基于非线性ECM模型的贝叶斯门限协整研究

 现有门限协整检验方法由于模型似然函数具有多峰、不连续特征,导致冗余参数识别存在困难,最优化计算相对复杂。本文提出基于非线性误差修正模型的贝叶斯门限协整分析,结合参数的后验条件分布设计MCMC抽样方案,进行贝叶斯门限协整检验;并利用Monte Carlo仿真研究了贝叶斯门限协整检验的有限样本性质,发现贝叶斯门限协整检验方法具有良好的有限样本性质。同时,利用不同期限的美国利率序列进行了实证研究,结果发现1个月与3个月利率之间、3个月与6个月利率之间以及3个月与1年利率之间均存在门限协整关系。研究结果表明：贝叶斯门限协整检验方法解决了冗余参数识别的难题,使计算变得相对简单,并提高了估计的精确度和检验的准确性。     

基于贝叶斯PSECM模型的非线性协整能源需求研究

文章通过面板数据平滑转换模型研究影响能源需求的主要因素.针对面板数据平滑转换模型的序列差分容易造成信息缺失的问题,进行误差修正,构建PSECM模型,刻画变量的非线性特征与变量之间的长期稳定的非线性关系.由于非线性最小二乘算法难以收敛,容易造成参数估计不准确,运用贝叶斯方法分析模型结构,估计模型参数;在此基础上,对新兴市场国家进行实证分析,研究结果表明:贝叶斯算法能够准确地估计模型各参数,证明了贝叶斯PSECM模型的有效性,能源需求弹性与经济水平、能源价格、金融发展水平之间存在长期稳定非线性协整关系.     

金融发展与贸易依存度的实证研究——基于东部十省市面板数据的协整分析

本文利用东部十省市的面板数据,研究金融发展与贸易依存度的关系。运用面板单位根检验和面板协整检验,建立面板协整模型进行分析。结果显示：金融发展仅与进口依存度存在长期均衡关系,且不同省市金融发展对进口依存度的推动作用不同。最后分析其作用差异的潜在原因,并根据实证结果提出了政策建议。     

我国经济增长与金融深度的关系检验

文章运用协整检验和格兰杰因果检验方法,研究了我国经济增长与金融深度的关系。协整检验结果表明我国的经济增长与金融深度之间不存在长期稳定的均衡关系;格兰杰因果检验结果表明我国的经济增长与金融深度之间存在着单向的由经济增长指向金融深度的因果关系。并且,首次运用阈值效应对此结果给出解释。     

含滞后效应的非线性协整分析

线性协整方法对非平稳经济和金融变量时间序列的计量经济学分析已经比较成熟,但许多宏观经济变量序列以及金融变量序列间的关系往往表现为非线性。文章以NLLS估计为基础提出反映滞后效应的非线性协整回归模型的非线性协整检验方法,统计模拟结果显示该方法具有较小的水平扭曲和较高的势。通过对中国财政支出与城镇居民可支配收入进行实证分析得出两者之间不存在线性协整而存在非线性协整关系,并且这种关系具有滞后效应。     

协整检验的DGP识别

本文用数值模拟的方法比较了不同DGP下协整检验迹统计量的分布特征,分析DGP误设对协整检验结果的重大影响;进而利用我国货币市场和股票市场的数据进行实证分析,通过真实的例子,揭示DGP误设可能导致的各种错误结果。实证分析中还进一步使用递归协整检验和协整关系的约束识别检验,从不同角度显示了协整关系检验结论的稳健性和可靠性。     

中国金融发展与全要素生产率的协整关系检验与波动影响分析

本文运用协整检验与误差修正模型研究金融发展对全要素生产率的影响,重点考察金融发展与全要素生产率间的长期协整关系以及短期波动的影响。     

金融发展和经济增长关系的再检验

根据1978～2011年的数据,建立VAR模型,采用协整检验、Granger检验、脉冲响应和方差分解分析对中国金融发展与经济增长的关系进行了实证再检验.结果表明,长期内金融发展规模的扩大显著推动了经济增长,金融发展效率却随着经济的增长而下降.     

关于分段非线性型变结构协整的研究

纵现金融时间序列的发展变化研究,变结构非线性协整是协整理论发展的必然的趋势,也是经济系统复杂多变的必然需求,文章补充了变结构非线性协整的定义,并提出了分段非线性型变结构的误差校正模型以及变结构点的搜寻的基本方法,最后给出基于Chow统计量的分段非线性型变结构非线性协整的检验方法.     

东、西部农村金融对农村经济增长贡献的比较研究

深化中国农村金融体制改革,必须辨明中国农村金融与农村经济增长的关系。本文基于中国东部和西部的区域数据,运用面板数据单位根检验、协整检验与格兰杰因果检验,对东部和西部金融发展与经济增长的因果关系和因果方向进行了比较研究。     

Bayesian inference for circular distributions with unknown normalising constants

Very often, the likelihoods for circular data sets are of quite complicated forms, and the functional forms of the normalising constants, which depend upon the unknown parameters, are unknown. This latter problem generally precludes rigorous, exact inference (both classical and Bayesian) for circular data.Noting the paucity of literature on Bayesian circular data analysis, and also because realistic data analysis is naturally permitted by the Bayesian paradigm, we address the above problem taking a Bayesian perspective. In particular, we propose a methodology that combines importance sampling and Markov chain Monte Carlo (MCMC) in a very effective manner to sample from the posterior distribution of the parameters, given the circular data. With simulation study and real data analysis, we demonstrate the considerable reliability and flexibility of our proposed methodology in analysing circular data.     

Reconsideration of the F-test of the analysis of variance: the semi-bayesian significance test

The usual F-test of the analysis of variance is reconsidered within the Bayesian framework, In terms of predictive distributions, This leads to the notion of semi-Bayesian significance test, so called because it consists in only probabilizing the space of nuisance parameters, thus bringing a general principle for “eliminating” nuisance parameters, or more exactly incorporating information about these parameters. The approach is shown to extend the F-tests, by allowing the testing of hypotheses of non-zero effects.     

Bayesian approach for meta - analysis of controlled clinical trials

A new method to perform meta - analysis of controlled clinical trials with binary response variable is developed using a Bayesian approach. It consists of three parts: (1) For each trial, the risk difference (the proportion of successes in the treated group minus the proportion of successes in the control group) is estimated; (2) The homogeneity of the risk difference among the different trials is tested; and (3) The hypothesis - the effect of the treatment for the homogeneous pool of trials is greater than or equal to a given fixed constant - is tested. The performance of the Bayesian procedure to test the homogeneity of the risk difference among trials is compared with the chi - square test proposed by DerSimonian and Laird (Controlled Clinical Trials 7, 177-188, 1986) by means of pseudo - random simulation. The conclusion was that the Bayes test is more reliable, either in its exact or asymptotic versions, since it makes fewer decision errors than the chi-square test. As an illustration, the Bayesian method is applied to data of chemotherapeutic prophylaxis of superficial bladder cancer.     

Two-sided tests for change in level for correlated data

The classical problem of change point is considered when the data are assumed to be correlated. The nuisance parameters in the model are the initial level μ and the common variance σ². The four cases, based on none, one, and both of the parameters are known are considered. Likelihood ratio tests are obtained for testing hypotheses regarding the change in level, δ, in each case. Following Henderson (1986), a Bayesian test is obtained for the two sided alternative. Under the Bayesian set up, a locally most powerful unbiased test is derived for the case μ=0 and σ²=1. The exact null distribution function of the Bayesian test statistic is given an integral representation. Methods to obtain exact and approximate critical values are indicated.     

Bayesian estimation of logistic regression with misclassified covariates and response

Measurement error is a commonly addressed problem in psychometrics and the behavioral sciences, particularly where gold standard data either does not exist or are too expensive. The Bayesian approach can be utilized to adjust for the bias that results from measurement error in tests. Bayesian methods offer other practical advantages for the analysis of epidemiological data including the possibility of incorporating relevant prior scientific information and the ability to make inferences that do not rely on large sample assumptions. In this paper we consider a logistic regression model where both the response and a binary covariate are subject to misclassification. We assume both a continuous measure and a binary diagnostic test are available for the response variable but no gold standard test is assumed available. We consider a fully Bayesian analysis that affords such adjustments, accounting for the sources of error and correcting estimates of the regression parameters. Based on the results from our example and simulations, the models that account for misclassification produce more statistically significant results, than the models that ignore misclassification. A real data example on math disorders is considered.     

Approximate Bayesian Inference for Survival Models

Abstract. Bayesian analysis of time‐to‐event data, usually called survival analysis, has received increasing attention in the last years. In Cox‐type models it allows to use information from the full likelihood instead of from a partial likelihood, so that the baseline hazard function and the model parameters can be jointly estimated. In general, Bayesian methods permit a full and exact posterior inference for any parameter or predictive quantity of interest. On the other side, Bayesian inference often relies on Markov chain Monte Carlo (MCMC) techniques which, from the user point of view, may appear slow at delivering answers. In this article, we show how a new inferential tool named integrated nested Laplace approximations can be adapted and applied to many survival models making Bayesian analysis both fast and accurate without having to rely on MCMC‐based inference.     

Approximate reference priors for Gaussian random fields

Reference priors are theoretically attractive for the analysis of geostatistical data since they enable automatic Bayesian analysis and have desirable Bayesian and frequentist properties. But their use is hindered by computational hurdles that make their application in practice challenging. In this work, we derive a new class of default priors that approximate reference priors for the parameters of some Gaussian random fields. It is based on an approximation to the integrated likelihood of the covariance parameters derived from the spectral approximation of stationary random fields. This prior depends on the structure of the mean function and the spectral density of the model evaluated at a set of spectral points associated with an auxiliary regular grid. In addition to preserving the desirable Bayesian and frequentist properties, these approximate reference priors are more stable, and their computations are much less onerous than those of exact reference priors. Unlike exact reference priors, the marginal approximate reference prior of correlation parameter is always proper, regardless of the mean function or the smoothness of the correlation function. This property has important consequences for covariance model selection. An illustration comparing default Bayesian analyses is provided with a dataset of lead pollution in Galicia, Spain.     

Exact and Monte Carlo calculations of integrated likelihoods for the latent class model

The latent class model or multivariate multinomial mixture is a powerful approach for clustering categorical data. It uses a conditional independence assumption given the latent class to which a statistical unit is belonging. In this paper, we exploit the fact that a fully Bayesian analysis with Jeffreys non-informative prior distributions does not involve technical difficulty to propose an exact expression of the integrated complete-data likelihood, which is known as being a meaningful model selection criterion in a clustering perspective. Similarly, a Monte Carlo approximation of the integrated observed-data likelihood can be obtained in two steps: an exact integration over the parameters is followed by an approximation of the sum over all possible partitions through an importance sampling strategy. Then, the exact and the approximate criteria experimentally compete, respectively, with their standard asymptotic BIC approximations for choosing the number of mixture components. Numerical experiments on simulated data and a biological example highlight that asymptotic criteria are usually dramatically more conservative than the non-asymptotic presented criteria, not only for moderate sample sizes as expected but also for quite large sample sizes. This research highlights that asymptotic standard criteria could often fail to select some interesting structures present in the data.     

Bayesian analysis of prevalence from the results of small screening samples

Bayesian analysis is applied to the number of cases screened positive to estimate the disease prevalence and to predict the number of future cases with disease. The analysis makes use of additional experimental information about the test’s sensitivity and specificity and of prior information on the prevalence of disease. Prior and posterior probability distributions of disease prevalence are conjugate mixtures of Beta densities and can be expressed in exact algebraic form.     

Asymptotic Likelihood Based Inference for Co-integrated Homogenous Gaussian Diffusions

In this paper we consider inference for a multivariate Gaussian homogenous diffusion which is co-integrated, i.e. admits a representation in terms of stable relations (ergodic diffusions) plus Brownian motions. We show that inference on co-integration rank (the number of stable relations) in continuous time can be based on existing asymptotic distributions from discrete time co-integration analysis. Likewise the asymptotic distributions of the co-integration parameters are shown to be mixed Gaussian. Special attention is given to the parametrization of the drift terms. It is shown that the asymptotic distribution of the co-integration rank test statistic does not depend on the level of the process as a result of the chosen parametrization.