基于PDL模型的城市化水平预测方法研究

在城市化水平预测领域,经济因素相关分析法因其在预测时充分考虑了经济发展对城市化水平的影响而得到广泛使用。然而,在以往研究中,当实际应用经济因素相关分析法对城市化水平进行预测时,往往并未从动态发展的角度出发,将既往历年的经济发展水平作为解释变量引入回归方程中去,在此基础上进行实际预测时必然会因为遗漏掉重要信息而使预测结果的可信性大打折扣。在对混合有限PDL模型应用于我国城市化水平预测领域的必要性进行阐述的基础之上,以PDL及ARMA模型的组合应用为例,对于经济因素相关分析法在我国城市化水平预测领域的应用进行了尝试性的探讨分析。结果表明,与仅考虑了当期经济发展水平对城市化发展的影响所建立的传统线性预测模型相比,PDL模型的预测效果更为理想。     

Non-stationary structural model with time-varying demand elasticities

The paper considers local linear regression of a time series model with non-stationary regressors and errors. Asymptotic property of the local linear estimator is derived under a new dependence measure of non-stationary time series. We apply the local linear regression method to estimate the “time-varying” coefficients of an economic-causal model for the industrial sector of the U.S. economy. Nonparametric bootstrap test on the time-varying coefficients strongly suggests that the price/income elasticities of the U.S. durable goods demand are time-varying.     

Value-at-risk forecasting based on Gaussian mixture ARMA–GARCH model

In this paper, we develop a new forecasting algorithm for value-at-risk (VaR) based on ARMA–GARCH (autoregressive moving average–generalized autoregressive conditional heteroskedastic) models whose innovations follow a Gaussian mixture distribution. For the parameter estimation, we employ the conditional least squares and quasi-maximum-likelihood estimator (QMLE) for ARMA and GARCH parameters, respectively. In particular, Gaussian mixture parameters are estimated based on the residuals obtained from the QMLE of GARCH parameters. Our algorithm provides a handy methodology, spending much less time in calculation than the existing resampling and bias-correction method developed in Hartz et al. [Accurate value-at-risk forecasting based on the normal-GARCH model, Comput. Stat. Data Anal. 50 (2006), pp. 3032–3052]. Through a simulation study and a real-data analysis, it is shown that our method provides an accurate VaR prediction.     

Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure

ABSTRACT

We introduce a class of large Bayesian vector autoregressions (BVARs) that allows for non-Gaussian, heteroscedastic, and serially dependent innovations. To make estimation computationally tractable, we exploit a certain Kronecker structure of the likelihood implied by this class of models. We propose a unified approach for estimating these models using Markov chain Monte Carlo (MCMC) methods. In an application that involves 20 macroeconomic variables, we find that these BVARs with more flexible covariance structures outperform the standard variant with independent, homoscedastic Gaussian innovations in both in-sample model-fit and out-of-sample forecast performance.     

Testing Alphas in Conditional Time-Varying Factor Models With High-Dimensional Assets

ABSTRACT

For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite sample performance and the usefulness of the proposed tests. Using the HDA test, the empirical example demonstrates that the FF three-factor model is better than CAPM in explaining the mean-variance efficiency of both the Chinese and U.S. stock markets. Furthermore, our results suggest that the U.S. stock market is more efficient in terms of mean-variance efficiency than the Chinese stock market. Supplementary materials for this article are available online.     

初次分配中劳动报酬比重测算方法研究

 统计资料的限制和统计口径的变化使得劳动报酬比重测算存在诸多困难。本文分析了资金流量表和投入产出表测度劳动报酬比重的优缺点,并提出利用时变弹性生产函数测度劳动报酬比重的新思路,从而为测度劳动报酬比重提供新的研究视角,为国民核算资料提供新的参考依据。实证研究结果表明,利用时变弹性生产函数测算初次分配中劳动报酬比重具有可行性、准确性和稳定性等优点,是一种可选择的新方法。     

集装箱港口预测研究方法: 香港港实证研究

基于系统科学理论基础及港口经济理论,构建了港口系统研究分析框架． 该分析框架反映了港口复杂系统的整个系统特性、功能和空间结构特征,归纳了港口系统管理和运行的整体性及其与系统各部分的相互关系． 不仅能够构成特殊的研究和认识港口系统的棱镜,而且还有着重大的研究港口系统方法论的价值． 其次,基于所构建的港口系统研究分析框架,设计了基于TIE@I 方法论的港口系统集成预测研究方法． 对香港港集装箱运量的预测研究结果表明,基于TEI@I 方法论的港口系统集成预测研究方法的预测效果远远优于其它模型的预测效果．     

基于ARMA模型的甘肃就业结构变动趋势分析

利用甘肃省1983-2010年三次产业就业数据,建立甘肃省三次产业就业人数的ARMA模型,对甘肃省"十二五"时期三次产业就业人数变化趋势进行预测。结果表明:第一产业就业人数呈上升趋势,就业人数比重呈波浪型趋势,第二产业就业人数和所占比重均呈下降趋势,第三产业就业人数和所占比重均呈上升趋势。最后,针对甘肃实际提出甘肃产业就业结构优化的对策建议。     

A design for testing clinical strategies: biased adaptive within-subject randomization

We propose a method for assigning treatment in clinical trials, called the 'biased coin adaptive within-subject' (BCAWS) design: during the course of follow-up, the subject's response to a treatment is used to influence the future treatment, through a 'biased coin' algorithm. This design results in treatment patterns that are closer to actual clinical practice and may be more acceptable to patients with chronic disease than the usual fixed trial regimens, which often suffer from drop-out and non-adherence. In this work, we show how to use the BCAWS design to compare treatment strategies, and we provide a simple example to illustrate the method.     

中国股市已实现?系数的特征分析与建模研究

利用已实现波动方法对上海股市404 只股票的周?系数进行实时估计，分行业对已实现周?系数进行了统计分析，结果表明。高科技行业的?系数同其他行业的?系数有显著性差异。探讨了股本与行业因素对不同模型预测?系数结果的影响，研究表明，平均绝对预测误差MAE和均方预测误差MSE表明预测精度有随着股本增大而减小的趋势，并且保守型行业预测精度要普遍好于高科技行业的预测精度。