中美贸易顺差与人民币汇率关系的实证分析

针对中美贸易顺差与人民币汇率关系的问题,本文通过协整分析和Granger因果检验发现,人民币升值在短期难以对中美贸易顺差产生调节作用,从长期影响而言,人民币升值的影响作用也不大。此外,中美贸易顺差在短期还是主要取决于当前贸易收支的现实情况,而中长期则在很大程度上受到美国经济波动的影响。因此,人民币升值不会缓解美国巨大的贸易逆差,而且即使美国贸易收支出现改善也不会对美国经济发展产生显著的正面影响。     

Measuring firm performance: Differentiating between uncontrollable and controllable bad outputs

This paper introduces a new model that differentiates between controllable and uncontrollable bad outputs in the measurement of firm performance; the airline industry is used as the empirical example. The model allows us to measure technical inefficiency, as well as the inefficiency in the production of bad outputs, and the inefficiency in the investments allocated to the reduction of uncontrollable bad outputs. Moreover, we provide a direct measure of the total cost of controllable bad outputs. As the model involves a number of latent (unobservable) variables that cannot be easily integrated out of the likelihood function without producing complicated expressions, we resort to Bayesian techniques. We find notable differences in inefficiency across airlines. More importantly though, we find that the production of bad or undesirable outputs impose significant costs on airlines. However, these cost issues could be reduced if airlines invest more in maintenance and service training.     

Selection of influential variables in ordinal data with preponderance of zeros

Presence of excess zero in ordinal data is pervasive in areas like medical and social sciences. Unfortunately, analysis of such kind of data has so far hardly been looked into, perhaps for the reason that the underlying model that fits such data, is not a generalized linear model. Obviously some methodological developments and intensive computations are required. The current investigation is concerned with the selection of variables in such models. In many occasions where the number of predictors is quite large and some of them are not useful, the maximum likelihood approach is not the automatic choice. As, apart from the messy calculations involved, this approach fails to provide efficient estimates of the underlying parameters. The proposed penalized approach includes ?₁ penalty (LASSO) and the mixture of ?₁ and ?₂ penalties (elastic net). We propose a coordinate descent algorithm to fit a wide class of ordinal regression models and select useful variables appearing in both the ordinal regression and the logistic regression based mixing component. A rigorous discussion on the selection of predictors has been made through a simulation study. The proposed method is illustrated by analyzing the severity of driver injury from Michigan upper peninsula road accidents.     

Is DNA fingerprinting the gold standard for estimation of adoption and impacts of improved lentil varieties?

In the early 1980s, disease susceptibility in short-season lentil landraces began to limit productivity in areas where relay cropping took place in Bangladesh. Since then, several improved high-yielding lentil varieties, which are resistant to rust and blight and suitable in the relay cropping system, have been released jointly by national and international research centers. This study used three methods, namely a panel of experts, a survey of 1000 households where the respondents named the variety they used, and DNA fingerprinting of seed samples collected from all lentil plots cultivated by survey households to estimate adoption. Double hurdle and instrumental variables regression methods were applied to the household survey and DNA fingerprinting data to identify determinants of adoption and measure their impacts. Of particular interest was whether estimates of adoption, determinants of adoption and impacts varied by method of variety identification. Results showed that the expert panel overestimated the adoption of more recent varieties while about 89% of the farmer-reported varieties were accurate, as verified by DNA fingerprinting. DNA fingerprinting appears to have little advantage for estimating the level of adoption in this case, where few varieties of lentils are found, local variety names do not exist, and most seed is obtained through a formal system. However, even under these conditions, determinants of adoption vary by identification method, and use of farmer-reported information on the variety can lead to erroneous conclusions about determinants of adoption. Because recent breeding efforts have focused on taste and cooking considerations, yield impacts were not significantly different from zero.     

Some Variants of Constrained Estimation in Finite Population Sampling

This is a review article that unifies several important examples using constrained optimisation techniques. The basic tools are three simple mathematical optimisation results subject to certain constraints. Applications include calibration, benchmarking in small area estimation and imputation. A final illustration is constrained optimisation under a general divergence loss.     

重叠滤波多音系统的频偏性能分析及优化

重叠滤波多音(O-FMT)是针对滤波多音(FMT)系统频谱利用率低的缺点而根据超奈奎斯特(FTN)概念引入子载波重叠得到的方案。鉴于O-FMT系统在频偏环境下的性能缺陷，理论分析了频偏对系统信号各部分的影响，比较了O-FMT与正交频分复用（OFDM）系统的抗频偏性能。针对频偏性能优化问题，提出了基于最佳线性无偏估计（BLUE）的改进盲估计算法，设计了基于频域滤波器系数的内嵌结构进行频偏补偿。仿真实验表明，O-FMT相比OFDM具有更好的抗频偏能力，改进的盲估计算法估计值精度更高，抗干扰、抗衰落能力更强，设计的频偏补偿结构可以使系统获得更好的性能。     

Nonparametric fixed effects model for panel data with locally stationary regressors

We develop methods for inference in nonparametric time-varying fixed effects panel data models that allow for locally stationary regressors and for the time series length $T$ and cross-section size $N$ both being large. We first develop a pooled nonparametric profile least squares dummy variable approach to estimate the nonparametric function, and establish the optimal convergence rate and asymptotic normality of the resultant estimator. We then propose a test statistic to check whether the bivariate nonparametric function is time-varying or the time effect is separable, and derive the asymptotic distribution of the proposed test statistic. We present several simulated examples and two real data analyses to illustrate the finite sample performance of the proposed methods.     

较差质量数据下的返回弹道计算及对落点影响的分析

针对某老式卫星返回时雷达跟踪数据曾发生的测量异常及质量较差等情况，为解决此类数据在返回弹道及落点计算中出现的实际困难以及给出应对措施，基于工程实际情况给出了三种不同建模类型下的弹道计算方法——当前统计解耦扩展卡尔曼滤波（EKF）、动力学建模无迹卡尔曼滤波（UKF）、多项式滑窗最小二乘估计算法，并应用该次质量较差的实际观测数据进行了算法应用效果分析与落点计算情况对比，对计算中的经验及教训进行了总结。研究结果表明，所给出的三种算法在面对较差质量数据时性能差异较大，在工程实际应用中可考虑将多种算法互为参考。研究结果及其经验教训对卫星返回弹道及落点的实时估计的工程实施有一定的参考价值。     

Varying Coefficient Regression Models: A Review and New Developments

Varying coefficient regression models are known to be very useful tools for analysing the relation between a response and a group of covariates. Their structure and interpretability are similar to those for the traditional linear regression model, but they are more flexible because of the infinite dimensionality of the corresponding parameter spaces. The aims of this paper are to give an overview on the existing methodological and theoretical developments for varying coefficient models and to discuss their extensions with some new developments. The new developments enable us to use different amount of smoothing for estimating different component functions in the models. They are for a flexible form of varying coefficient models that requires smoothing across different covariates' spaces and are based on the smooth backfitting technique that is admitted as a powerful technique for fitting structural regression models and is also known to free us from the curse of dimensionality.     

What is the major determinant of cross-border banking flows?

This study examines the major determinant of cross-border credit flows through global banks across 70 countries. Employing a Bayesian dynamic latent factor model, we decompose volatilities of banking flows into the contribution of a global common factor, regional common factor, and country-specific factor. The results indicate that the global and regional common factor explains about 40–50 percent of volatility in overall cross-border banking flows. In particular, the contribution of the global common factor increased in the 2000s. Simultaneously, main determinants are largely heterogeneous across countries: this implies that the desirable policy response to credit inflows may differ for each host country.