Fear of Floating and Pegging: A Simultaneous Choice Model of De Jure and De Facto Exchange Rate Regimes in Developing Countries

We present an analysis of the determinants of de jure and de facto exchange rate regimes based on a panel probit model with simultaneous equations. The model is estimated using simulation-based maximum likelihood methods. The empirical results suggest a triangular structure of the model such that the choice of de facto regimes depends on the choice of de jure regimes but not vice versa. This gives rise to a novel interpretation of regime discrepancies.

Effectiveness of harvest strategies in achieving multiple management objectives in a multispecies fishery

Fisheries management is characterised by multiple objectives, some of which may be complementary, while others may require trade‐offs between outcomes. Balancing these objectives is made more complex in the case of multispecies and multigear fisheries. In this paper, we develop a bioeconomic model that captures the key elements of such a fishery to test a range of potential harvest strategies to provide insights into how economic target reference points could lead to both desirable and undesirable management outcomes (e.g. discards). The model is developed as a long‐run optimisation model to identify target reference points to achieve multispecies maximum economic yield, and a dynamic recursive optimisation model, which includes more realistic representation of fishers’ behaviour, such as discards and trading of under‐caught species quotas. The potential economic, social and ecological impacts are evaluated using data envelopment analysis (DEA). The results suggest that the use of proxy target reference points can result in short‐term economic benefits at the cost of slower stock recovery and higher discarding. Limiting the number of species subject to quota controls may also prove beneficial in multispecies fisheries, while ensuring quota markets are efficient is likely to produce benefits irrespective of the harvest strategy adopted.     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.     

Distribution of residual autocorrelations for multiplicative seasonal ARMA models with uncorrelated but nonindependent error terms

In this paper, we consider portmanteau tests for testing the adequacy of multiplicative seasonal autoregressive moving‐average models under the assumption that the errors are uncorrelated but not necessarily independent. We relax the standard independence assumption on the error terms in order to extend the range of applications of the seasonal autoregressive moving‐average models. We study the asymptotic distributions of residual and normalized residual empirical autocovariances and autocorrelations under weak assumptions on noise. We establish the asymptotic behavior of the proposed statistics. A set of Monte Carlo experiments and an application to monthly mean total sunspot number are presented.     

Performance Measures in Dose-Finding Experiments

In the first phase of pharmaceutical development, and assuming that the probability of positive response increases with dose, the main statistical goal is to estimate a percentile of the dose–response function for a given target value $\mathrm{\Gamma }$. We compare the Maximum Likelihood and centred isotonic regression estimators of the target dose and we discuss several performance criteria to assess inferential precision, the amount of toxicity exposure and the trade-off between them for a set of some exemplary adaptive designs. We compare these designs using graphical tools. Several scenarios are considered using simulation, including the use of several start-up rules, the change of slope of the dose-toxicity function at the target dose and also different theoretical models, as logistic, normal or skew-normal distribution functions.     

Incentives and teacher effort

Few would contest that teachers are a very important determinant of how much students learn in school, and how to improve teacher performance has been the focus of lively policy debate in both rich and poor countries. This paper examines how teacher incentives, both pecuniary and non‐pecuniary, correlate with teacher effort. Using school survey data from Lao PDR, we estimate measures of teacher effort, including the number of hours that teachers spend preparing for classes and teacher provision of private tutoring outside of class hours, which are not the typical measures used in previous research. Estimation results fit well under the standard labour supply framework and indicate that greater teacher effort is associated with non‐pecuniary incentives such as more teacher autonomy over teaching materials and monitoring as measured by the existence of an active parent – teacher association and the ability of school principals to dismiss teachers. Methodologically, this paper provides a detailed derivation of a simultaneous OLS‐probit model with school random effects that can jointly estimate teacher work hours and tutoring provision.     

用益损矩阵法对采购物流进行风险决策的优化研究

从定性及定量分析相结合的角度,采用益损矩阵预测分析法,对如何实现采购物流的最优化进行风险决策分析。通过建立采购物流风险决策模型,并列举实例进行量化分析,得到采购的损益期望值,通过选取最大期望值确定最优的采购方案。同时指出为更好的实现采购物流的优化管理应加强的几方面工作。     

刍议工程招标中拦标价的设立

针对目前工程量清单招标项目中设立拦标价的做法是否合法合理,以及如何准确计算拦标价仍存在一定的争议。论述了拦标价设立的目的、作用、法规依据和造价依据以及拦标价在应用中存在的一些问题,提出了一种基于概率论的知识来确定拦标价上浮比例的方法,并指出拦标价在招标和评标应用中需要注意的问题。     

公差原则及其应用

对ＧＢ／Ｔ４２４９－１９９６《公差原则》的图样标注、应用范围、检验等学习体会予以交流。     

快速校正天波雷达电离层相位污染的改进方法

时变的电离层会对天波超视距雷达(OTHR)回波信号相位进行调制,产生相位污染,导致回波谱展宽。最大似然估计(MLE)法具有比相位梯度法更佳的污染校正效果,但计算量非常大。通过引入投影近似子空间跟踪法,提出了一种改进的MLE方法。改进方法采用递归手段估计最大特征值对应的特征向量,避免了特征值分解过程,能够显著降低计算量,污染校正效果与MLE法相当。理论分析与仿真对比表明改进方法普适性强,计算量只有MLE法的万分之一,更适合工程实现。