On Testing for the Nullity of Some Skewness Coefficients

Three tests for the skewness of an unknown distribution are derived for iid data. They are based on suitable normalization of estimators of some usual skewness coefficients. Their asymptotic null distributions are derived. The tests are next shown to be consistent and their power under some sequences of local alternatives is investigated. Their finite sample properties are also studied through a simulation experiment, and compared to those of the √ b ₂-test.     

The Tax‐smoothing Hypothesis: Evidence from Sweden, 1952–1999

This paper tests Barro's (1979) tax‐smoothing hypothesis using Swedish central government data for the period 1952–1999. According to the tax‐smoothing hypothesis, the government sets the budget surplus equal to expected changes in government expenditure. When expenditure is expected to increase, the government runs a budget surplus, and when expenditure is expected to fall, the government runs a budget deficit. The empirical evidence suggests that the model provides a useful benchmark and that tax‐smoothing behavior can explain about 60 percent of the variability in the Swedish central government budget surplus.     

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.     

The Impact of SFAS No. 114 on the Linear Information Dynamic for Commercial Banks

Increase (decrease) in loan loss provisions would decrease (increases) bank earnings, but increase (decreases) regulatory capital. Previous studies have separately documented earnings and capital management behavior via loan loss provisions by commercial banks. However, it is difficult to isolate a bank's demand for increasing earnings from its demand for regulatory capital because earnings is a source of capital. Based on the objective bank function, this study investigates the impact of SFAS No. 114 on the information content of loan loss provisions in relation to both earnings quality and capital adequacy in a linear information dynamic framework. Test results show that the association between market value with loan loss provisions became significantly stronger for commercial banks in the post- than in the pre-adoption period. As a result, SFAS No. 114 is also found to positively affect the association of market value with both bank earnings and regulatory capital through the clean surplus relation because of the higher value relevance of loan loss provisions. The findings thus provide empirical evidence that SFAS No. 114 has significantly complemented banking regulations in enhancing (reducing) the (dispersion from the) accounting measurement construct of loan loss provisions.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Market structure and rating strategies in credit rating markets – A dynamic model with matching of heterogeneous bond issuers and rating agencies

This paper investigates the impact of rating agencies in a market with asymmetric information. In particular, the role of credit rating agencies as an intermediary between investors and bond issuers is discussed. We model this setting in a dynamic framework in which both rating agencies and bond issuers are of heterogeneous quality. Rating agencies can apply costly research technology to reveal the fundamental nature of bond issuers and engage in rating smoothing. We show that rating smoothing can compensate for low research quality, even though it is accompanied by a quality deterioration in the rating market and market clustering. Moreover, low-quality bond issuers have a general tendency to match with low-quality rating agencies. If investors place a strong emphasis on the reputation of rating agencies, rating markets also tend to be strongly clustered.     

全球经济危机对我国进出口贸易的影响——基于多参数平滑法的定量分析

选取2000-2010年我国进出口的月度数据,运用多参数平滑方法,分析了全球经济危机对我国进出口贸易的影响。定量分析显示全球经济危机使我国2009年和2010年对外出口规模减少额超过10000亿美元,进口规模减少额超过6000亿美元,影响十分巨大,危机开始时对一般贸易的影响超过对进出口的整体影响。随着我国政府实施扩张性的财政政策和货币政策的效果逐渐显现,世界经济逐步回暖,全球经济危机对我国进出口的影响逐渐减少。进一步的研究显示:危机使得我国对欧盟进出口的影响超过了对美国进出口的影响;对欧美地区出口的影响超过进口的影响;对我国不同地区进出口的影响是从东到西影响强度逐步减弱,对外开放程度越高的地区受到的影响越显著,能够充分发挥比较优势的产品或行业受到全球经济危机负面影响的程度较小。     

Impact of copula choice on the modeling of crop yield basis risk

A number of problems in agricultural economics involve modeling joint distributions for which the assumption of multivariate normality may not be warranted. Yet, very little work has been conducted evaluating competing methods for modeling joint dependence. We develop a simulation framework to evaluate the bias and efficiency impacts of copula choice in the context of evaluating county‐to‐farm basis risk. The results suggest significant differences in performance across various copulas and approaches. The findings have important implications for risk analysis, insurance, and policy modeling problems in agriculture regarding the selection of method to model dependence among random variables.     

工业废水污染与城镇化进程的动态关系分析

选取2003—2012年中国工业废水中的5类污染物,对工业废水污染物和城镇化率的关系进行库兹涅茨曲线拟合,并在此基础上采用3次指数平滑法对工业废水污染物的排放量进行预测。结果表明:随着城镇化建设的加快,我国工业废水污染物中石油类和铅的排放量呈现先下降后上升的趋势,但化学需氧量、氨氮和挥发酚等污染物却不符合典型的环境库兹涅茨曲线形状。预计到2020年,化学需氧量和氨氮的排放量将呈现负值,而挥发酚、石油类和铅的排放量将急剧增加。     

基于遥感技术的耕地复种指数研究进展

复种是我国重要的农作制度之一,对于提高我国耕地综合生产能力和确保国家粮食安全具有重要作用。该文在介绍复种指数基本概念和研究方法的基础上,对基于遥感技术的耕地复种指数研究进展进行了评述,尤其对时序植被指数平滑方法和复种指数的判别方法等两个关键技术进行了详细阐述。该文提出了遥感技术和地面观测的结合将是未来耕地复种指数遥感监测的发展趋势之一。如何进行遥感数据平滑最优化处理,以及获取高时空分辨率遥感数据及规范准确的地面观测数据将是需要重点解决的关键问题。