborations involving academia, government and industry are believed vital to the success of regional technology development. However, due to differences in culture, organizational functioning and incentive mechanisms as well as the different objectives of the various actors involved, such collaboration is difficult to create and sustain. A case study of the organization called Precarn, a collaborative, which manages a program of triple helix projects, is used here to illustrate how an intermediate organization can help triple helix partnerships towards the successful commercialization of new technologies. The paper contributes to the literature on managing R&D collaborations and innovation networks using organization theories to explain why and how collaborative intermediate organizations can facilitate successful technological adoption and commercialization across innovation networks.     

Simplified Alonso–Mills–Muth model with a monopoly vendor

One important but unrealistic assumption in the simplified Alonso–Mills–Muth (AMM(0)) model is that the composite good is ubiquitous and thus there is zero shopping cost for residents. This paper assumes that the composite good is only sold by a monopoly vendor inside the city and hence a shopping cost is inevitable for residents. It is shown that the vendor will locate at the city boundary in equilibrium. In contrast to the symmetric land rent pattern in the AMM(0) model, the current AMM(k) model offers an asymmetric land rent pattern in equilibrium. Moreover, this paper shows that a rent-maximizing government either regulates the vendor to locate at the central business district (CBD) (when income is high) or does not enact any regulation (when income is low).     

Semiparametric estimation of a censored regression model with an unknown transformation of the dependent variable

This paper presents a method for estimating the model Λ(Y)=min(β′X+U, C), where Y is a scalar, Λ is an unknown increasing function, X is a vector of explanatory variables, β is a vector of unknown parameters, U has unknown cumulative distribution function F, and C is a censoring threshold. It is not assumed that Λ and F belong to known parametric families; they are estimated nonparametrically. This model includes many widely used models as special cases, including the proportional hazards model with unobserved heterogeneity. The paper develops n^1/2-consistent, asymptotically normal estimators of Λ and F. Estimators of β that are n^1/2-consistent and asymptotically normal already exist. The results of Monte Carlo experiments illustrate the finite-sample behavior of the estimators.