东北地区就业密度分布的空间特征——基于两次经济普查数据的空间计量经济分析

基于区域密度方程和空间统计方法,利用两次经济普查的就业数据对东北地区就业密度的空间特征进行了研究。结果表明,东北地区形成了一个向右倾斜的倒"Y"字型就业密度格局,从北向南已经形成和正在形成的次中心逐渐增多,沿海化趋势和局域融合趋势也比较明显,东北地区多中心就业格局已经形成。2004—2008年,东北地区整体就业密度的集中性还在继续加强,但不同局域就业中心表现出了不同的增长模式,黑龙江处于中心增长型扩散的单中心阶段,吉林省和辽宁省处于向心集聚和中心增长型扩散并存的多中心阶段,大部分就业中心均处于中心增长型扩散阶段,尚无去中心化扩散的就业中心。     

Ultimate 100‐m world records through extreme‐value theory

We use extreme‐value theory to estimate the ultimate world records for the 100‐m running, for both men and women. For this aim we collected the fastest personal best times set between January 1991 and June 2008. Estimators of the extreme‐value index are based on a certain number of upper order statistics. To optimize this number of order statistics we minimize the asymptotic mean‐squared error of the moment estimator. Using the thus obtained estimate for the extreme‐value index, the right endpoint of the speed distribution is estimated. The corresponding time can be interpreted as the estimated ultimate world record: the best possible time that could be run in the near future. We find 9.51 seconds for the 100‐m men and 10.33 seconds for the women.     

一种基于小波脊时频分析的差分跳频信号检测方法

针对差分跳频信号频带宽、跳速快、检测难的问题,在分析比较短时傅里叶变换(STFT)的基础之上,提出一种通过提取小波脊进行时频分析和参数估计的信号检测方法。该方法的检测效果明显优于短时傅里叶变换,当信噪比大于-7 dB时,在不要求高采样率的情况下,对于差分跳频信号的参数估计是有效的。     

A bootstrap-based non-parametric forecast density

Interest in density forecasts (as opposed to solely modeling the conditional mean) arises from the possibility of dynamics in higher moments of a time series, as well as in forecasting the probability of future events in some applications. By combining the idea of Markov bootstrapping with that of kernel density estimation, this paper presents a simple non-parametric method for estimating out-of-sample multi-step density forecasts. The paper also considers a host of evaluation tests for examining the dynamic misspecification of estimated density forecasts by targeting autocorrelation, heteroskedasticity and neglected non-linearity. These tests are useful, as a rejection of the tests gives insight into ways to improve a particular forecasting model. In an extensive Monte Carlo analysis involving a range of commonly used linear and non-linear time series processes, the non-parametric method is shown to work reasonably well across the simulated models for a suitable choice of the bandwidth (smoothing parameter). Furthermore, an application of the method to the U.S. Industrial Production series provides multi-step density forecasts that show no sign of dynamic misspecification.     

现金流贴现模型下的企业估值

随着中国市场经济的发展，企业交易例如并购、整合等越来越频繁。企业估值的意义在于寻找被市场低估的企业进行套利投资，为行业整合提供价值基础。企业估值已成为行业整合的价值基础，更多的投资者通过评估企业的价值来寻找被市场低估的企业进而进行套利投资。与其他估价方法相比，现金流量折现法是企业估值使用最广泛，理论最健全的方法，它量化了企业未来的风险、盈利及增长、成本等因素。更能有效的评估企业的价值。本文详细地介绍了现金流量折现评估方法，包括现金流量折现模型，折现率以及未来现金流量的预测。     

Social Security and the Retirement and Savings Behavior of Low Income Households

In this paper, we develop and estimate a model of retirement and savings incorporating limited borrowing, stochastic wage offers, health status and survival, social security benefits, Medicare and employer-provided health insurance coverage, and intentional bequests. The model is estimated on a sample of relatively poor households from the first three waves of the Health and Retirement Study (HRS), for whom we would expect social security income to be of particular importance. The estimated model is used to simulate the responses to changes in social security rules, including changes in benefit levels, in the payroll tax, in the social security earnings tax and in early and normal retirement ages. Welfare and budget consequences are estimated.     

Parametric bootstrap tests for continuous and discrete distributions

Statistical procedures based on the estimated empirical process are well known for testing goodness of fit to parametric distribution families. These methods usually are not distribution free, so that the asymptotic critical values of test statistics depend on unknown parameters. This difficulty may be overcome by the utilization of parametric bootstrap procedures. The aim of this paper is to prove a weak approximation theorem for the bootstrapped estimated empirical process under very general conditions, which allow both the most important continuous and discrete distribution families, along with most parameter estimation methods. The emphasis is on families of discrete distributions, and simulation results for families of negative binomial distributions are also presented.     

Education public financing and economic growth: an endogenous growth model versus evidence

Using an Endogenous Growth Model with physical and human capital and unemployment (Mauro and Carmeci in J Macroecon 25:123–137, 2003), we study the effects of subsidies to education in economic growth. According to the model, we conclude that government subsidies to education only enhance economic growth conditional on unemployment and that this relationship is negatively influenced by unemployment. We provide evidence from a broad panel data of countries that confirms the importance of unemployment in the relationship between subsidies to education and economic growth but dismiss its importance as a direct determinant of economic growth.      

Maximum penalized quasi-likelihood estimation of the diffusion function

We develop a maximum penalized quasi-likelihood estimator for estimating in a non-parametric way the diffusion function of a diffusion process, as an alternative to more traditional kernel-based estimators. After developing a numerical scheme for computing the maximizer of the penalized maximum quasi-likelihood function, we study the asymptotic properties of our estimator by way of simulation. Under the assumption that overnight London Interbank Offered Rates (LIBOR), the USD/EUR, USD/GBP, JPY/USD, and EUR/USD nominal exchange rates, and the 1-month, 3-month Treasury bill yields, and 30-year Treasury bond yields are generated by diffusion processes, we use our numerical scheme to estimate the diffusion function.     

Estimation of a k-monotone density: characterizations, consistency and minimax lower bounds

The classes of monotone or convex (and necessarily monotone) densities on     can be viewed as special cases of the classes of k - monotone densities on     . These classes bridge the gap between the classes of monotone (1-monotone) and convex decreasing (2-monotone) densities for which asymptotic results are known, and the class of completely monotone (∞-monotone) densities on     . In this paper we consider non-parametric maximum likelihood and least squares estimators of a k -monotone density g ₀. We prove existence of the estimators and give characterizations. We also establish consistency properties, and show that the estimators are splines of degree k −1 with simple knots. We further provide asymptotic minimax risk lower bounds for estimating the derivatives     , at a fixed point x ₀ under the assumption that     .