Estimation and diagnostic analysis in skew-generalized-normal regression models

The skew-generalized-normal distribution [Arellano-Valle, RB, Gómez, HW, Quintana, FA. A new class of skew-normal distributions. Comm Statist Theory Methods 2004;33(7):1465–1480] is a class of asymmetric normal distributions, which contains the normal and skew-normal distributions as special cases. The main virtues of this distribution is that it is easy to simulate from and it also supplies a genuine expectation–maximization (EM) algorithm for maximum likelihood estimation. In this paper, we extend the EM algorithm for linear regression models assuming skew-generalized-normal random errors and we develop a diagnostics analyses via local influence and generalized leverage, following Zhu and Lee's approach. This is because Cook's well-known approach would be more complicated to use to obtain measures of local influence. Finally, results obtained for a real data set are reported, illustrating the usefulness of the proposed method.     

The Leverage of Protest: Market,Media, and Reputational Disruption in Social Movement Success

How do social movements gain concessions from large corporations? The ability of protests to attain leverage by imposing disruption costs on their targets is widely assumed but less often tested. In this article, we assess the ability of protests to attain concessions by disrupting three broad sources of interest to firm officials: maximizing shareholder value, gaining positive media, and fostering a well-reputed image. In contrast to the body of research on the benefits to movements from shaping media discourses and damaging the reputations of their targets, we find that only market disruption provides protests with leverage. We show this through statistical analyses of an original database of protests against large corporations in the United States over five years, 2005–2009. This study advances social movement and organizational research by demonstrating the ways in which the interests of large corporations provide insurgents with means of attaining leverage over their targets. It also speaks to the broad debate over the importance of disrupting the material versus symbolic interests of movement targets. Our results suggest that when it comes to obtaining concessions from large corporations, it is material disruption and not symbolic disruption that provides movements with leverage.     

“收益率宽幅”是导致宏观杠杆率攀升的原因吗

从会计学收益的微观视角研究宏观经济运行风险,发现货币性资产与投资性房地产之间的资产收益率差距持续拉大的经济现象,诱发宏观杠杆率攀升、金融风险加大。选取中国2000—2018年的年度数据,构建Koyck模型,实证检验不同类型资产之间的收益率宽幅与政府部门杠杆率、居民部门杠杆率、非金融企业部门杠杆率、实体经济部门杠杆率的相关关系。结果表明,收益率宽幅与政府部门杠杆率、居民部门杠杆率、非金融企业部门杠杆率、实体经济部门杠杆率之间存在着显著的正相关关系,收益率宽幅持续拉大,各部门宏观杠杆率不断攀升,资产泡沫难以抑制,宏观经济运行风险较大,这为平缓去杠杆、守住不发生系统性金融风险的底线提供了新视角。     

A high breakdown,high efficiency and bounded influence modified GM estimator based on support vector regression

Regression analysis aims to estimate the approximate relationship between the response variable and the explanatory variables. This can be done using classical methods such as ordinary least squares. Unfortunately, these methods are very sensitive to anomalous points, often called outliers, in the data set. The main contribution of this article is to propose a new version of the Generalized M-estimator that provides good resistance against vertical outliers and bad leverage points. The advantage of this method over the existing methods is that it does not minimize the weight of the good leverage points, and this increases the efficiency of this estimator. To achieve this goal, the fixed parameters support vector regression technique is used to identify and minimize the weight of outliers and bad leverage points. The effectiveness of the proposed estimator is investigated using real and simulated data sets.     

Heteroskedasticity-consistent interval estimators

The linear regression model is commonly used in applications. One of the assumptions made is that the error variances are constant across all observations. This assumption, known as homoskedasticity, is frequently violated in practice. A commonly used strategy is to estimate the regression parameters by ordinary least squares and to compute standard errors that deliver asymptotically valid inference under both homoskedasticity and heteroskedasticity of an unknown form. Several consistent standard errors have been proposed in the literature, and evaluated in numerical experiments based on their point estimation performance and on the finite sample behaviour of associated hypothesis tests. We build upon the existing literature by constructing heteroskedasticity-consistent interval estimators and numerically evaluating their finite sample performance. Different bootstrap interval estimators are also considered. The numerical results favour the HC4 interval estimator.     

稳杠杆和稳增长目标下最优货币政策规则

针对货币政策如何对宏观杠杆率作出反应的问题,构建福利损失函数、动态IS-NK-Phillips曲线和自然利率内生决定方程所刻画的宏观经济约束环境,求解引入宏观杠杆率的最优利率和数量规则,并基于中国1996～2020年的季度宏观经济数据,采用GMM方法和State-Space方法分别估计固定参数和时变参数的最优货币政策规则。研究发现,中国最优利率规则和数量规则均具有显著盯住宏观杠杆的政策取向,且最优利率政策操作相比最优数量政策更具主动性; 最优利率规则促增长同时宏观杠杆率较为稳定,而最优数量规则熨平经济波动同时容易引发宏观杠杆率大幅波动; 面对重大经济冲击时,央行更加注重稳增长的同时包容温和通胀和适度杠杆。央行应对宏观杠杆率予以关注,发挥利率政策促增长的同时把好货币供应总闸门,进一步拓宽优化中国利率政策框架,延伸名义利率调控通货膨胀、实体经济和宏观杠杆的宽度,由不稳定规则转变为稳定规则,增强利率政策对宏观杠杆目标的调节功效,从而促进宏观经济可持续高质量发展。     

Influence measure for the L1 regression

Because outliers and leverage observations unduly affect the least squares regression, the identification of influential observations is considered an important and integrai part of the analysis. However, very few techniques have been developed for the residual analysis and diagnostics for the minimum sum of absolute errors, L₁ regression. Although the L₁ regression is more resistant to the outliers than the least squares regression, it appears that outliers (leverage) in the predictor variables may affect it. In this paper, our objective is to develop an influence measure for the L₁ regression based on the likelihood displacement function. We illustrate the proposed influence measure with examples.     

On the asymptotic distribution of Cook's distance in logistic regression models

It sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations.     

BL-GARCH models with elliptical distributed innovations

In this work, we discuss the class of bilinear GARCH (BL-GARCH) models that are capable of capturing simultaneously two key properties of non-linear time series: volatility clustering and leverage effects. It has often been observed that the marginal distributions of such time series have heavy tails; thus we examine the BL-GARCH model in a general setting under some non-normal distributions. We investigate some probabilistic properties of this model and we conduct a Monte Carlo experiment to evaluate the small-sample performance of the maximum likelihood estimation (MLE) methodology for various models. Finally, within-sample estimation properties were studied using S&P 500 daily returns, when the features of interest manifest as volatility clustering and leverage effects. The main results suggest that the Student-t BL-GARCH seems highly appropriate to describe the S&P 500 daily returns.