人民币汇率失衡及贸易壁垒对我国出口行业影响异质性研究

贸易摩擦、新冠疫情使我国经济贸易面临严峻挑战,人民币汇率波动及贸易壁垒将常态化地影响我国出口贸易.在加快形成以国内大循环为主体、国内国际双循环相互促进的新发展格局背景下,文章基于HS分类原则,选取WTO等机构的贸易数据,通过构建BEER模型测算了人民币汇率失衡程度;通过建立固定效应面板模型研究了人民币汇率失衡及贸易壁垒双因素对我国出口行业影响的异质性.研究表明:人民币汇率失衡对我国出口贸易整体上具有显著负向影响,非关税壁垒对我国出口贸易整体上具有显著正向影响;关税和非关税构成的贸易壁垒与人民币汇率失衡双因素对我国出口贸易具有显著抑制作用,并对不同出口行业的影响存在异质性;人民币汇率失衡与非关税壁垒双因素对大多行业均具有显著负向影响,人民币汇率失衡与关税壁垒双因素对不同行业影响的正负效应及程度大小有异质性.因此,建议通过运行逆周期因子等完善人民币汇率自我修正机制;通过引导出口企业了解使用外汇衍生品等促进外汇衍生品市场运行机制的健全;通过加强"海外仓"建设及深耕RCEP框架下的东盟十国市场等实施出口行业差异化贸易策略;通过提升自主科技创新能力等加快出口行业产业转型升级,推动双循环,实现我国出口贸易高质量发展.     

Efficient Estimation in Heteroscedastic Partially Linear Varying Coefficient Models

This article considers statistical inference for the heteroscedastic partially linear varying coefficient models. We construct an efficient estimator for the parametric component by applying the weighted profile least-squares approach, and show that it is semiparametrically efficient in the sense that the inverse of the asymptotic variance of the estimator reaches the semiparametric efficiency bound. Simulation studies are conducted to illustrate the performance of the proposed method.     

China’s Choice of Analytical Models for Its Anti-Monopoly Law

Modern analytical models for anti-monopoly laws are a core element of the application of those laws. Since the Anti-Monopoly Law of the People’s Republic of China was promulgated in 2008, law enforcement and judicial authorities have applied different analytical models, leading to divergent legal and regulatory outcomes as similar cases receive different verdicts. To select a suitable analytical model for China’s Anti-Monopoly Law, we need to consider the possible contribution of both economic analysis and legal formalism and to learn from the mature systems and experience of foreign countries. It is also necessary to take into account such binding constraints as the current composition of China’s anti-monopoly legal system, the ability of implementing agencies and the supply of economic analysis, in order to ensure complementarity between the analytical model chosen and the complexity of economic analysis and between the professionalism of implementing agencies and the cost of compliance for participants in economic activities. In terms of institutional design, the models should provide a considered explanation of the legislative aims of the law’s provisions. It is necessary, therefore, to establish a processing model of behavioral classification that is based on China’s national conditions, applies analytical models using normative comprehensive analysis, makes use of the distribution rule of burden of proof, improves supporting systems related to analytical models and enhances the ability of public authorities to implement the law.     

How do countries choose their monetary policy frameworks?

This paper investigates the determinants of countries’ choices of monetary policy framework. A brief narrative focused on groupings of countries motivates an econometric analysis which draws on previous work on the determinants of exchange rate regimes, bringing in standard factors as well as the trade networks of potential anchor currency blocs and the financial market depth that are emphasised in the narrative. The model turns out to be able to predict three quarters of countries’ choices, and there is no obvious systematic pattern in the errors. The results have important implications for how countries should choose their monetary policy frameworks.     

汇率货币模型的非线性协整关系检验——基于深度GRU神经网络

本文采用深度门控循环单元（GRU）神经网络探讨三种汇率货币模型（弹性价格、前瞻性和实际利率差模型）的非线性协整关系。GRU技术在深度学习中具有智能记忆、自主学习和强逼近能力等优点。为此，本文运用该技术对6组典型浮动汇率制国别数据进行了非线性Johansen协整检验。结果表明，汇率与宏观经济基本面之间存在非线性协整关系，从而说明了货币模型在非线性条件下的有效性，以及先进的深度学习工具在检验经济理论中的优势。     

Restricted minimax estimation in semiparametric linear models

Abstract

In this article we develop the minimax estimation approach of general linear models to the semiparametric linear models when the parameters are simultaneously constrained by an ellipsoid and linear restrictions. Combining sample information and prior constraints the minimax estimator is obtained and compared with partially least square estimator by theoretical and simulation methods.     

储层岩石矿物成分对酸蚀裂缝导流能力的影响

储层岩石矿物成分是酸压改造效果影响因素之一。研究认为，在储层岩石矿物成分中易溶物的含量及分布方式决定了酸蚀裂缝导流能力大小，易溶蚀物的含量越多，岩石溶蚀速率越快。室内测试了不同溶蚀速率下的灰岩和白云岩的酸蚀裂缝导流能力，研究了岩石溶蚀速率、易溶蚀物含量对酸蚀裂缝导流能力的影响，对酸压工艺设计方案提出了指导性建议。研究结果表明，储层岩石溶蚀速率与酸蚀裂缝导流能力增加倍比存在抛物线关系，易溶蚀物占30%40% 时酸蚀裂缝导流能力保持较好。     

Multiparameter evidence synthesis in epidemiology and medical decision-making: current approaches

Summary.  Alongside the development of meta-analysis as a tool for summarizing research literature, there is renewed interest in broader forms of quantitative synthesis that are aimed at combining evidence from different study designs or evidence on multiple parameters. These have been proposed under various headings: the confidence profile method, cross-design synthesis, hierarchical models and generalized evidence synthesis. Models that are used in health technology assessment are also referred to as representing a synthesis of evidence in a mathematical structure. Here we review alternative approaches to statistical evidence synthesis, and their implications for epidemiology and medical decision-making. The methods include hierarchical models, models informed by evidence on different functions of several parameters and models incorporating both of these features. The need to check for consistency of evidence when using these powerful methods is emphasized. We develop a rationale for evidence synthesis that is based on Bayesian decision modelling and expected value of information theory, which stresses not only the need for a lack of bias in estimates of treatment effects but also a lack of bias in assessments of uncertainty. The increasing reliance of governmental bodies like the UK National Institute for Clinical Excellence on complex evidence synthesis in decision modelling is discussed.     

Kernel Estimation of Rate Function for Recurrent Event Data

Abstract.  Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth will produce a curve with nicks occurring at the censoring times, whereas there is no such problem with the least squares method. Furthermore, the asymptotic variance of the least squares estimator is shown to be smaller under regularity conditions. However, in the implementation of the bootstrap procedures, the moment method is computationally more efficient than the least squares method because the former approach uses condensed bootstrap data. The performance of the proposed procedures is studied through Monte Carlo simulations and an epidemiological example on intravenous drug users.