Assessing non-tariff barriers in Syria

International trade in Syria is highly regulated through a combination of tariffs and non-tariff barriers. At 8% of the value of imports on average, effective tariffs are relatively low. However, non-tariff barriers to trade actually make Syria's trade restrictiveness very high. Comparing world and domestic prices of imports indeed suggests that non-tariff barriers increase the domestic price of imported goods by 17% on average, notably the result of significant quantitative restrictions. Using a computable general equilibrium model, the costs of NTBs on the Syrian economy are assessed. Simulations suggest that reallocation gains resulting from a complete removal of NTBs could be substantial. Accordingly, the key message from the analysis is that trade reform if it focuses only on tariff reduction will have limited growth benefits. On the contrary, if the Government abolishes the widespread non-tariff barriers to trade, including the elimination of quantitative trade restrictions, trade policy can become the central instrument to redress Syria's growth prospects.     

Performance measurement: a critical analysis of the literature with respect to total quality management

This paper represents a comprehensive review of the literature on performance measurement. The roadmap used for this analysis is from the evolution of performance measurement, to the way it is currently defined and how it is applied from a corporate-wide perspective. One useful contribution of this review is a special focus on performance measurement in a non-financial, non-traditional sense. This discussion was further amplified by referring to newly developed tools and techniques and by highlighting critical factors of success in the application of performance measurement.
The key factors from this literature review reveal that, although interest in the area of measurement is growing significantly, this is not reflected by the tangible and credible experiences in the organizations concerned. There is a concurrent view amongst various writers that there are both hard and soft issues reflecting, for instance, the lack of strategic thinking and goal clarity, putting measurement systems in the driving seat and therefore trusting unreliable designs and not focusing on the true ethos of modern measurement towards continuous improvement and thereby not engaging employees and neglecting the culture change aspects.
The paper concludes by highlighting the dearth in research in the important field of performance measurement and, in particular, the design and implementation of performance measurement systems that cover all the essential aspects through an integrated perspective.     

The local geographies of welfare in Tunisia: Does neighbourhood matter?*

Amara M, Ayadi M. The local geographies of welfare in Tunisia: Does neighbourhood matter? The aim of this article is to show that spatial analysis techniques outperform non‐spatial statistical counterparts for understanding the geographic determinants of welfare and poverty in Tunisia. First, an Exploratory Spatial Data Analysis, based on a Geographical Information System, was conducted to visualise the local spatial structure of welfare. Second, a spatial autoregressive (SAR) model and a geographically weighted regression (GWR) model, respectively, were used to deal with both spatial autocorrelations and unobserved spatial heterogeneity of households' behaviours. Spatial and non‐spatial models were compared according to their predictive performances. Results of this case study confirm that SAR and GWR spatial models are preferable to the traditional non‐spatial regression model and that they give a better approximation of the Tunisian poverty map.     

Some tests for the power series distibutions in one parameter using the kullback-leibler information measure

The purpose of the article is, in case of one sample, to obtain tests concerning the parameter in the power series distribution in one parameter using Ku11back-Leibier information measure. The class of power series distibutions contains a host of discrete distributions. Ve illustrate the general results obtained in case of the geometric distibution.     

Asymptotic expansions for sequential confidence levels in inverse linear regression

Non-linear renewal theory is used to derive second order asymptotic expansions for the coverage probability of a fixed-width sequential confidence interval for an unknown parameter xin the inverse linear regression model. These expansions are obtained for a two-stage sequential procedure, proposed by Perng and Tong (1974) for the construction of a confidence interval for x.     

Sparse conformal predictors

Conformal predictors, introduced by Vovk et al. (Algorithmic Learning in a Random World, Springer, New York, 2005), serve to build prediction intervals by exploiting a notion of conformity of the new data point with previously observed data. We propose a novel method for constructing prediction intervals for the response variable in multivariate linear models. The main emphasis is on sparse linear models, where only few of the covariates have significant influence on the response variable even if the total number of covariates is very large. Our approach is based on combining the principle of conformal prediction with the ℓ ₁ penalized least squares estimator (LASSO). The resulting confidence set depends on a parameter ε>0 and has a coverage probability larger than or equal to 1−ε. The numerical experiments reported in the paper show that the length of the confidence set is small. Furthermore, as a by-product of the proposed approach, we provide a data-driven procedure for choosing the LASSO penalty. The selection power of the method is illustrated on simulated and real data.     

Modeling longitudinal count data with dropouts

This paper explores the utility of different approaches for modeling longitudinal count data with dropouts arising from a clinical study for the treatment of actinic keratosis lesions on the face and balding scalp. A feature of these data is that as the disease for subjects on the active arm improves their data show larger dispersion compared with those on the vehicle, exhibiting an over‐dispersion relative to the Poisson distribution. After fitting the marginal (or population averaged) model using the generalized estimating equation (GEE), we note that inferences from such a model might be biased as dropouts are treatment related. Then, we consider using a weighted GEE (WGEE) where each subject's contribution to the analysis is weighted inversely by the subject's probability of dropout. Based on the model findings, we argue that the WGEE might not address the concerns about the impact of dropouts on the efficacy findings when dropouts are treatment related. As an alternative, we consider likelihood‐based inference where random effects are added to the model to allow for heterogeneity across subjects. Finally, we consider a transition model where, unlike the previous approaches that model the log‐link function of the mean response, we model the subject's actual lesion counts. This model is an extension of the Poisson autoregressive model of order 1, where the autoregressive parameter is taken to be a function of treatment as well as other covariates to induce different dispersions and correlations for the two treatment arms. We conclude with a discussion about model selection. Published in 2009 by John Wiley & Sons, Ltd.     

Detection of outliers in simple circular regression models using the mean circular error statistic

The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we propose a new numerical statistic called mean circular error to identify possible outliers in circular regression models by using a row deletion approach. Through intensive simulation studies, the cut-off points of the statistic are obtained and its power of performance investigated. It is found that the performance improves as the concentration parameter of circular residuals becomes larger or the sample size becomes smaller. As an illustration, the statistic is applied to a wind direction data set.     

Optimum grouping and the boundary problem

Given a set of n elements or observations that form a continuous variable, it is required to divide their distribution into k homogenous groups where k > 2, and the purpose is to minimize the within-groups variance. This paper investigates procedures for such a division and shows how to find the boundaries separating the groups.