Markov regime switching in mean and in fractional integration parameter

We propose a specific general Markov-regime switching estimation both in the long memory parameter d and the mean of a time series. We employ Viterbi algorithm that combines the Viterbi procedures in two state Markov-switching parameter estimation. It is well-known that existence of mean break and long memory in time series can be easily confused with each other in most cases. Thus, we aim at observing the deviation and interaction of mean and d estimates for different cases. A Monte Carlo experiment reveals that the finite sample performance of the proposed algorithm for a simple mixture model of Markov-switching mean and d changes with respect to the fractional integrating parameters and the mean values for the two regimes.     

Applying Islamic Norms in Europe: The Example of Bosnian Muslims

Abstract

Is Islamic law still valid in Europe? This paper argues “yes”—though not in the form of hard “law” but rather in the form of soft “norms” which are not state-sanctioned, but still carry heavy significance for practicing Muslims. The paper examines cases where Islamic moral, ethical and in some cases legal norms can be applied in a secular country without clashing with state laws. It further demonstrates that, based on fatawa issued by Islamic scholars in Bosnia and Herzegovina, Islamic norms may still apply for Muslims living in secular European states, although they are not legally binding. This may be illustrated by classifying norms into religious (God's commandment to fast, pray, give alms), moral–ethical (consumption of alcohol, dressing properly) and Islamic legal norms (marriage, divorce, inheritance). The conclusion is that a vast majority of these norms can be adhered to by Muslims either within the scope of guaranteed religious freedoms in civil society or may otherwise be applied without clashing with secular civil laws.     

Using the Diffusion of Innovation Concept to Explain the Factors That Contribute to the Adoption Rate of E-journal Publishing

This paper reports on the factors that contribute to the adoption rate of e-journal publishing based upon responses from 82 Malaysian journal publishers. Drawing upon the theory of innovation diffusion, the study examines the role of awareness, three organization variables (publication size, age of the journal, and experience of editors), and attributes of e-journals (relative advantage, compatibility, complexity, trialability and observability) as influences to the rate of e-journal publishing adoption. Findings show that only two attributes—complexity and trialability—emerge as significant contributors to e-journal adoption rates, and all three organization variables—journal's age, publication size, and editorial experience—are significant. Collectively, these five significant variables explained 57.8% of the variance in adoption rate. We discuss the results and implication of the findings with respect to the wider context of e-journal publishing.     

A Bayesian semiparametric method for analyzing length-biased data

Survival data obtained from prevalent cohort study designs are often subject to length-biased sampling. Frequentist methods including estimating equation approaches, as well as full likelihood methods, are available for assessing covariate effects on survival from such data. Bayesian methods allow a perspective of probability interpretation for the parameters of interest, and may easily provide the predictive distribution for future observations while incorporating weak prior knowledge on the baseline hazard function. There is lack of Bayesian methods for analyzing length-biased data. In this paper, we propose Bayesian methods for analyzing length-biased data under a proportional hazards model. The prior distribution for the cumulative hazard function is specified semiparametrically using I-Splines. Bayesian conditional and full likelihood approaches are developed for analyzing simulated and real data.     

DOES LIMITED PUNISHMENT LIMIT THE SCOPE FOR CROSS RETALIATION?

This paper analyzes two prominent institutional rules in the international trading system: a limited cross‐retaliation rule characterized by the Understanding on Rules and Procedures Governing the Settlement of Disputes (DSU) Article 22.3 and a limited punishment rule characterized by the General Agreement on Tariffs and Trade (GATT) Article XXVIII. In general, both rules are designed to limit the countermeasures upon a violation; however, the former rule specifies the limits of composition in retaliation, whereas the latter one designates the limits of retaliation magnitude. We show that, albeit seemingly unrelated, the limited cross‐retaliation rule complements the limited punishment rule in permitting greater trade liberalization. Specifically, we show how the limited cross‐retaliation rule also helps limit the incentives to violate the trade agreement when the limited punishment rule prevails. (JEL F13, K33, C73)     

Mehr Kinderlose oder weniger Kinderreiche?Increasing childlessness or fewer families with many children?

The decline of fertility in the course of the Second Demographic Transition is mainly caused by both an increase in childlessness and a decline of large families (three or more births). Which of these effects are stronger in Germany has been contested for a long time. An exact quantification of the particular effects is still missing. In this paper we develop a decomposition method which allows calculating the effects of the particular parity changes between cohort total fertility rates (CTFR) of different cohorts. This method is applied in order to compare German CTFRs for the cohorts the fertility decline occurred in, namely the cohorts between 1933 and 1968. The analyses are differentiated by regions and periods. The CTFR decline can be separated into four components: increasing childlessness, decreasing shares of higher order births (3+), a combined effect and a changing relation of first and second births. In Germany, the effect of the increasing childlessness accounts for 25.9 per cent of the CTFR decline, the decreasing higher order births for 68.0 per cent and the combined effect for 6.1 per cent. The relation of first and second births changed only marginally. Remarkably, these figures are based on two entirely different periods: The CTFR decline between the cohorts of 1933 and 1947 is solely based on the decline of women with three or more children. However, two thirds of the CTFR decline since 1947 is caused by an increasing childlessness. The results are relevant for fertility theories because increasing childlessness and decreasing higher order births have different reasons.     

Critical appraisal of Bayesian dynamic borrowing from an imperfectly commensurate historical control

Bayesian dynamic borrowing designs facilitate borrowing information from historical studies. Historical data, when perfectly commensurate with current data, have been shown to reduce the trial duration and the sample size, while inflation in the type I error and reduction in the power have been reported, when imperfectly commensurate. These results, however, were obtained without considering that Bayesian designs are calibrated to meet regulatory requirements in practice and even no‐borrowing designs may use information from historical data in the calibration. The implicit borrowing of historical data suggests that imperfectly commensurate historical data may similarly impact no‐borrowing designs negatively. We will provide a fair appraiser of Bayesian dynamic borrowing and no‐borrowing designs. We used a published selective adaptive randomization design and real clinical trial setting and conducted simulation studies under varying degrees of imperfectly commensurate historical control scenarios. The type I error was inflated under the null scenario of no intervention effect, while larger inflation was noted with borrowing. The larger inflation in type I error under the null setting can be offset by the greater probability to stop early correctly under the alternative. Response rates were estimated more precisely and the average sample size was smaller with borrowing. The expected increase in bias with borrowing was noted, but was negligible. Using Bayesian dynamic borrowing designs may improve trial efficiency by stopping trials early correctly and reducing trial length at the small cost of inflated type I error.