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.     

Mass media effects on trading activities: television broadcasting evidence from Japan

This study examines how information broadcasting through television (TV) media influences stock market activities. Consistent with the effect of TV information to attract investor attention, we find that increased information flow through TV is significantly associated with greater trading volume and larger price change. For information type, hard news from business-oriented programmes and earnings-related news strongly contributes to the attention effect, while the effect of soft news is weaker. Bid–ask spread widens for more TV information flows, suggesting that new information arrival in the market expands information asymmetry. Finally, the impact of TV is more influential for stocks with more individual shareholders than those with institutional shareholders.     

A distributed lag estimator derived from Shiller's smoothness priors: An extension

In this paper we consider a polynomial distributed model with varying coefficients and Shiller's smoothness priors. It has been shown that the Bayes estimator under this specification produces a less smooth lag pattern than the Almon and Shiller estimators for the fixed coefficients case.     

The exact,large-sample and small-disturbance conditions of dominance of biased estimators in linear models

In this paper we show that while the dominance conditions of biased estimators by small-disturbance approximation are identical with the exact conditions, this is not so in the case of large-sample approximation. The use of small-disturbance approximation is suggested for various other situations.     

Local polynomial estimation of nonparametric simultaneous equations models

We define a new procedure for consistent estimation of nonparametric simultaneous equations models under the conditional mean independence restriction of Newey et al. [1999. Nonparametric estimation of triangular simultaneous equation models. Econometrica 67, 565–603]. It is based upon local polynomial regression and marginal integration techniques. We establish the asymptotic distribution of our estimator under weak data dependence conditions. Simulation evidence suggests that our estimator may significantly outperform the estimators of Pinkse [2000. Nonparametric two-step regression estimation when regressors and errors are dependent. Canadian Journal of Statistics 28, 289–300] and Newey and Powell [2003. Instrumental variable estimation of nonparametric models. Econometrica 71, 1565–1578].     

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.     

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.     

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。     

Nonparametric regression estimation with general parametric error covariance: a more efficient two-step estimator

Recently Martins-Filho and Yao (J Multivar Anal 100:309–333, 2009) have proposed a two-step estimator of nonparametric regression function with parametric error covariance and demonstrate that it is more efficient than the usual LLE. In the present paper we demonstrate that MY’s estimator can be further improved. First, we extend MY’s estimator to the multivariate case, and also establish the asymptotic theorem for the slope estimators; second, we propose a more efficient two-step estimator for nonparametric regression function with general parametric error covariance, and develop the corresponding asymptotic theorems. Monte Carlo study shows the relative efficiency loss of MY’s estimator in comparison with our estimator in nonparametric regression with either AR(2) errors or heteroskedastic errors. Finally, in an empirical study we apply the proposed estimator to estimate the public capital productivity to illustrate its performance in a real data setting.