Monitoring the conforming fraction of high-quality processes using a control chart p under a small sample size and an alternative estimator

In this paper, we propose to change the traditional monitored statistic in a control chart p, by changing the sampling proportion ${\hat{p}}$ to a new statistics denoted as ${\tilde{p}}$ . We aim to minimize problems in designing the control chart p for high quality processes when only a small sample size is available. The idea of the new statistics is simple, as it involves taking two independent samples of a Bernoulli population. From each sample, the sampling proportion is calculated, and the new statistic to monitor is the weighted mean of the sampling proportion of each sample employed to weight the overall sampling proportion. We note that the control chart p that employs the new ${\tilde{p}}$ statistic provides more in-control values of average run length closer to the usual fixed value of 370 than the traditional statistic, that is, the sampling proportion. Numerical examples illustrate the new proposal.     

A Bayesian Markov-Switching Correlation Model for Contagion Analysis on Exchange Rate Markets

This article develops a new Markov-switching vector autoregressive (VAR) model with stochastic correlation for contagion analysis on financial markets. The correlation and the log-volatility dynamics are driven by two independent Markov chains, thus allowing for different effects such as volatility spill-overs and correlation shifts with various degrees of intensity. We outline a suitable Bayesian inference procedure based on Markov chain Monte Carlo algorithms. We then apply the model to some major and Asian-Pacific cross rates against the U.S. dollar and find strong evidence supporting the existence of contagion effects and correlation drops during crises, closely in line with the stylized facts outlined in the contagion literature. A comparison of this model with its closest competitors, such as a time-varying parameter VAR, reveals that our model has a better predictive ability. Supplementary materials for this article are available online     

A Stochastic Volatility Model With Realized Measures for Option Pricing

Abstract

Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence—the crucial parameter in pricing Standard and Poor’s 500 Index options.     

Inferences in dichotomous classifications with misclassifications based on sequential repetitive classifications

To create inferences in dichotomous classifications with misclassifications and possibly perform repeated classifications, the maximum likelihood method is commonly used, mainly because of its efficiency in obtaining parameter estimators of a mixture of two binomial distributions. One simpler alternative that is operationally easier is to consider the simple majority method. In this method, each of n items are classified r times as conforming or non-conforming. The final classification of the item is determined by the most frequent class. This method yielded lower mean squared errors than the maximum likelihood and the moments estimators and is asymptotically efficient. In this paper, we introduce a new approach in which the realization of all r repeated classifications of each item may not be needed. Each of n items is sequentially classified as conforming or nonconforming, and the process ceases when the frequency of conforming or non-conforming classification reaches the integer a. We show that, by a Monte Carlo simulation, the last procedure presents a lower mean squared error than the simple majority results for a similar number of r repeated classifications.     

Education and gender bias in the sex ratio at birth: Evidence from India

This article investigates the possible existence of a nonlinear link between female disadvantage in natality and education. To this end, we devise a theoretical model based on the key role of social interaction in explaining people’s acquisition of preferences, which justifies the existence of a nonmonotonic relationship between female disadvantage in natality and education. The empirical validity of the proposed model is examined for the case of India, using district-level data. In this context, our econometric analysis pays particular attention to the role of spatial dependence to avoid any potential problems of misspecification. The results confirm that the relationship between the sex ratio at birth and education in India follows an inverted U-shape. This finding is robust to the inclusion of additional explanatory variables in the analysis, and to the choice of the spatial weight matrix used to quantify the spatial interdependence between the sample districts.     

A Matter of Learning: How Human Resources Affect Organizational Performance

Recently, a number of studies have tried to examine the processes that explain the influence of high performance work systems (HPWS) on company performance, in an attempt to understand which variables mediate this relationship and to what extent they do so. The importance of the organizational learning capability (OLC) construct has traditionally been outlined as being essential for a company's survival and effective performance. Thus, it seems important to establish whether HPWS can be considered an antecedent of OLC, and consequently to confirm whether OLC acts as a mediating variable in the HPWS–company performance linkage. Bearing in mind that HPWS represent a ‘bundle’ of mutually reinforcing, overlapping and synergistic individual human resource practices, this positive connection between HPWS and OLC seems reasonable. We tested our hypotheses by applying a structural equation methodology to a sample of 163 Spanish companies. Our findings show that the effects of HPWS on organizational performance are mediated by OLC.