Correction to: Patronage and Public Goods Provisioning in an Unequal Land

Social Indicators Research - The article Patronage and Public Goods Provisioning in an Unequal Land, written by Hadia Majid and Rashid Memon, was originally published electronically on the...     

The Roman Catholic Church: A Centuries Old History of Awareness of Clerical Child Sexual Abuse (from the First to the 19th Century)

Debates in international forums and in mainstream media on the role, responsibility, liability, and response of ecclesiastical authorities of the Roman Catholic Church (RCC) toward clerical child sexual abuse (cCSA) fail to take into account the historical roots and awareness of the problem. Reports also fail to mention the historic organizational laws RCC developed over centuries. In contrast, RCC documents evidence that the Catholic Church not only carried century’s old history of cCSA, but also repeatedly condemned cCSA by successive papal authorities, organizational laws, and institutional management mechanisms. During the first millennium, however, church laws remained confined to the bookshelves and were not converted into appropriate management policies and infrastructural models. This was largely due to the absence of a central administrative organizational structure, which developed later in the 12th century, following the Second Council of Lateran (1139) when the Papacy asserted its authority to establish administrative control over the organizational church. It was only then that management policies started to be framed and institutional structures enacted to deal more appropriately with cCSA from the 14th to 20th centuries. Despite this, RCC developed a culture of secrecy using clandestine organizational management models and institutional laws prescribed in 1568, 1622, 1741, 1866, 1922, and 1962 which aimed to manage cCSA. The current study traces reported cCSA as far back as the first century and critically examines the organizational laws, and institutional policies developed by RCC to address clerical sexual misconduct up to the end of the 19th century.     

Rank-based analysis of repeated measures block designs

A rank-based inference is developed for repeated measures balanced incomplete block and randomized complete block designs using a suitable dispersion function. Asymptotic distributions of rank estimators are developed after establishing approximate linearity of the gradient vector of the dispersion function. Unlike available nonparametric procedures for those designs, estimation and testing are tied together. Three different test statistics are developed for testing the linear hypotheses. Friedman's (1937) statistic and Durbin's (1951) statistic are particular cases of one of the three proposed statistics. An estimate of a scale parameter which appears in the ARE expression as well as as in the variences and covariances of the rank estimators is discussed.     

On the Testability of Identification in Some Nonparametric Models With Endogeneity

This paper examines three distinct hypothesis testing problems that arise in the context of identification of some nonparametric models with endogeneity. The first hypothesis testing problem we study concerns testing necessary conditions for identification in some nonparametric models with endogeneity involving mean independence restrictions. These conditions are typically referred to as completeness conditions. The second and third hypothesis testing problems we examine concern testing for identification directly in some nonparametric models with endogeneity involving quantile independence restrictions. For each of these hypothesis testing problems, we provide conditions under which any test will have power no greater than size against any alternative. In this sense, we conclude that no nontrivial tests for these hypothesis testing problems exist.     

Control charts for location based on different sampling schemes

Control charts are the most important statistical process control tool for monitoring variations in a process. A number of articles are available in the literature for the X? control chart based on simple random sampling, ranked set sampling, median-ranked set sampling (MRSS), extreme-ranked set sampling, double-ranked set sampling, double median-ranked set sampling and median double-ranked set sampling. In this study, we highlight some limitations of the existing ranked set charting structures. Besides, we propose different runs rules-based control charting structures under a variety of sampling strategies. We evaluate the performance of the control charting structures using power curves as a performance criterion. We observe that the proposed merger of varying runs rules schemes with different sampling strategies improve significantly the detection ability of location control charting structures. More specifically, the MRSS performs the best under both single- and double-ranked set strategies with varying runs rules schemes. We also include a real-life example to explain the proposal and highlight its significance for practical data sets.     

RBI’s Monetary Policy and Macroeconomic Announcements: Impact on S&P CNX Nifty VIX

This study investigates the information content of RBI’s monetary policy and macroeconomic announcements and its impact on the implied volatility index. The empirical findings suggest that implied volatility (VIX) increases prior to the scheduled macroeconomic announcements. This study takes into account the RBI’s (Reserve Bank of India) monetary policy in the form of monetary credit information review meeting days, consumer price index, wholesale/producers price index, index of industrial production, employment report, gross domestic product. The empirical result explains that investors regard the scheduled macroeconomic announcements in the valuation of their financial assets. More specifically study shows that investors jointly consider the MCIR and GDP reports for their financial planning. This study contributes in two ways; it is useful in the forecasting of short-term market volatility and pricing of Options.     

Using principal components to test normality of high-dimensional data

Many multivariate statistical procedures are based on the assumption of normality and different approaches have been proposed for testing this assumption. The vast majority of these tests, however, are exclusively designed for cases when the sample size n is larger than the dimension of the variable p, and the null distributions of their test statistics are usually derived under the asymptotic case when p is fixed and n increases. In this article, a test that utilizes principal components to test for nonnormality is proposed for cases when p/n → c. The power and size of the test are examined through Monte Carlo simulations, and it is argued that the test remains well behaved and consistent against most nonnormal distributions under this type of asymptotics.