基于30个城市非平稳面板计量的住房财富效应实证检验

采用最新发展的非平稳面板(面板单位根、面板协整和面板DOLS)计量方法,利用30个大中城市1998-2009年的季度面板数据,对我国城镇居民的住房财富效应进行实证检验。面板数据的使用扩大了样本容量、提高结论的可靠性,而且可以进行城市间的比较分析。研究结果表明:近十年房价持续上涨,大部分城市的住房财富效应显著为负,全国总体来看住房财富效应微弱:各城市的住房财富效应表现形式存在明显差异;股票财富效应非常微弱。     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Estimation When a Parameter is on a Boundary

This paper establishes the asymptotic distribution of an extremum estimator when the true parameter lies on the boundary of the parameter space. The boundary may be linear, curved, and/or kinked. Typically the asymptotic distribution is a function of a multivariate normal distribution in models without stochastic trends and a function of a multivariate Brownian motion in models with stochastic trends. The results apply to a wide variety of estimators and models. Examples treated in the paper are: (i) quasi-ML estimation of a random coefficients regression model with some coefficient variances equal to zero and (ii) LS estimation of an augmented Dickey-Fuller regression with unit root and time trend parameters on the boundary of the parameter space.     

求y″ py′ qy=f(x)特解的一种新方法

对于二阶常系数非齐次线性微分方程：ｙ″＋ ｐｙ′＋ ｑｙ＝ ｆ（ｘ），给出了当特征根ｒ１ 与ｒ２不等时的特解公式。利用该公式，只需求出两个一阶线性微分方程的特解，就可以得到相应二阶常系数非齐次线性微分方程的特解。