几类非线性协整模型研究综述

协整分析方法经过20多年的发展成为计量经济学界的一个前沿工具,在经济与金融领域得到了广泛的应用。线性协整分析已经成熟,而非线性协整的理论与方法仍在持续研究中。本文回顾了最近20年非线性协整的发展历史,其中包括结构变化、门限非线性、马尔可夫转换和平滑转换等几类非线性协整模型,强调了这些非线性机制的本质区别,总结了已取得的一些重要研究成果,最后对该问题的最新发展动向加以概括。     

再谈卓越经营模式

卓越经营模式,在本质上是把组织卓越绩效的因素和随之产生的结果之间联系起来,这些模式也是许多国家、地区乃至国际质量奖评奖的基础     

Bayesian Inference in Spatial Sample Selection Models

In this study, we consider Bayesian methods for the estimation of a sample selection model with spatially correlated disturbance terms. We design a set of Markov chain Monte Carlo algorithms based on the method of data augmentation. The natural parameterization for the covariance structure of our model involves an unidentified parameter that complicates posterior analysis. The unidentified parameter – the variance of the disturbance term in the selection equation – is handled in different ways in these algorithms to achieve identification for other parameters. The Bayesian estimator based on these algorithms can account for the selection bias and the full covariance structure implied by the spatial correlation. We illustrate the implementation of these algorithms through a simulation study and an empirical application.     

Much Ado About Nothing: the Mixed Models Controversy Revisited

We consider a well-known controversy that stems from the use of two mixed models for the analysis of balanced experimental data with a fixed and a random factor. It essentially originates in the different statistics developed from such models for testing that the variance parameter associated to the random factor is null. The corresponding hypotheses are interpreted as that of null random factor main effects in the presence of interaction. The controversy is further complicated by different opinions regarding the appropriateness of such hypothesis. Assuming that this is a sensible option, we show that the standard test statistics obtained under both models are really directed at different hypotheses and conclude that the problem lies in the definition of the main effects and interactions. We use expected values as in the fixed effects case to resolve the controversy showing that under the most commonly used model, the test usually associated to the inexistence of the random factor main effects addresses a different hypothesis. We discuss the choice of models, and some further problems that occur in the presence of unbalanced data.     

Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models

The paper takes up inference in the stochastic frontier model with gamma distributed inefficiency terms, without restricting the gamma distribution to known integer values of its shape parameter (the Erlang form). The paper shows that Gibbs sampling with data augmentation can be used in a computationally efficient way to explore the posterior distribution of the model and conduct inference regarding parameters as well as functions of interest related to technical inefficiency.     

Statistical Inference in Nonparametric Frontier Models: The State of the Art

Efficiency scores of firms are measured by their distance to an estimated production frontier. The economic literature proposes several nonparametric frontier estimators based on the idea of enveloping the data (FDH and DEA-type estimators). Many have claimed that FDH and DEA techniques are non-statistical, as opposed to econometric approaches where particular parametric expressions are posited to model the frontier. We can now define a statistical model allowing determination of the statistical properties of the nonparametric estimators in the multi-output and multi-input case. New results provide the asymptotic sampling distribution of the FDH estimator in a multivariate setting and of the DEA estimator in the bivariate case. Sampling distributions may also be approximated by bootstrap distributions in very general situations. Consequently, statistical inference based on DEA/FDH-type estimators is now possible. These techniques allow correction for the bias of the efficiency estimators and estimation of confidence intervals for the efficiency measures. This paper summarizes the results which are now available, and provides a brief guide to the existing literature. Emphasizing the role of hypotheses and inference, we show how the results can be used or adapted for practical purposes.     

Threshold Effects in Cointegrating Relationships*

In this paper, we introduce threshold‐type nonlinearities within a single‐equation cointegrating regression model and propose a testing procedure for testing the null hypothesis of linear cointegration vs. cointegration with threshold effects. Our framework allows the modelling of long‐run equilibrium relationships that may change according to the magnitude of a threshold variable assumed to be stationary and ergodic, and thus constitutes an attempt to deal econometrically with the potential presence of multiple equilibria. The framework is flexible enough to accommodate regressor endogeneity and serial correlation.     

Statistical Inference,Learning and Models in Big Data

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.     

Inference of Dynamic Generalized Linear Models: On-Line Computation and Appraisal

The purpose of this paper is to provide a critical discussion on real-time estimation of dynamic generalized linear models. We describe and contrast three estimation schemes, the first of which is based on conjugate analysis and linear Bayes methods, the second based on posterior mode estimation, and the third based on sequential Monte Carlo sampling methods, also known as particle filters. For the first scheme, we give a summary of inference components, such as prior/posterior and forecast densities, for the most common response distributions. Considering data of arrivals of tourists in Cyprus, we illustrate the Poisson model, providing a comparative analysis of the above three schemes.     

Disclosure‐protected Inference Using Generalised Linear Models

Vast amounts of data that could be used in the development and evaluation of policy for the benefit of society are collected by statistical agencies. It is therefore no surprise that there is very strong demand from analysts, within business, government, universities and other organisations, to access such data. When allowing access to micro‐data, a statistical agency is obliged, often legally, to ensure that it is unlikely to result in the disclosure of information about a particular person or organisation. Managing the risk of disclosure is referred to as statistical disclosure control (SDC). This paper describes an approach to SDC for output from analysis using generalised linear models, including estimates of regression parameters and their variances, diagnostic statistics and plots. The Australian Bureau of Statistics has implemented the approach in a remote analysis system, which returns analysis output from remotely submitted queries. A framework for measuring disclosure risk associated with a remote server is proposed. The disclosure risk and utility of approach are measured in two real‐life case studies and in simulation.     

基于贝叶斯结构突变的随机系数动态面板模型研究

针对目前随机系数动态面板模型中存在内生变量初始值固定、个体自回归系数平稳以及不存在结构突变的种种限制,本文提出用分层贝叶斯方法首次检测和估计了含未知结构突变的随机系数动态面板模型。容许初始值与个体相关,自回归系数服从logitnormal分布保证平稳性,得到了未知结构突变和随机系数的后验密度估计。对1995年到2012年中国五省市出口总值月度数据进行实证分析,检测出四个结构突变,分析突变前后的情况表明出口总值存在三大特征：呈现稳定增长态势,但省市间差距逐渐扩大;重大的外部需求冲击对出口有显著影响;出口总值的结构突变有明显的季节特征.     

Cointegrating rank selection in models with time-varying variance

Reduced rank regression (RRR) models with time varying heterogeneity are considered. Standard information criteria for selecting cointegrating rank are shown to be weakly consistent in semiparametric RRR models in which the errors have general nonparametric short memory components and shifting volatility provided the penalty coefficient _Cn→∞$C_n\to \infty$ and _Cn/n→0$C_n/n\to 0$ as n→∞$n\to \infty$. The AIC criterion is inconsistent and its limit distribution is given. The results extend those in Cheng and Phillips (2009a) and are useful in empirical work where structural breaks or time evolution in the error variances is present. An empirical application to exchange rate data is provided.     

社区更新机制的弹性:英国模式对中国的启示

社区更新是市场化背景下城市更新多元合作趋势的重要形式。全球化、体制转型等带来的复杂环境使机制弹性成为其可持续发展的关键。已有研究聚焦于理论阐释、实证研究,缺乏对弹性机制建构的研究。英国是社区更新最发达的国家之一,是我国良好的借鉴对象。运用文献法提炼苏格兰地区的三种典型模式,借用社会资本分析各自弹性,比较其异同,发现机制弹性与社区的全面能力、自主管理能力、经济驱动能力、自我持续发展能力直接相关。比较中、英国情差异,提出我国弹性机制建构路径:赋予居委会自主管理与社区公共福利相关事务的权利,促进居委会组织培训和交流、促成社区与专业机构合作,完善信托法制及管理、促进社区公共福利供给与公益信托结合,展开建筑产权差异化管理研究。     

Confidence Sets for the Break Date in Cointegrating Regressions

In this paper, we propose constructing confidence sets for a break date in cointegrating regressions by inverting a test for the break location, which is obtained by maximizing the weighted average of power. It is found that the limiting distribution of the test depends on the number of I(1) regressors whose coefficients sustain structural change and the number of I(1) regressors whose coefficients are fixed throughout the sample. By Monte Carlo simulations, we then show that compared with a confidence interval developed by using the existing method based on the limiting distribution of the break point estimator under the assumption of the shrinking shift, the confidence set proposed in the present paper has a more accurate coverage rate, while the length of the confidence set is comparable. By using the method developed in this paper, we then investigate the cointegrating regressions of Russian macroeconomic variables with oil prices with a break.     

Granger Causality and Regime Inference in Markov Switching VAR Models with Bayesian Methods

In this paper, we derive restrictions for Granger noncausality in MS‐VAR models and show under what conditions a variable does not affect the forecast of the hidden Markov process. To assess the noncausality hypotheses, we apply Bayesian inference. The computational tools include a novel block Metropolis–Hastings sampling algorithm for the estimation of the underlying models. We analyze a system of monthly US data on money and income. The results of testing in MS‐VARs contradict those obtained with linear VARs: the money aggregate M1 helps in forecasting industrial production and in predicting the next period's state. Copyright © 2016 John Wiley & Sons, Ltd.     

The Use of Valuation Models by UK Investment Analysts

This paper examines the use of valuation models by UK investment analysts. The study is based on, first, semi-structured interviews with 35 sell-side analysts from 10 leading investment banks and with 7 buy-side analysts from 3 asset management firms and, second, content analysis based on 98 equity research reports for FTSE-100 companies covered by the sell-side interviewees. We observe that analysts perceive the discounted cash flow (DCF) (and to some extent ‘sophisticated’ models in general) to have become significantly more important than prior survey evidence suggests, although we also find the (somewhat paradoxical) continued importance of ‘unsophisticated’ valuation multiples, notably the price/earnings ratio (PE). We find perceived limitations in the technical applicability of the DCF, which cause analysts to rely in practice upon valuation multiples and subjective judgement of whether the market price ‘feels right’. We also find that contextual factors, notably the analysts' need for their research to be credible to buy-side clients, cause the use of subjective, unsophisticated methods of valuation to be played down. Given the inherent flexibility of the DCF model, coupled with its ostensible credibility, it becomes the natural vehicle for conveying the analyst's research, even though it is very rarely relied upon to determine target prices and investment recommendations. We conclude that, while the literature has focused on the technical merits of alternative valuation models, analysts' actual usage of valuation models also requires an understanding of social and economic context and motivations.     

Interpretation of Cointegrating Coefficients in the Cointegrated Vector Autoregressive Model*

Regression coefficients are interpreted by a counterfactual experiment. For simultaneous equations this experiment can be implemented if the coefficients are identified, and throws some light on the role of instruments and the method of indirect least squares. This paper discusses another counterfactual experiment in the vector autoregressive model in order to interpret the coefficients of an identified cointegrating relation. The dynamics of the model is used to implement a long‐run change by changing the current values. The counterfactual experiment can be conducted precisely when the cointegrating relation is identified.