Finite sample properties of maximum likelihood estimator in spatial models

We investigate the finite sample properties of the maximum likelihood estimator for the spatial autoregressive model. A stochastic expansion of the score function is used to develop the second-order bias and mean squared error of the maximum likelihood estimator. We show that the results can be expressed in terms of the expectations of cross products of quadratic forms, or ratios of quadratic forms in a normal vector which can be evaluated using the top order invariant polynomial. Our numerical calculations demonstrate that the second-order behaviors of the maximum likelihood estimator depend on the degree of sparseness of the weights matrix.     

河南省耕地“非粮化”空间分异特征及其驱动因素分析

目的 河南省作为农业大省,耕地“非粮化”现象日益明显。理清河南省耕地“非粮化”空间分异特征及其驱动因素,为防治耕地“非粮化”,优化耕地利用提供决策参考。方法 文章采用空间自相关分析对河南省耕地“非粮化”空间分异格局进行研究,使用地理探测器模型对其驱动因素进行探究。结果 （1）2010年、2015年和2020年河南省耕地“非粮化”率平均值分别为32.12%、29.15%和32.21%,耕地“非粮化”水平具有一定的变动幅度。2015—2020年耕地“非粮化”上升率增加了3.06%,且目前处于中度“非粮化”。（2）在空间上形成了河南省西南部（南阳市、三门峡市）、中部（郑州市、开封市、许昌市）等地区“非粮化”率高,北部（新乡市、鹤壁市、安阳市、濮阳市、焦作市）、东部（周口市、商丘市）等地区较低的空间分异格局,且具有明显的集聚特征。（3）河南省耕地“非粮化”的空间分异格局是由多因素综合作用的结果,其中高程、人均消费水平、人均耕地面积和坡度对于该格局变化具有较强的影响。结论 研究期内,河南省耕地“非粮化”在经济、自然资源条件等各种因素的驱动下,呈现出上升趋势,并具有显著的空间分异特征。基于河南省耕地“非粮化”的时空演变特征及其驱动因素分析,为政府精准制定防治耕地“非粮化”措施提供决策参考依据。     

Stock market reaction to fed funds rate surprises: State dependence and the financial crisis

This paper examines the response of US stock returns to Federal Funds rate (FFR) surprises between 1989 and 2012, focusing on the impact of the recent financial crisis. We find that outside the crisis period, stock prices increased as a response to unexpected FFR cuts. State dependence is identified with stocks exhibiting larger increases when interest rate easing coincided with recessions, bear markets, and tightening credit conditions. However, an important structural shift occurred during the crisis, changing the stocks’ response to FFR shocks and the nature of state dependence. Throughout the crisis period, stocks did not react positively to unexpected FFR cuts, which were interpreted as signals of worsening future economic conditions. This triggered a rebalancing of investment portfolios away from falling equities and towards safe-haven assets. Our results highlight the severity of the crisis and the ineffectiveness of conventional monetary policy close to the zero lower bound.     

基于金融企业网络的京津冀城市网络结构特征分析

城市之间基于各种"流"的联系正在促使我国区域城市网络的形成,作为生产性服务业中掌握城市正常运转资金流的金融业,在城市网络结构的形程中扮演着重要的角色。选取银行、证券、保险等金融企业数据,从企业组织角度测度京津冀城市群的网络结构特征,依据京津冀区域金融企业分支机构数量等数据,构建企业网络服务值、企业网络连接通量、连锁链接数等多个指标分析京津冀城市群网络结构的网络性、中心性和功能性特征。研究结果表明:首先,京津冀城市群已初步形成网络化格局,城市间通过企业流的作用建立了不同程度的网络联系且层次分明;其次,区域中心城市发展差异化明显,北京的金融服务能力和中心性远高于其他城市,在空间格局上,天津作为区域双中心之一,其对外辐射能力明显不足,城市群多中心发展趋势仍然有待加强;再次,区域功能极化明显,区域金融产业集中于北京、天津、石家庄,其他中小城市的金融产业功能有待培育。最后,从金融产业层面提出构建世界级多中心网络型城市群,促进京津冀城市网络协同发展的政策建议。     

The second-order bias and mean squared error of estimators in time-series models

We develop analytical results on the second-order bias and mean squared error of estimators in time-series models. These results provide a unified approach to developing the properties of a large class of estimators in linear and nonlinear time-series models and they are valid for both normal and nonnormal samples of observations, and where the regressors are stochastic. The estimators included are the generalized method of moments, maximum likelihood, least squares, and other extremum estimators. Our general results are applied to four time-series models. We investigate the effects of nonnormality on the second-order bias results for two of these models, while for all four models, the second-order bias and mean squared error results are given under normality. Numerical results for some of these models are also presented.