地区与国家GDP核算总量数据衔接方法比较研究

 本文以国家数据为准,通过采用2005-2009年的数据为样本,从理论与实证上比较分析了地区与国家GDP数据衔接的三种方法即Geary和Stark的产出估算方法、线性调整法与辅助回归法,比较结果显示：（1）从理论上分析,三种方法都有其合理性,只是辅助回归法较另两种方法更具可取性。（2）从衔接效果上看,辅助回归法优于Geary和Stark的产出估算方法,Geary和Stark的产出估算方法又优于线性调整法。不过不同的方法皆有相应的适用场合与特点以及不同的衔接效果,因而只能说三种方法中有趋优的方法,但不能明确断定何种方法可以具体应用于实际数据衔接中并能达到良好的调整效果。     

Nonparametric regression estimation in a null recurrent time series

We derive an asymptotic theory of nonparametric estimation for a time series regression model Z_t=f(X_t)+W_t, where {X_t} and {Z_t} are observed nonstationary processes, and {W_t} is an unobserved stationary process. The class of nonstationary processes allowed for {X_t} is a subclass of the class of null recurrent Markov chains. This subclass contains the random walk, unit root processes and nonlinear processes. The process {W_t} is assumed to be linear and stationary.     

基于分位点回归模型的条件VaR估计以及杠杆效应分析

 在文献中,分析杠杆效应时大多数都是基于ARCH类模型,本文应用分位点回归模型及其变点检测模型分析了“已实现”波动率条件下的CVaR,并尝试从CVaR的角度对杠杆效应进行分析。最后,对中国股票市场进行了实证研究,得到了“已实现”波动率条件下的CVaR估计,并对中国股市的杠杆效应进行了分析。     

Is regression adjustment supported by the Neyman model for causal inference?

This paper examines both theoretically and empirically whether the common practice of using OLS multivariate regression models to estimate average treatment effects (ATEs) under experimental designs is justified by the Neyman model for causal inference. Using data from eight large U.S. social policy experiments, the paper finds that estimated standard errors and significance levels for ATE estimators are similar under the OLS and Neyman models when baseline covariates are included in the models, even though theory suggests that this may not have been the case. This occurs primarily because treatment effects do not appear to vary substantially across study subjects.     

Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information

This paper considers alternative estimators of the intercept parameter of the linear regression model with normal error when uncertain non-sample prior information about the value of the slope parameter is available. The maximum likelihood, restricted, preliminary test and shrinkage estimators are considered. Based on their quadratic biases and mean square errors the relative performances of the estimators are investigated. Both analytical and graphical comparisons are explored. None of the estimators is found to be uniformly dominating the others. However, if the non-sample prior information regarding the value of the slope is not too far from its true value, the shrinkage estimator of the intercept parameter dominates the rest of the estimators.     

On Rao score and PearsonX 2 statistics in generalized linear models

The identity of the Rao score and PearsonX ² statistics is well known in the areas where the latter was first introduced: goodness-of-fit in contingency tables and binary responses. We show in this paper that the same identity holds when the two statistics are used for testing goodness-of-fit of Generalized Linear Models. We also highlight the connections that exist between the two statistics when they are used for the comparison of nested models. Finally, we discuss some merits of these unifying results. Work financially supported by cofin. MIUR grants 2000 and 2002.     

Estimation of non-parametric regression for dasometric measures

The aim of this paper is to describe a simulation procedure to compare parametric regression against a non-parametric regression method, for different functions and sets of information. The proposed methodology improves lack of fit at the edges of the regression curves, and an acceptable result is obtained for the no-parametric estimation in all studied cases. Larger differences appear at the edges of the estimation. The results are applied to the study of dasometric variables, which do not fulfil the normality hypothesis needed for parametric estimation. The kernel regression shows the relationship between the studied variables, which would not be detected with more rigid parametric models.     

并购企业支付方式选择与内部控制、资本结构——基于logistic回归模型的实证研究

以2008—2010年间沪市上市公司的922起并购案例为研究样本,通过logistic回归方法,实证分析了内部控制水平、资本结构对并购支付方式选择产生的影响。研究发现：与现金支付相比,资产负债率越低,股权支付的可能性越大;内部控制水平越低,股权支付的可能性越小。内部控制水平与资本结构对资产支付及其他支付方式（以混合支付为主）影响不显著。     

基于ARCH类模型的国际粮价波动规律研究

粮价的波动最终会给一国的粮食安全带来巨大风险。了解国际粮价的波动特征有助于采取相应的措施稳定粮价,避免粮价波动的国际传导对国内粮食市场进而对粮食安全产生不利影响。通过建立ARCH类模型,分析各类粮食国际价格的波动规律。得出的主要结论有:国际粮价波动存在明显的集族性;外部冲击对国际粮价波动具有持久性影响;国际粮食市场不存在高风险高粮价的特征;国际粮价波动具有显著的非对称性,而且粮价上涨的信息引发的粮价波动要大于粮价下降的信息引发的波动。     

大禹与传统文化模式研究的现代转换

大禹和中华传统文化之形成关系密切。大禹治水是“救灾建国”理论的第一次明确的政治实践,其目的是寻找粮食以解决生存危机。在“灾民恐惧”之下,救灾英雄由此成为中国政治文化的主宰者,其逻辑是强权执政和恩人执政。二者合谋必然导致的楷模型政治成为我国文化模式的主要特征,楷模政治是一种历史的幻象,其本质是一种超人专制,而大禹是这种政治文化最早的历史偶像之一,对大禹的新解读是文化现代转换路径实现的必要步骤。