Likelihood-based specification analysis of continuous-time models of the short-term interest rate

An extensive collection of continuous-time models of the short-term interest rate is evaluated over data sets that have appeared previously in the literature. The analysis, which uses the simulated maximum likelihood procedure proposed by Durham and Gallant (2002), provides new insights regarding several previously unresolved questions. For single factor models, I find that the volatility, not the drift, is the critical component in model specification. Allowing for additional flexibility beyond a constant term in the drift provides negligible benefit. While constant drift would appear to imply that the short rate is nonstationary, in fact, stationarity is volatility-induced. The simple constant elasticity of volatility model fits weekly observations of the three-month Treasury bill rate remarkably well but is easily rejected when compared with more flexible volatility specifications over daily data. The methodology of Durham and Gallant can also be used to estimate stochastic volatility models. While adding the latent volatility component provides a large improvement in the likelihood for the physical process, it does little to improve bond-pricing performance.     

Contemporary Extensions of the Rasch Model

This paper will introduce, discuss and illustrate two contemporary extensions of theRasch model: the one parameter logistic model (Verhelst and Glas, 1995) and theMultidimensional Rasch model (Hoijtink et al., 1999). Using data with respect tothe measurement of schizotypy (Vollema and Hoijtink, 2000) the most importantfeatures of both models will be illustrated. For the one parameter logistic modelthese include: a (discrete) discrimination parameter for each item; a test for itembias; and, estimation of the location of a person on the (latent) trait that is beingmeasured. For the multidimensional Rasch model these include: specification ofthe model; and, model selection. All analyses presented in this paper can be executedusing either OPLM (Verhelst et al., 1995), TESTFACT (Wilson et al.,1984) or ConQuest (Wu et al., 1998). At the end of the paper some features ofmodels and software that have not been discussed will be summarized.     

离散选择模型的基本原理及其发展演进评介

离散选择模型的研究真正兴起于19世纪50年代末，属于微观计量经济学的范畴。该模型能够对个体和家庭行为进行经验性的统计分析，因而在经济学和其他社会科学中得到广泛的应用。本文从离散选择模型的基本性质及效用最大化的理论背景出发，指出logit模型虽然使用的是最早并且最为广泛的离散选择模型，但是其存在着三大局限性：不能表示随机口味的变化、暗含成比例的替代形式和不能处理不可观测因素在不同期间相关的情形。GEV（含嵌套logit）、probit和混合logit模型等其他的离散选择模型，很大程度上都是为了避免这些限制而产生并发展起来的。     

Simpler Probabilistic Population Forecasts: Making Scenarios Work

The traditional high-low-medium scenario approach to quantifying uncertainty in population forecasts has been criticized as lacking probabilistic meaning and consistency. This paper shows, under certain assumptions, how appropriately calibrated scenarios can be used to approximate the uncertainty intervals on future population size and age structure obtained with fully stochastic forecasts. As many forecasting organizations already produce scenarios and because dealing with them is familiar territory, the methods presented here offer an attractive intermediate position between probabilistically inconsistent scenario analysis and fully stochastic forecasts.     

Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings.     

分形理论在货运量预测中的应用探讨

在已有研究的基础上，提出了一种新的分形预测模型——等长度递补变维分形模型。首先阐述了等长度递补变维分形模型的基本原理；然后利用该模型对1990年～2004年我国货运量进行了预测，并利用后验差方法分析了模型的精度；最后利用模型预测了2005年～2010年我国货运量及其构成（铁路、公路和水运各自所占比重）.     

货币政策和银行风险承担：一种非线性关系

宽松的货币政策会通过估值、收入和现金流机制,追逐收益机制以及中央银行沟通和反应机制等渠道增加银行风险承担,通过风险转移机制降低银行风险承担,从而导致货币政策与银行风险承担之间可能存在复杂的非线性关系。本文使用面板阈值模型,基于我国银行业数据研究了货币政策对银行风险承担的影响,发现我国货币政策对银行风险承担的影响存在门限效应,即货币政策对银行风险承担的影响取决于货币政策基准利率偏离泰勒规则利率的程度。当这种偏离小于门限值时,宽松货币政策会增加银行风险承担;当这种偏离大于门限值时,宽松货币政策会降低银行风险承担。本文研究对更好地理解我国货币政策对银行风险承担及金融稳定的影响有一定参考意义。     

公开市场操作与利率期限结构行为——基于混频数据信息的研究视角

在混频数据信息环境中,精准识别公开市场操作(央行政策利率)和国债收益率曲线(基准利率体系)之间的关联机制至关重要,其影响了货币政策期限结构传导的有效性。本文在混频Nelson-Siegel(N-S)利率期限结构模型框架下,引入央行政策利率,揭示公开市场操作与利率期限结构(水平、斜率、曲度)因子之间的作用机制。实证结果表明:混频数据信息条件下,引入的公开市场操作信息显著改进国债收益率曲线的拟合效果;斜率因子冲击对公开市场操作具有显著的正向影响,而利率期限结构因子对政策调控的反应不敏感。进一步研究表明,2015年以来,公开市场操作对斜率因子的影响逐渐扩大,政策利率向国债收益率曲线的传导效率得到显著提高,我国现代货币政策框架日益健全。     

在ASP.NET下实现MVC设计模式的探讨

从视图、控制器、模型三个方面简要介绍了在ASP．NET环境下，MVC设计模式的实现，并讨论了MVC模式与其他模式的关系，最后对MVC的优点及缺点进行了分析。     

美国对华贸易技术壁垒的政治经济学分析

技术贸易壁垒已成为中美贸易摩擦的最主要原因之一。本文拓展了格罗斯曼和海普曼的关税模型．对美国对华贸易技术壁垒进行政治经济学分析，指出贸易技术壁垒是美国政府与相关利益集团博弈的结果，其存在具有必然性和长期性，进而提出对我国政府、企业、行业协会的政策建议。