How to Reduce the Impact of Equivalence Scales on Poverty Measurement: Evidence from Turkey

The main aim of the paper is to contribute to the poverty measurement literature by demonstrating a method to reduce the impact of equivalence scales in poverty measurement. This is accomplished by choosing the most appropriate reference household type. The results showed that one adult household is certainly not suitable for being the reference household type. When one adult household is set as the reference, in the range of no equivalence scale and per capita equivalence scale, poverty head count ratio changes from 1 to 48 %, showing the huge effect of the equivalence scale choice. Also the analyses at household size level showed that one adult household type is not convenient to be the reference household type. On the other hand, no clear distinction could be made between central household types, but the importance of choosing a household type close to the center was demonstrated for Turkish data.     

Sensing Abnormal Resource Flow Using Adaptive Limit Process Charts in a Complex Supply Network*

Supply networks are becoming increasingly complex with multiple overlapping relationships between firms that may span across industries. Consequently, inventory management is becoming more difficult as managers have to cope with variability in the supply flows that originate from different parts of the network. Managers that quickly sense abnormal flows may intervene and adapt their inventory policies in response to system changes. In this article, we present a framework for sensing abnormal flows originating within the upstream supply network of a focal organization. Our framework combines time series modeling with process charts to identify abnormal flow patterns in the incoming supply streams. It is a flexible framework that uses off‐the‐shelf technology to provide managers with a process that can be employed for monitoring multiple individual or aggregated data streams originating within any complex system such as complex adaptive supply networks. We illustrate our framework on four years of longitudinal supply data from the second largest food bank in the United States. We identify multiple instances of abnormal supply flows and validate our results through rigorous inventory analysis as well as field‐based expert interviews. We discuss the implications of our findings for inventory management in complex supply networks, both from academic and practitioner points of view.     

The effect of Phase I sample size on the run length performance of control charts for autocorrelated data

Traditional control charts assume independence of observations obtained from the monitored process. However, if the observations are autocorrelated, these charts often do not perform as intended by the design requirements. Recently, several control charts have been proposed to deal with autocorrelated observations. The residual chart, modified Shewhart chart, EWMAST chart, and ARMA chart are such charts widely used for monitoring the occurrence of assignable causes in a process when the process exhibits inherent autocorrelation. Besides autocorrelation, one other issue is the unknown values of true process parameters to be used in the control chart design, which are often estimated from a reference sample of in-control observations. Performances of the above-mentioned control charts for autocorrelated processes are significantly affected by the sample size used in a Phase I study to estimate the control chart parameters. In this study, we investigate the effect of Phase I sample size on the run length performance of these four charts for monitoring the changes in the mean of an autocorrelated process, namely an AR(1) process. A discussion of the practical implications of the results and suggestions on the sample size requirements for effective process monitoring are provided.     

Shipping Fees or Shipping Free? A Tale of Two Price Partitioning Strategies in Online Retailing

In this article, we study the price partitioning decisions of online retailers regarding shipping and handling (S&H) fees. Specifically, we analyze two partitioning formats used by retailers in this context. In the first scenario, retailers present customers with a price that is partitioned into a product price and a separate S&H surcharge (the PS strategy); in the second, customers are offered free shipping through a non‐partitioned format where the product price already includes the shipping cost (the ZS strategy). We first develop a stylized game‐theoretic model that captures the competitive dynamics between (and within) these two formats. Analysis of the model provides insights into how both firm and product level characteristics drive a retailer's strategic choice regarding which partitioning format to adopt and, hence, determines the equilibrium market structure in terms of proportion of ZS and PS retailers. Subsequently, we conduct empirical analyses, based on product and S&H prices data for two different product categories (digital cameras and printers) collected from online retailers, to validate all the results of our theoretical model. We establish that PS retailers charge lower product prices than ZS ones, but the total price (product + S&H) charged is higher for the first group. The S&H charge for PS retailers can be significant—it is, on average, 5.4% (printers) and 3.0% (digital cameras) for our two product categories. Furthermore, retailers which are popular and/or face risky cost environment are more likely to opt for the ZS strategy, while retailers whose portfolio mostly includes large or heavy products with high cost (S&H)‐to‐price ratios usually choose the PS strategy. Lastly, our empirical study also illustrates that the price adjustment behavior of retailers is affected by their shipping‐fee policies—for example, ZS retailers change their product prices almost 1.5 times more frequently than PS ones.     

A New Estimation Method Based on Order Statistics in the Families of Symmetric Location-scale Distributions

In this study, new unbiased and nonlinear estimators based on order statistics are proposed for the family of symmetric location-scale distributions and these estimators can be computed from both uncensored and symmetric doubly Type II censored samples. In addition, other relevant unbiased estimators are proposed to estimate standard deviations of these new estimators. A simulation study has been performed to evaluate the performance of the new estimators compared to BLU estimators for small sample sizes. As a result of the simulation study, the new estimators proposed for the location-scale family in general performed nearly as good as BLU estimators. Furthermore, the computational advantage of the proposed estimators over BLU and ML estimators are worthy of notice. In addition, these new estimators have been applied to real data, and the estimation results obtained have been compatible with those of BLUE methods.     

Selecting the Correct Number of Factors in Approximate Factor Models: The Large Panel Case With Group Bridge Estimators

This article proposes a group bridge estimator to select the correct number of factors in approximate factor models. It contributes to the literature on shrinkage estimation and factor models by extending the conventional bridge estimator from a single equation to a large panel context. The proposed estimator can consistently estimate the factor loadings of relevant factors and shrink the loadings of irrelevant factors to zero with a probability approaching one. Hence, it provides a consistent estimate for the number of factors. We also propose an algorithm for the new estimator; Monte Carlo experiments show that our algorithm converges reasonably fast and that our estimator has very good performance in small samples. An empirical example is also presented based on a commonly used U.S. macroeconomic dataset.     

Adaptive Elastic Net for Generalized Methods of Moments

Model selection and estimation are crucial parts of econometrics. This article introduces a new technique that can simultaneously estimate and select the model in generalized method of moments (GMM) context. The GMM is particularly powerful for analyzing complex datasets such as longitudinal and panel data, and it has wide applications in econometrics. This article extends the least squares based adaptive elastic net estimator by Zou and Zhang to nonlinear equation systems with endogenous variables. The extension is not trivial and involves a new proof technique due to estimators’ lack of closed-form solutions. Compared to Bridge-GMM by Caner, we allow for the number of parameters to diverge to infinity as well as collinearity among a large number of variables; also, the redundant parameters are set to zero via a data-dependent technique. This method has the oracle property, meaning that we can estimate nonzero parameters with their standard limit and the redundant parameters are dropped from the equations simultaneously. Numerical examples are used to illustrate the performance of the new method.     

Threshold Autoregression with a Unit Root

This paper develops an asymptotic theory of inference for an unrestricted two‐regime threshold autoregressive (TAR) model with an autoregressive unit root. We find that the asymptotic null distribution of Wald tests for a threshold are nonstandard and different from the stationary case, and suggest basing inference on a bootstrap approximation. We also study the asymptotic null distributions of tests for an autoregressive unit root, and find that they are nonstandard and dependent on the presence of a threshold effect. We propose both asymptotic and bootstrap‐based tests. These tests and distribution theory allow for the joint consideration of nonlinearity (thresholds) and nonstationary (unit roots). Our limit theory is based on a new set of tools that combine unit root asymptotics with empirical process methods. We work with a particular two‐parameter empirical process that converges weakly to a two‐parameter Brownian motion. Our limit distributions involve stochastic integrals with respect to this two‐parameter process. This theory is entirely new and may find applications in other contexts. We illustrate the methods with an application to the U.S. monthly unemployment rate. We find strong evidence of a threshold effect. The point estimates suggest that the threshold effect is in the short‐run dynamics, rather than in the dominate root. While the conventional ADF test for a unit root is insignificant, our TAR unit root tests are arguably significant. The evidence is quite strong that the unemployment rate is not a unit root process, and there is considerable evidence that the series is a stationary TAR process.     

Lifetime of Prevalence and Risk Factors of Problem and Pathologic Gambling in North Cyprus

In this article, the results of the national survey of adult gambling behavior in North Cyprus (NC) in 2012 are presented. The aim of this study is to investigate the characteristics of adults’ participation in gambling, and to determine the prevalence of ‘problem and pathological gambling’ in NC. The population of this study was formed from all the people living permanently in NC, speaking Turkish, and within the age group 18–65. Household interviews were conducted with 966 people. To obtain data, a 30 item questionnaire prepared by the researchers and a Turkish version of the Revised South Oaks Gambling Screen were used. Prevalence rates are compared with the results of the study conducted in 2007 using the same methodology and survey form. The lifetime prevalence of participating at least once in any of the 17 gambling activities investigated in the survey was 66.4 %. 3.5 % of the respondents scored as lifetime probable pathological gamblers and 9.2 % as probable problem gamblers. Risk factors for becoming probable problem and pathological gamblers include being male, being in the 19–28 age group, having a high education level, having a job and being born in Cyprus. This study shows that the prevalence of problem gambling is high in NC and increasing gradually. NC has socio-cultural features such as a history of colonization, socioeconomic problems and high unemployment, similar to other high prevalence gambling regions, which is suggestive of the importance of socio-cultural factors on gambling behavior.