Retrospective change detection in categorical time series

The aim of this paper is to propose methods of detecting change in the coefficients of a multinomial logistic regression model for categorical time series offline. The alternatives to the null hypothesis of stationarity can be either the hypothesis that it is not true, or that there is a temporary change in the sequence. We use the efficient score vector of the partial likelihood function. This has several advantages. First, the alternative value of the parameter does not have to be estimated; hence, we have a procedure that has a simple structure with only one parameter estimation using all available observations. This is in contrast with the generalized likelihood ratio-based change point tests. The efficient score vector is used in various ways. As a vector, its components correspond to the different components of the multinomial logistic regression model’s parameter vector. Using its quadratic form a test can be defined, where the presence of a change in any or all parameters is tested for. If there are too many parameters one can test for any subset while treating the rest as nuisance parameters. Our motivating example is a DNA sequence of four categories, and our test result shows that in the published data the distribution of the four categories is not stationary.     

An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves

We propose an adaptive functional autoregressive (AFAR) forecast model to predict electricity price curves. With time-varying operators, the AFAR model can be safely used in both stationary and nonstationary situations. A closed-form maximum likelihood (ML) estimator is derived under stationarity. The result is further extended for nonstationarity, where the time-dependent operators are adaptively estimated under local homogeneity. We provide theoretical results of the ML estimator and the adaptive estimator. Simulation study illustrates nice finite sample performance of the AFAR modeling. The AFAR model also exhibits a superior accuracy in the forecast exercise of the California electricity daily price curves compared to several alternatives.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.     

Weighted simulated integrated conditional moment tests for parametric conditional distributions of stationary time series processes

In this article, we propose a weighted simulated integrated conditional moment (WSICM) test of the validity of parametric specifications of conditional distribution models for stationary time series data, by combining the weighted integrated conditional moment (ICM) test of Bierens (1984 Bierens, H. J. (1984). Model specification testing of time series regressions. Journal of Econometrics 26:323–353.[Crossref], [Web of Science ®] , [Google Scholar]) for time series regression models with the simulated ICM test of Bierens and Wang (2012 Bierens, H. J., Wang, L. (2012). Integrated conditional moment tests for parametric conditional distributions. Econometric Theory 28:328–362.[Crossref], [Web of Science ®] , [Google Scholar]) of conditional distribution models for cross-section data. To the best of our knowledge, no other consistent test for parametric conditional time series distributions has been proposed yet in the literature, despite consistency claims made by some authors.     

基于格兰杰因果关系检验的ISM改进与应用

文章提出了一种基于格兰杰因果关系检验的解释结构模型(ISM)改进方法,该方法能依据客观统计数据识别影响因素的两两关系,进而改善了原有模型中采用专家评价法确定邻接矩阵的主观性困境.通过对物流与经济协同发展水平的影响因素分析,实证检验了解释结构模型改进方法的可行性.     

基于DPN模型的我国出口拉动增加值时滞模型

由于我国加工出口占比较高,学者们提出了区分贸易方式的非竞争型投入产出模型.该模型可以更加准确地测算我国出口对增加值的拉动,但是该模型忽略了出口对增加值拉动的时滞性,这容易产生所有效应都在当年发生的误解.文章基于区分贸易方式的非竞争型投入产出模型构建了各部门出口对增加值拉动的时滞模型.实证结果表明:非加工出口部门的时滞长于加工出口部门的时滞.     

静态金融可计算一般均衡扩展模型构建及货币政策模拟

文章将要素价格完全内生化,并通过引入“货币乘数”和“消费价格指数”,构建了扩展的静态金融可计算一般均衡模型,模拟了货币发行规模变化和存款准备金水平的改变对我国宏观经济、各产业部门和价格水平的影响.结论表明:从货币角度看,价格水平变化并不单一地依赖于货币总量的改变,准备金对货币市场调控效果远强于货币发行,而无论何种宽松的货币政策,央行释放的货币与银行信贷资金增加余额不成比例,银行惜贷现象仍较为严重;从产业角度看,增加货币供给会提高劳动要素报酬而降低资本要素支付,无论采取宽松或是紧缩的货币政策,农业相较于其他产业,对货币的弹性较大.     

基于非平稳面板计量的中国经济动态效率分析

戴蒙德的代际交叠模型从理论上阐述了处于长期均衡增长路径的经济体运行可能是动态无效的.文章基于前人对AMSZ准则的扩展,根据中国1997-2015年的省际面板数据,利用新发展的非平稳面板计量方法进行了实证分析.克服了以往研究指标选取上的争议和小样本带来的估计偏误.结果发现:中国经济运行是动态无效率的,资本存量已经超过了黄金律水平.     

不完全数据多重插补的Bootstrap方差估计

当对插补所得的“完整数据集”使用标准的完全数据统计方法的时候,往往会低估插补估计量的方差.Bootstrap方法(自助法)是非参数统计中的一种重要的统计方法,是基于原始观测数据进行重复抽样,能充分的利用已知数据,不需要对未知总体进行任何的分布假设或增加新的样本信息,进而再利用现有的统计模型对总体的分布特性进行统计推断.本文首先运用多重插补的方法对缺失数据进行了插补,之后利用Bootstrap方法对插补之后的数据进行了插补统计量的方差估计,结果表明运用Bootstrap方法进行插补统计量的方差估计更科学更准确.     

中国十大城市群城镇化水平与城市脆弱性的脱钩关系研究

文章基于中国十大城市群内100个地级以上城市的相关数据,通过构建城镇化水平与城市脆弱性综合评价指标体系,采用因子分析法和Tapio脱钧指数法,实证研究了中国十大城市群及所属城市的城镇化水平与城市脆弱性的脱钩关系,并对其脱钩状态进行判别和分析比较.结果表明:京津翼城市群城镇化水平与城市脆弱性的脱钩状态最理想,辽中南城市群、长三角城市群、山东半岛城市群和川渝城市群均为相对理想的脱钩状态,其他城市群的脱钩状况均不容乐观;在17个中心城市中,仅有洛阳、深圳、成都和西安4个城市的城镇化水平与城市脆弱性的脱钩状态相对乐观,经济发展水平较高的北京、上海和广州3大城市的脱钩状态均不理想.