Empirical Limits for Time Series Econometric Models

This paper characterizes empirically achievable limits for time series econometric modeling and forecasting. The approach involves the concept of minimal information loss in time series regression and the paper shows how to derive bounds that delimit the proximity of empirical measures to the true probability measure (the DGP) in models that are of econometric interest. The approach utilizes joint probability measures over the combined space of parameters and observables and the results apply for models with stationary, integrated, and cointegrated data. A theorem due to Rissanen is extended so that it applies directly to probabilities about the relative likelihood (rather than averages), a new way of proving results of the Rissanen type is demonstrated, and the Rissanen theory is extended to nonstationary time series with unit roots, near unit roots, and cointegration of unknown order. The corresponding bound for the minimal information loss in empirical work is shown not to be a constant, in general, but to be proportional to the logarithm of the determinant of the (possibility stochastic) Fisher–information matrix. In fact, the bound that determines proximity to the DGP is generally path dependent, and it depends specifically on the type as well as the number of regressors. For practical purposes, the proximity bound has the asymptotic form (K/2)log n, where K is a new dimensionality factor that depends on the nature of the data as well as the number of parameters in the model. When ‘good’ model selection principles are employed in modeling time series data, we are able to show that our proximity bound quantifies empirical limits even in situations where the models may be incorrectly specified. One of the main implications of the new result is that time trends are more costly than stochastic trends, which are more costly in turn than stationary regressors in achieving proximity to the true density. Thus, in a very real sense and quantifiable manner, the DGP is more elusive when there is nonstationarity in the data. The implications for prediction are explored and a second proximity theorem is given, which provides a bound that measures how close feasible predictors can come to the optimal predictor. Again, the bound has the asymptotic form (K/2)log n, showing that forecasting trends is fundamentally more difficult than forecasting stationary time series, even when the correct form of the model for the trends is known.     

中国人文社会科学学术期刊和作者区域关联性分析——基于2018—2020年复印报刊资料转载数据

我国区域间文化和经济发展状况不均衡,在期刊布局、人才储备、科研力量、科研产出等方面具有显著差异,深入分析中国人文社科学术期刊和作者的区域分布特征,有助于明确人文社科的区域发展格局并引导各区域均衡发展,也有助于促进各区域学术期刊与作者之间的良性互动。相关文献研究表明,区域学术期刊数量与所在地高校数量、人口与GDP等变量都有正向相关性,但鲜有深入分析不同区域学术期刊与作者的互动关系。作者对期刊的选择,是否有区域的偏好或限制;期刊努力开放办刊的过程中,呈现出稿源本地化和主办单位自稿率较高现象,期刊用稿和作者投稿过程中,在多大程度上受区域因素的影响？本文基于复印报刊资料的转载数据,从期刊出版地与作者所在地两个维度出发,提出不同区域的期刊依赖度、作者贡献度、期刊本地稿源率与作者本地投稿率等概念与指标,着力研究期刊出版地与作者机构所在地的互动效果与互动关系,以量化的方式观察期刊开放办刊的实际效果,以及学者投稿的地域倾向性,反映出不同区域的学术流动规律;应用于具体学科,反映出学科期刊与作者的区域分布与流动特点,分析不同区域的学科研究成果流入流出规律;应用于不同区域,刻画出区域内期刊办刊特点与当地作者的互动关系特征,为本区域的期刊发展方向与优秀稿源引导提供切实的衡量工具。研究发现:中国人文社科期刊和作者所在地分布均呈现不均衡性,优秀作者的"马太效应"更强,科研机构区域布局不均衡,叠加科研机构的人才竞争,加剧了科研人才的定向流动与区域富集,而优秀期刊同样呈现区域不均衡,但受到多种因素的影响,聚集效应受到一定限制;不同地区、不同学科的期刊和作者之间的区域关联特征多样,各区域的优秀期刊与优秀作者的关联特征差异较大,在不同学科呈现出"封闭""疏离""失衡"等多种状态,部分特征将对区域的学科发展产生不利的影响。这些特征要求管理政策要进一步优化学术资源配置,促进不同区域学术力量的均衡发展,办刊者也应重视期刊和作者的区域布局问题,加强学术交流,增进期刊与学者间的了解,促使学术协同发展,更要强化以学术评价引导区域均衡发展的机制与方式,将区域分布广度纳入学术期刊、作者、机构的评价范围。     

Is peer regulation perceived as an effective mediator to strengthen the mission orientation of INGO leaders?

International non-governmental organizations (INGOs) operate in competitive funding markets and face political pressures, which can incentivize their leaders to compromise their mission-oriented strategy for more resource security. This article empirically investigates whether INGO leaders perceive peer regulation as an effective governance mechanism that allows them to strengthen their mission-oriented strategy to withstand these economic and political constraints. In particular, we assess whether peer regulation is perceived to promote organizational learning processes. We test a partial least squares structural equation model (PLS-SEM) based on a unique data set from an international survey among 201 INGO leaders from 21 countries. Our findings suggest that INGO leaders indeed perceive that peer regulation strengthens their mission orientation, despite the economic competition and political opportunity constraints their organization faces. This effect is partially mediated by organizational learning processes. Our findings are based on a constructivist perspective that emphasizes the learneffect of peer regulation on INGOs' strategic orientation, and allows the derivation of several managerial recommendations to foster INGOs' strategic autonomy.     

Variable selection in heterogeneous panel data models with cross-sectional dependence

This paper studies the Bridge estimator for a high-dimensional panel data model with heterogeneous varying coefficients, where the random errors are assumed to be serially correlated and cross-sectionally dependent. We establish oracle efficiency and the asymptotic distribution of the Bridge estimator, when the number of covariates increases to infinity with the sample size in both dimensions. A BIC-type criterion is also provided for tuning parameter selection. We further generalise the marginal Bridge estimator for our model to asymptotically correctly identify the covariates with zero coefficients even when the number of covariates is greater than the sample size under a partial orthogonality condition. The finite sample performance of the proposed estimator is demonstrated by simulated data examples, and an empirical application with the US stock dataset is also provided.     

监管政策影响了债券评级质量吗？——理论框架与经验证据

近年来,减少金融监管对信用市场的干扰已成为我国监管部门关注的重点问题。通过构建包含监管部门、评级机构和债券发行方等主体在内的三阶段博弈论模型,从理论上探讨了监管政策对债券评级质量的影响。研究发现,监管部门提高债券评级要求会导致市场充斥大量违约风险较高的债券。为了增加评级收入,评级机构会给出虚高评级。在此基础上,基于2017年4月7日中国证券登记结算有限责任公司调整交易所债券市场上信用债券质押入库资格这一准自然实验,利用2015—2019年我国企业债发行数据,采用PSM-DID方法进一步实证检验了监管政策调整对债券评级质量的影响。经验证据表明,提高债券资格门槛会助推债券评级上升,但政策实施前后债券的利差并未发生显著变化,即监管政策调整助推了债券评级虚高。因此,监管部门需改变通过评级结果来识别债券风险的传统监管思路,应尝试构建多元化的信用监管机制。     

技术依赖、财政支持与府际竞争: 政府数据开放绩效的“推拉模型”研究

党的十八大以来,中央政府针对政府数据开放制定了系列政策,为地方政府自主探索开放数据发展方案释放了激励性政策信号。随着地方政府数据开放实践的深入,地方政府间数据开放绩效的分异和差距愈加凸显。为解释同样的央级激励性政策作用下,地方政府数据开放绩效差异如何形成,基于经典TOE框架的技术、组织和环境分析维度,结合资源基础理论和绩效锦标赛理论,构建了一个解释我国地方政府数据开放绩效影响机制的“推拉模型”。通过定性比较分析,发现技术推力、组织推力和环境拉力共同构成政府数据开放绩效的“推拉”系统。技术依赖、地方财政支持和府际竞争是影响地方政府数据开放绩效的核心机制,地方政府数据开放的绩效差异在“推拉”之间逐渐形成和外显。     

Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence

Entropy is a classical statistical concept with appealing properties. Establishing asymptotic distribution theory for smoothed nonparametric entropy measures of dependence has so far proved challenging. In this paper, we develop an asymptotic theory for a class of kernel‐based smoothed nonparametric entropy measures of serial dependence in a time‐series context. We use this theory to derive the limiting distribution of Granger and Lin's (1994) normalized entropy measure of serial dependence, which was previously not available in the literature. We also apply our theory to construct a new entropy‐based test for serial dependence, providing an alternative to Robinson's (1991) approach. To obtain accurate inferences, we propose and justify a consistent smoothed bootstrap procedure. The naive bootstrap is not consistent for our test. Our test is useful in, for example, testing the random walk hypothesis, evaluating density forecasts, and identifying important lags of a time series. It is asymptotically locally more powerful than Robinson's (1991) test, as is confirmed in our simulation. An application to the daily S&P 500 stock price index illustrates our approach.