宽带对中国经济增长影响的实证分析

本文运用2000年至2011年中国宽带渗透省际面板数据,研究了宽带与中国经济增长之间的关系,发现宽带引入和渗透对中国经济增长起到了显著推动作用,2000年到2011年的数据表明,宽带渗透率每增长10% 能带动人均GDP年增长率增加约0.19个百分点。分析表明宽带渗透对经济增长的贡献存在一定的网络效应,当宽带渗透率达到约10%后,宽带渗透对人均GDP年增长率的贡献率增加。此外,宽带对中国经济增长的影响具有区域差异性,宽带对于东部、中部经济发展的推动作用显著,而对西部还不明显,可能的原因是西部信息化应用水平与东、中部地区还有较大差距。截止2012年,中国宽带渗透水平以及宽带传输速率与发达国家还有明显差距,并有进一步加大的趋势,实施宽带中国战略具有现实紧迫性。     

固定效应面板线性回归模型的移动分块经验似然估计

本文针对固定效应面板线性回归模型中特意误差项为任意形式序列相关情形,提出了移动分块经验似然估计方法,并给出了大样本性质。模拟研究表明：该方法适用于特意误差项序列相关形式已知和形式未知两种情形,较Baltagi和Li（1994）以及Gon?alves（2011）提出的方法有效。本文采用该方法对CO2排放量与城市化水平之间的关系进行了实证分析,结果表明：城市化水平对CO2排放量有显著影响,不同城市化阶段对CO2排放量影响不同。     

中国工业行业能源CES生产函数的适用性研究及非线性计量估算

本文首次对我国1980-2011年间36个工业大类行业CES生产函数中资本、能源和劳动力之间三种嵌套形式的参数做了非线性计量估计,并以资本/劳动力比率为门限变量,对我国CES生产函数的门限效应做了考察。本文研究表明：①对于三种CES嵌套形式,多数行业能拒绝替代弹性等于1的原假设,这表明目前很多研究在构建模型时选择使用Cobb-Douglas函数形式的做法有待商榷;②对于三种CES嵌套形式,仅有极少数行业不能拒绝规模报酬等于1的原假设,这表明在构建模型时假定规模报酬不变有较大风险;③总体上,多数行业能拒绝把三种要素放在一个核里的原假设,因此对CES函数进行适当的嵌套十分必要;④综合来看,资本与能源先聚合再与劳动力聚合的嵌套形式(KE)L比较符合我国工业实际情况;⑤多数行业门限效应显著,且门限值大多出现在20世纪90年代之后,表明不少行业随资本/劳动力比率增长到一定程度后,要素替代弹性、技术进步参数及规模报酬等的表现有显著变化。     

A Superstructure of Process Capability Indices for Circular Specification Region

Circular specification regions can be seen in processes like hitting a target (in ballistics), drilling a hole (in manufacturing industries) and so on. However, only a few process capability indices are available in the literature to address the problem. Most of these indices, in turn, make some assumptions like equality of variance and independence of the two axes of the circular tolerance region. Since, in most of the cases, these assumptions are not practically viable, in the present article, we have proposed a few of the process capability indices which do not need the above assumptions to be valid. Also, we propose a superstructure which unifies all the proposed indices. Some properties of these indices have been studied including the threshold value and the relationship of the proportion of non-conformance with the member indices of the superstructure. These strengthen the practical utility of the superstructure. Distributional properties like expectations and variances of the member indices of the superstructure are also studied to have a better insight about the indices. A real life example has been discussed to carry out a comparative study of the performance of the existing as well as the newly developed indices.     

Threshold Vector Arma Models

In this article, we propose the threshold vector autoregressive moving average model (TVARMA). It is a multivariate nonlinear time series model characterized by two or more regimes that follow a vector ARMA structure and where the switching among them is regulated by a latent variable. The TVARMA model represents a generalization of some nonlinear models proposed in the literature and shows interesting features that are explored. The condition for the strong and weak stationarity of the TVARMA model are presented and the moments up to order two of the process are derived.     

Flexible Threshold Models for Modelling Interest Rate Volatility

This paper focuses on interest rate models with regime switching and extends previous nonlinear threshold models by relaxing the assumption of a fixed number of regimes. Instead we suggest automatic model determination through Bayesian inference via the reversible jump Markov Chain Monte Carlo (MCMC) algorithm. Moreover, we allow the thresholds in the volatility to be driven not only by the interest rate but also by other economic factors. We illustrate our methodology by applying it to interest rates and other economic factors of the American economy.     

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ABSTRACT

In the class of stochastic volatility (SV) models, leverage effects are typically specified through the direct correlation between the innovations in both returns and volatility, resulting in the dynamic leverage (DL) model. Recently, two asymmetric SV models based on threshold effects have been proposed in the literature. As such models consider only the sign of the previous return and neglect its magnitude, this paper proposes a dynamic asymmetric leverage (DAL) model that accommodates the direct correlation as well as the sign and magnitude of the threshold effects. A special case of the DAL model with zero direct correlation between the innovations is the asymmetric leverage (AL) model. The dynamic asymmetric leverage models are estimated by the Monte Carlo likelihood (MCL) method. Monte Carlo experiments are presented to examine the finite sample properties of the estimator. For a sample size of T = 2000 with 500 replications, the sample means, standard deviations, and root mean squared errors of the MCL estimators indicate only a small finite sample bias. The empirical estimates for S&;P 500 and TOPIX financial returns, and USD/AUD and YEN/USD exchange rates, indicate that the DAL class, including the DL and AL models, is generally superior to threshold SV models with respect to AIC and BIC, with AL typically providing the best fit to the data.     

A Multivariate Threshold Varying Conditional Correlations Model

In this article, a multivariate threshold varying conditional correlation (TVCC) model is proposed. The model extends the idea of Engle (2002 Engle , R. F. ( 2002 ). Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models . Journal of Business and Economic Statistics 20 ( 3 ): 339 – 350 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Tse and Tsui (2002 Tse , Y. K. , Tsui , A. K. C. ( 2002 ). A multivariate GARCH model with time-varying correlations . Journal of Business and Economic Statistics , July 2002 , 20 ( 3 ): 351 – 362 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) to a threshold framework. This model retains the interpretation of the univariate threshold GARCH model and allows for dynamic conditional correlations. Techniques of model identification, estimation, and model checking are developed. Some simulation results are reported on the finite sample distribution of the maximum likelihood estimate of the TVCC model. Real examples demonstrate the asymmetric behavior of the mean and the variance in financial time series and the ability of the TVCC model to capture these phenomena.     

Random balanced resampling: A new method for estimating variance components in unbalanced designs

Variance components in factorial designs with balanced data are commonly estimated by equating mean squares to expected mean squares. For unbalanced data, the usual extensions of this approach are the Henderson methods, which require formulas that are rather involved. Alternatively, maximum likelihood estimation based on normality has been proposed. Although the algorithm for maximum likelihood is computationally complex, programs exist in some statistical packages. This article introduces a simpler method, that of creating a balanced data set by resampling from the original one. Revised formulas for expected mean squares are presented for the two-way case; they are easily generalized to larger factorial designs. The results of a number of simulation studies indicate that, in certain types of designs, the proposed method has performance advantages over both the Henderson Method I and maximum likelihood estimators.     

门限协整套利:理论与实证研究

 不同市场上的同质或相似商品的价格存在长期均衡关系,当价格偏离均衡时,由于套利交易的存在,偏离会迅速回到均衡。在一定的门限值以外,二者服从协整关系,在门限值以内,二者没有协整关系,这种关系称为门限协整。本文在Balke,Fomby(1997)[1]和Hasen（1996）[6]的基础上提出了基于门限向量误差修正模型（T-VECM）的sup-Wald检验,用Bootstrap方法模拟统计量的渐进分布,验证了英国富时指数期货（uk100）和德国法兰克福指数期货（ger30）的门限协整关系,并用Hasen,Seo（2002）[11]提出的极大似然估计方法（MLE）同时估计出门限参数和协整向量,并给出了在这种门限协整关系下进行跨市场无风险套利的策略。