结构参数计量经济学模型的识别及其应用

结构计量模型可识别性决定了结构参数估计的稳定性。为此,本文从扩展结构参数与简化型参数关系体系的视角讨论了结构参数模型的识别问题。首先,证明了一种识别结构参数模型的秩条件。研究发现,对于具有系数线性约束的联立方程组模型,本文的秩条件等价于Koopmans的秩条件;而且,对于SVAR模型,本文的秩条件推广了Hamilton的三角约束条件、Blanchard和Quah的短期识别约束条件、Gali的长期识别约束条件和Rubio-Ramirez等的合并约束条件。另外,应用本文的秩条件研究了一种DSGE模型的可识别性。     

例说基于可识最小值之识别性与参数估计及特征的关系

讨论基于可识最小值之识别性与参数估计及特征的关系,以二元Marshall-Olkin型Weibull分布为例,存在全部参数可估计且可识别且有识别特征的情形;以二元McKay型伽马分布为例,存在全部参数可估计且部分参数可识别且无识别特征而有其它分离特征的情形,若是基于可识最小值及差值,则是全部参数可估计且全部参数可识别且有识别特征的情形;以二元极值二点分布为例,存在部分参数可估计且部分参数可识别且有识别特征的情形.     

二元寿命分布的识别性及参数估计

讨论二元寿命分布的识别性及参数估计,仅是最小值的分布已知时,只有一个参数可识别,当可识最小值的分布已知时,所有参数皆可识别;由此得到了所有参数的最大似然估计.     

二元一般指数分布的识别性及其参数估计

讨论了一般二元指数分布的识别性问题及参数估计问题.本文证明了两个结论:其一、当只有最大值随机变量的分布已知时,仅一个参数可识别;其二、当可识别最大值的分布已知时,所有参数皆可识别.进一步根据上述结论得到了所有参数的最大似然估计.     

一类因果模型的可识别性条件

因果问题在近代医学,生物学,社会科学的研究中占有非常重要的地位。通过因果关系预见某些行为或策略对研究对象的影响已经成为一些实际研究的最终目的。Rubin(1978)提出了解决因果问题的虚拟事实模型,建立了因果推断统计分析的基本框架。虚拟事实模型的因果效应是以实际观测数据为研究对象的,但又不完全由数据之间的相关性决定,因此在讨论因果效应时存在可识别性问题。如果因果效应可识别,则有可能利用观测数据直接计算因果效应。但是,众 所周知:在不加任何假设或限制的条件下,虚拟事实模型的因果效应是不可识别的。若要研究变量间的因果效应就必须对虚拟事实模型加入某些必要的限制,使因果效应在这些限制下可识别。郑忠国,张艳艳,童行伟在“因果模型因果效应的可识别性研究”中针对控制变量与协变量相互独立的一类模型的可识别性进行了研究,指出在某些特定的可替换性假设之下,模型的因果效应具有可识别性。该文将针对控制变量作用于协变量的虚拟事实模型进行可识别性研究。作者将指出:控制变量是否作用于协变量并不影响因果效应的可识别性和可替换性假设。并给出:此类模型因果效应可唯一确定的充要条件 。      

二元Kundu-Gupta型二点分布参数的最大似然估计

讨论二元Kundu-Gupta型二点分布的识别性及参数估计,已知可识最小值的分布时,则参数可识别;由此得到了参数的最大似然估计;其中二个参数的估计量是无偏的,另外一个参数的估计量的期望不存在;模拟结果显示:估计值均稳定于真值参数.     

一种识别动态因子模型的秩条件——基于Gaussian似然函数的视角

为了提高高维动态因子模型识别的有效性,借鉴SVAR模型的结构分析方法,提出了通过隐性因子的新息ε_t来推断和识别动态因子模型正交结构冲击的分析过程.并且,根据动态因子模型的似然函数表示,通过信息矩阵推导出了动态因子模型识别的秩条件.秩条件仅仅依赖于因子的个数以及约束条件,而不依赖于数据的维数,易于在实际应用中验证动态因子模型的识别性.     

动力学方程组中参数识别模型及其优化性质

本文建立了动力学方程组中参数优化识别的数学模型.这是个含有动态约束的泛函极小化问题.应用线性算子的连续性,证明了该问题最优解的存在性,即参数的可识别性.最后依据弱Gateaux微分,给出并证明达到最优解的一个必要条件.     

二元Burr Ⅲ型分布的识别性和渐近独立性

本文讨论了二元Burr Ⅲ分布的识别性和尾部相关性,若已知可识最小值的分布密度时,所有参数皆可识别;另外,还计算了X,Y之间的尾部相关系数,证明了X,Y之间的渐近独立性.     

基于最佳摄动量法反演浅水海湾水质模型的综合扩散系数

作为浅水海湾水质模型基本参数的综合扩散系数,常通过试算来率定,耗时、经验性强.该文应用渤海湾化学需氧量(COD)的部分监测资料,对污染物的综合扩散系数进行了反演.由于问题的非线性,结合正则化方法对求解过程的不适定性进行处理,应用算子识别摄动法进行求解.利用反演得到的综合扩散系数对渤海湾化学需氧量(COD)、无机氮(IN)、无机磷(IP)的分布进一步模拟,并与监测数据进行对比,结果表明反演的综合扩散系数是合理的.该方法还可用于反演水质模型中的其他参数.     

Codimension-two bifurcation analysis of the continuous stirred tank reactor model with delay

The aim of this paper is to research the dynamical behaviors of the continuous stirred tank reactor (CSTR) model with delay. Firstly, we discuss the situation that its related characteristic equation has a simple zero root and a pair of purely imaginary roots. Secondly, the center manifold method and the normal form method are used to reduce the equation of CSTR model. Finally, some characteristics about the CSTR model can be obtained. We analyze three different topological structure and give entire bifurcation diagrams and phase portraits, which are innovative phenomenon. At the end, we obtain the stable and unstable periodic solutions by numerical simulation.     

Online affine model identification of nonlinear processes using a new adaptive neuro-fuzzy approach

This paper presents a new online identification algorithm to drive an adaptive affine dynamic model for nonlinear and time-varying processes. The new algorithm is devised on the basis of an adaptive neuro-fuzzy modeling approach. Two adaptive neuro-fuzzy models are sequentially identified on the basis of the most recent input-output process data to realize an online affine-type model. A series of simulation test studies has been conducted to demonstrate the efficient capabilities of the proposed algorithm to automatically identify an online affine-type model for two highly nonlinear and time-varying continuous stirred tank reactor (CSTR) benchmark problems having inherent non-affine dynamic model representations. Adequacy assessments of the identified models have been explored using different evaluation measures, including comparison with an adaptive neuro-fuzzy inference system (ANFIS) as the pioneering and the most popular adaptive neuro-fuzzy system with powerful modeling features.     

基于残差修正的GM(1,1)模型在水质预测中的应用研究

将基于残差修正的GM(1,1)模型应用到永定河流域官厅水库出口断面的水质预测中,对常规污染物COD,DO,NH_3-N的浓度值进行趋势预测并对预测结果进行后验差检验.结果表明,与传统的GM(1,1)模型相比,残差修正的GM(1,1)模型能够有效提高预测精度.     

Selection of parameter values in environmental models using sparse data: A case study

Models of environmental processes must often be constructed without the use of extensive data sets. This can occur because the exercise is preliminary (aimed at guiding future data collection) or because requisite data are extremely difficult, expensive, or even impossible to obtain. In such cases traditional, statistically based methods for estimating parameters in the model cannot be applied; in fact, parameter estimation cannot be accomplished in a rigorous way at all. We examine the use of a regionalized sensitivity analysis procedure to select appropriate values for parameters in cases where only sparse, imprecise data are available. The utility of the method is examined in the context of equilibrium and dynamic models for describing water quality and hydrological data in a small catchment in Shehandoah National Park, Virginia. Results demonstrate that (1) models can be “tentatively calibrated” using this procedure; (2) the data most likely to provide a stringent test of the model can be identified; and (3) potential problems with model identifiability can be exposed in a preliminary analysis.     

Methodology of on-line optimisation applied to a chemical reactor

The structure of an on-line optimisation procedure is complex, and may involve several steps from different areas. In this paper, a methodology of on-line optimisation that consists of: steady-state detection, data reconciliation, gross error detection, parameter estimation and optimisation is discussed, and applied under simulation, on a two continuous stirred tank reactor (CSTR) connected in series system. Simulation results show that there are some advantages to be gained when this methodology is applied in conjunction with the ISOPE algorithm.     

Structural identifiability of a model of dialysis

In this paper, structural identifiability from input–output data is considered. A mathematical model to describe a dialysis process based on linear dynamic systems is used and the identifiability of this model is tested. The problem of estimating the parameters and obtaining conditions to assure the uniqueness of the parameters is solved. Some conditions to obtain attractive points for the considered model are given, and finally, the stability problem is analyzed.     

Identifiability of factor analysis: some results and open problems

Identifiability of the factor analysis model is discussed, and some recent results and their relation to previous work are surveyed. Sufficient conditions for local identifiability are presented. A counterexample shows that these conditions are not necessary in general. A simple counting rule for local identifiability of the factor analysis model is given generically. Finally some open problems are formulated.     

Identifiability of intermediate variables on causal paths

We discuss the discovery of causal mechanisms and identifiability of intermediate variables on a causal path. Different from variable selection, we try to distinguish intermediate variables on the causal path from other variables. It is also different from ordinary model selection approaches which do not concern the causal relationships and do not contain unobserved variables. We propose an approach for selecting a causal mechanism depicted by a directed acyclic graph (DAG) with an unobserved variable. We consider several causal networks, and discuss their identifiability by observed data. We show that causal mechanisms of linear structural equation models are not identifiable. Furthermore, we present that causal mechanisms of nonlinear models are identifiable, and we demonstrate the identifiability of causal mechanisms of quadratic equation models. Sensitivity analysis is conducted for the identifiability.     

Estimation of systems with statistically-constrained inputs

This paper discusses the estimation of a class of discrete-time linear stochastic systems with statistically-constrained unknown inputs (UI), which can represent an arbitrary combination of a class of un-modeled dynamics, random UI with unknown covariance matrix and deterministic UI. In filter design, an upper bound filter is explored to compute, recursively and adaptively, the upper bounds of covariance matrices of the state prediction error, innovation and state estimate error. Furthermore, the minimum upper bound filter (MUBF) is obtained via online scalar parameter convex optimization in pursuit of the minimum upper bounds. Two examples, a system with multiple piecewise UIs and a continuous stirred tank reactor (CSTR), are used to illustrate the proposed MUBF scheme and verify its performance.