A consistency result in general censoring models

In this paper we prove a consistency result for sieved maximum likelihood estimators of the density in general random censoring models with covariates. The proof is based on the method of functional estimation. The estimation error is decomposed in a deterministic approximation error and the stochastic estimation error. The main part of the proof is to establish a uniform law of large numbers for the conditional log-likelihood functional, by using results and techniques from empirical process theory.     

Four comments for the paper from Liu et al. (2019)

We give comments for the paper from Liu et al. (2019) about the Item Response Theory (IRT) model under consideration, the justification to compute Marginal likelihood, about what we learn with the data analysis performed and finally, about the computational issues in this paper.     

Restrictions on Risk Prices in Dynamic Term Structure Models

Restrictions on the risk-pricing in dynamic term structure models (DTSMs) tighten the link between cross-sectional and time-series variation of interest rates, and make absence of arbitrage useful for inference about expectations. This article presents a new econometric framework for estimation of affine Gaussian DTSMs under restrictions on risk prices, which addresses the issues of a large model space and of model uncertainty using a Bayesian approach. A simulation study demonstrates the good performance of the proposed method. Data for U.S. Treasury yields calls for tight restrictions on risk pricing: only level risk is priced, and only changes in the slope affect term premia. Incorporating the restrictions changes the model-implied short-rate expectations and term premia. Interest rate persistence is higher than in a maximally flexible model, hence expectations of future short rates are more variable—restrictions on risk prices help resolve the puzzle of implausibly stable short-rate expectations in this literature. Consistent with survey evidence and conventional macro wisdom, restricted models attribute a large share of the secular decline in long-term interest rates to expectations of future nominal short rates. Supplementary materials for this article are available online.     

生产性服务业集聚与中国企业出口生存——考虑异质性和传导机制的分析

本文以生产性服务业集聚的出口传导效应为切入点，采用我国222个城市数据和微观工业企业数据，通过Cloglog模型研究了生产性服务业集聚对企业出口生存的影响并进行了异质性和传导机制的扩展分析。研究发现：生产性服务业集聚通过专业化分工外溢、综合成本节约效应降低企业面临的出口风险，提高出口生存的概率，但上述效应表现出明显的滞后性；高端生产性服务业集聚对延长企业出口持续时间具有显著的正向效应，而低端生产性服务业集聚由于“拥挤效应”阻碍了企业的出口生存和可持续性，内资企业和非国有企业表现得尤为突出；同时，生产性服务业集聚的影响具有显著的地区异质性，高端生产性服务业集聚显著降低了东部地区企业的出口风险，低端生产性服务业集聚对提升西部地区企业出口成功概率的作用更大；此外，我国生产性服务业集聚的特征使得多样化集聚成为影响企业出口生存的主要机制，专业化集聚的作用相对较小。本研究有助于更全面地认识生产性服务业集聚影响我国企业出口生存的作用机制，对政府管理者更加精准与科学地制定服务业发展和对外开放政策具有积极的现实意义。     

双模网络下基于节点流行度的潜在空间模型

本文提出了双模网络下基于节点流行度的潜在空间模型，不仅能够显式地表达节点间产生连接的概率，而且可以推导出双模网络的连接的传递性、节点度的异质性等特征，这些特征可以通过数值化定量的方式描述网络生成过程中的常见规律。在此基础之上，本文进一步提出了加权概率指标，用以衡量双模网络的节点间未来产生连接的可能性。最后，本文分别在模拟数据、公开数据集和某在线点评网站的商户一消费者网络数据上验证了模型假设符合实际数据的分布，并使用加权概率指标与其他多种双模网络链路预测的方法进行比较分析。实验结果表明，本文提出的方法不仅可以量化分析网络生成过程中的特征，而且在实验数据上的链路预测能力整体优于其他双模链路预测方法。     

A semiparametric nonlinear mixed model approach to phase I profile monitoring

When process data follow a particular curve in quality control, profile monitoring is suitable and appropriate for assessing process stability. Previous research in profile monitoring focusing on nonlinear parametric (P) modeling, involving both fixed and random-effects, was made under the assumption of an accurate nonlinear model specification. Lately, nonparametric (NP) methods have been used in the profile monitoring context in the absence of an obvious linear P model. This study introduces a novel technique in profile monitoring for any nonlinear and auto-correlated data. Referred to as the nonlinear mixed robust profile monitoring (NMRPM) method, it proposes a semiparametric (SP) approach that combines nonlinear P and NP profile fits for scenarios in which a nonlinear P model is adequate over part of the data but inadequate of the rest. These three methods (P, NP, and NMRPM) account for the auto-correlation within profiles and treats the collection of profiles as a random sample with a common population. During Phase I analysis, a version of Hotelling’s T² statistic is proposed for each approach to identify abnormal profiles based on the estimated random effects and obtain the corresponding control limits. The performance of the NMRPM method is then evaluated using a real data set. Results reveal that the NMRPM method is robust to model misspecification and performs adequately against a correctly specified nonlinear P model. Control charts with the NMRPM method have excellent capability of detecting changes in Phase I data with control limits that are easily computable.     

中国短期资本外流规模再估算及其影响因素分析

通过比较资本外流的三种核算口径，本文将间接法与权益差额调整法相结合，对我国1982-2016年资本外流的规模进行估计。结果显示，2008年全球金融危机以来，我国资本外流规模逐年增加且近年呈增速递增趋势。通过构建非限制性VAR模型，本文进一步对导致资本外流的结构性与周期性“推动”及“拉动”因素进行分析，经验结果显示，2008年全球金融危机爆发至2013年美联储货币周期转向以前，导致我国资本外流的主要因素为人民币贬值预期与我国经济周期性调整等国内“推动”因素。2014年以来，我国资本外流进一步加剧主要受美国经济复苏步伐加快以及我国房地产市场调整等国内外“拉动”与“推动”因素共同作用。其中，外部“拉动”因素是主要诱因。在资本加速流出背景下，我国金融开放步伐应渐进审慎。     

Variable selection for spatial Poisson point processes via a regularization method

It is often of interest to use regression analysis to study the relationship between occurrence of events in space and spatially-indexed covariates. One model for such regression analysis is the Poisson point process. Here, we develop a method to perform the selection of covariates and the estimation of model parameters simultaneously for this model via a regularization method. We assess the finite-sample properties of our method with a simulation study. In addition, we propose a variant of our method that allows the selection of covariates at multiple pixel resolutions. For illustration, we consider the locations of a tree species, Beilschmiedia pendula, in a study plot at Barro Colorado Island in central Panama. We find that Beilschmiedia pendula occurs in greater abundance at locations with higher elevation and steeper slope. Also, we identify three species to which Beilschmiedia pendula tends to be attracted, two species by which it appears to be repelled, and a species with no apparent relationship.     

Who wins from an FTA induced revival of world trade?

The last decade has been characterized by a slowdown in globalization, referred to by some as "slowbalisation" and “deglobalization”. The COVID-19 pandemic has reinforced this impression. To overcome the deadlock in the Doha round negotiations at WTO the major players in world trade are negotiating or have already implemented several free trade agreements (FTAs) as a second-best solution. We analyze within a common framework nine mega FTAs, some of them are already in effect, others will be enacted soon. Overall, not the big players in world trade, the EU and the United States win by a simultaneous implementation of the nine FTAs. Japan would be the winner because it participates in four combinations (overlaps) of FTAs: EU-Japan, USA-Japan, CPTPP and RCEP. The United States hardly gain from further globalization. Similarly, the EU27 cannot profit much from further globalization.     

资产价格泡沫何时发生崩溃？——基于LPPL模型的在中国金融市场上的有效性检验

本研究基于对数周期幂律模型LPPL（Log Periodic Power Law Model），针对金融时间序列将一维价格波动翻译成反映市场泡沫微观结构的多维变量。通过对多维变量的动态监测，把握市场中泡沫的演变并预测泡沫破裂的临界点，从而有效降低或防范金融资产泡沫破裂所导致的风险。为检验LPPL模型在中国金融市场中的适用性，本文分别使用上证综指、四个期货连续合约以及两支个股检验模型效果。实证结果表明当金融资产价格序列呈现超指数加速震荡上升或下降时，该模型能获得稳定的估计效果，有效预测泡沫破裂临界时点。