Evaluating factor forecasts for the UK: The role of asset prices

This paper applies a large data set, consisting of 167 monthly time series for the UK, both economic and financial, to simulate out-of-sample predictions of industrial production, inflation, 3-month Treasury Bills, and other variables. Fifteen dynamic factor models that allow forecasting based on large panels of time series are considered. The performances of these factor models are then compared to the following competing models: a simple univariate autoregressive, a vector autoregressive, a leading indicator, and a Phillips curve models. The results show that the best dynamic factor models outperform the competing models in forecasting at 6-, 12-, and 24-month horizons. Thus, the financial markets may have predictive power for the economic activity. This can be a useful tool for central banks and financial institutions, which may use the factor models to construct leading indicators of the economic conditions. In addition, researchers can see a strategic application of factor models.     

Optimal prediction under LINLIN loss: Empirical evidence

I compare the forecasts of returns from the mean predictor (optimal under MSE), with the pseudo-optimal and optimal predictor for an asymmetric loss function under the assumption that agents have an asymmetric LINLIN loss function. The results strongly suggest not using the conditional mean predictor under conditions of asymmetry. In general, forecasts can be improved by the use of optimal predictor rather than the pseudo-optimal predictor, suggesting that the loss reduction from using the optimal predictor can actually be important for practitioners as well.     

经济混沌研究的非线性科学方法

本文根据混沌理论和分形理论,对经济混沌特征研究的非线性科学方法进行了必要的总结,具体包括经济序列时间分维的计算方法、关联维的计算方法、Lyapunov指数和Kolmogorov熵的计算方法以及R/S分析方法.在此基础上,对上述方法在经济序列混沌特征的非线性研究中的未来应用进行了必要的展望.     

循环水温经济性分析

本文通过技术经济性分析,对于循环水温经济性分析。     

A bootstrap-assisted spectral test of white noise under unknown dependence

To test for the white noise null hypothesis, we study the Cramér-von Mises test statistic that is based on the sample spectral distribution function. Since the critical values of the test statistic are difficult to obtain, we propose a blockwise wild bootstrap procedure to approximate its asymptotic null distribution. Using a Hilbert space approach, we establish the weak convergence of the difference between the sample spectral distribution function and the true spectral distribution function, as well as the consistency of bootstrap approximation under mild assumptions. Finite sample results from a simulation study and an empirical data analysis are also reported.     

Nonparametric model validations for hidden Markov models with applications in financial econometrics

We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous-time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.     

A unified approach to validating univariate and multivariate conditional distribution models in time series

Modeling conditional distributions in time series has attracted increasing attention in economics and finance. We develop a new class of generalized Cramer–von Mises (GCM) specification tests for time series conditional distribution models using a novel approach, which embeds the empirical distribution function in a spectral framework. Our tests check a large number of lags and are therefore expected to be powerful against neglected dynamics at higher order lags, which is particularly useful for non-Markovian processes. Despite using a large number of lags, our tests do not suffer much from loss of a large number of degrees of freedom, because our approach naturally downweights higher order lags, which is consistent with the stylized fact that economic or financial markets are more affected by recent past events than by remote past events. Unlike the existing methods in the literature, the proposed GCM tests cover both univariate and multivariate conditional distribution models in a unified framework. They exploit the information in the joint conditional distribution of underlying economic processes. Moreover, a class of easy-to-interpret diagnostic procedures are supplemented to gauge possible sources of model misspecifications. Distinct from conventional CM and Kolmogorov–Smirnov (KS) tests, which are also based on the empirical distribution function, our GCM test statistics follow a convenient asymptotic $N(0,1)$ distribution and enjoy the appealing “nuisance parameter free” property that parameter estimation uncertainty has no impact on the asymptotic distribution of the test statistics. Simulation studies show that the tests provide reliable inference for sample sizes often encountered in economics and finance.     

基于单片机控制的非接触式IC卡读卡器设计

本文在深入分析非接触式IC卡工作原理的基础上,提出了一种基于AT89S51单片机控制的S50系列IC卡读卡器的设计方法,并从元器件选型,硬件电路设计,软件程序设计等方面进行了探讨,并结合公共交通收费系统的实际应用,系统地讲解了公交车刷卡机的读写技术和工作原理。     

生态环境的外部性与环境经济政策

生态环境是人类生存和发展的基本条件,是经济、社会发展的基础,生态环境问题已威胁到人类的生存和发展。将外部性概念引入环境保护领域,是为了寻找促使外部性问题内在化的有效方式,以解决日益严重的环境问题。与环境问题有关的外部性,主要是生产和消费的负外部性。环境经济政策是国家环境当局从影响成本—收益入手,引导经济当事人进行选择,以便最终有利于环境的一种政策手段,其主要目标之一是最大限度地减弱以至消除其外部性的影响,将外部性问题内在化。     

基于BP神经网络的时间序列预测研究

建立了基于BP网络的时间序列预测模型,将模型应用于实际算例,设计了模型的网络结构、初始权值和偏差,结果验证了模型的有效性。