Peter Schmidt: Econometrician and consummate professional

Peter Schmidt has been one of its best-known and most respected econometricians in the profession for four decades. He has brought his talents to many scholarly outlets and societies, and has played a foundational and constructive role in the development of the field of econometrics. Peter Schmidt has also served and led the development of Econometric Reviews since its inception in 1982. His judgment has always been fair, informed, clear, decisive, and constructive. Respect for ideas and scholarship of others, young and old, is second nature to him. This is the best of traits, and Peter serves as an uncommon example to us all. The seventeen articles that make up this Econometric Reviews Special Issue in Honor of Peter Schmidt represent the work of fifty of the very best econometricians in our profession. They honor Professor Schmidt's lifelong accomplishments by providing fundamental research work that reflects many of the broad research themes that have distinguished his long and productive career. These include time series econometrics, panel data econometrics, and stochastic frontier production analysis.     

The foundations of econometrics-are there any

The conclusions of alogically consistent economic theory which strictly adheres to Aristotle's axioms of logic are factually true if its sufficient conditions are all factually true. Alternatively, if a conclusion of such a theory is false, then at- least one of its assumptions is false. Unfortunately, the factual truth of sufficient conditions cannot be established because the problem of induction i s impossible t o solve. It is algo true that the falsity of a conclusion cannot be established in the presence of uncertainty. While the philosophy of instrumentalism applied to sufficient and logically consistent explanations may provide useful solutions to immediate practical problems, the principles of simplicity, parsimony and profligacy--all of them requiring conditional deductive arguments--are useless as criteria for model choice.     

Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics

Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory.     

数字经济、空间效应与全要素生产率

当前,数字经济蓬勃发展,已成为经济增长的新动能。本文从数字产业化与产业数字化 两个维度,采用主成分分析法构建了数字经济发展水平的指标体系,并利用2004-2017年我国省际面板 数据,在克服内生性问题的基础上,实证分析了数字经济发展对全要素生产率的影响。研究发现,数字 经济发展显著促进了全要素生产率的提升。但较之高生产率地区和东部地区,数字经济发展对低生产率地区和中西部地区全要素生产率的提升作用更大。就机制而言,人力资本投资与产业结构升级是数 字经济影响全要素生产率的两个渠道。进一步的空间计量分析表明,数字经济发展不仅会提升本地区 的全要素生产率,还存在显著的空间溢出效应,有助于提升邻近地区的全要素生产率。本文的研究为评估数字经济发展的影响效果提供了数据支撑和分析视角,也为探寻全要素生产率的提升路径提供了政策参考。     

我国基础设施经济增长效应的传导机制实证检验

基础设施作用于交易效率进而促进经济增长的微观机制,能有效促进产出。文章采用空间计量经济学方法,对我国基础设施建设与经济增长的关系进行了实证分析,说明政府在基础设施建设中如果偏重于能大幅度提高交易效率的那些基础设施建设（比如公路、通讯等）,则会较大幅度地提高居民的福利水平。但是,当该比重达到一定水平后,交易效率和福利水平的提高就变得十分有限。这一结论是在提示政府：不能总是拿基础设施投入作为刺激经济增长的惟一手段,它不是拉动内需的灵丹妙药。     

计量经济学中的潜变量模型：一个方法论角度的考察

上世纪中叶,因子分析和典型相关分析方法的发展完善,解决了潜变量的测度及其相关关系衡量问题,奠定了潜变量因果模型的方法论基础。此后,潜变量模型被引入到计量经济学研究领域,依次经历了共同结构范式模型、经典潜变量模型和非经典潜变量模型三个阶段,逐步成为现代计量经济模型的重要组成部分。本文从方法论角度对计量经济学中的潜变量模型发展过程进行了全面考察,比较了各个阶段建模方法论的特征,归纳总结了其发展演化规律,并对下一步研究的重点领域进行了展望。     

农村产业融合发展对新型城镇化的影响--基于省际面板数据

本文厘清了农村产业融合发展影响新型城镇化的内在机制,并基于2007—2018年我国省际面板数据,运用空间计量模型进行实证检验。研究发现:(1)我国新型城镇化水平存在显著的正向空间相关性;(2)全国层面,农村产业融合发展对新型城镇化水平的提升产生显著的正向影响,且具有显著的正向空间溢出效应;(3)区域层面,东部、中部、西部农村产业融合发展对新型城镇化的直接和间接效应均显著为正,中部直接效应最大,东部间接效应最大。据此,本文提出健全金融支撑农村产业融合发展的体系、强化人才支撑农村产业融合发展的能力、推动农村产业融合多元化发展和加强区域间产业协调发展等建议。     

非经典计量经济建模方法论的特征分析与比较研究

 按照内容的沿革,计量经济模型分为经典计量经济模型和非经典计量经济模型。非经典计量经济建模方法论既是经典计量经济建模方法论的发展和延伸,又在建模理念、建模方法、模型应用等方面有着很大的不同。本文从数据类型、模型变量、建模对象、参数形式、建模思想等几个方面,对非经典计量经济建模方法论进行了系统的梳理、归纳和分析,重点比较了其与经典计量经济建模方法论的不同特征,对计量经济模型方法论发展演进的规律进行了初步的总结。     

Econometrics exams and round numbers: Use or misuse of indirect estimation methods?

The use of Monte Carlo methods to generate exam datasets is nowadays a well-established practice among econometrics and statistics examiners all over the world. Its advantages are well known: providing each student a different data set ensures that estimates are actually computed individually, rather than copied from someone sitting nearby. The method however has a major fault: initial “random errors,” such as mistakes in downloading the assigned dataset, might generate downward bias in student evaluation. We propose a set of calibration algorithms, typical of indirect estimation methods, that solve the issue of initial “random errors” and reduce evaluation bias. Ensuring round initial estimates of the parameters for each individual dataset, our calibration procedures allow the students to determine if they have started the exam correctly. When initial estimates are not round numbers, this random error in the initial stage of the exam can be corrected for immediately, thus reducing evaluation bias. The procedure offers the further advantage of rounding markers’ life by allowing them to check round numbers answers only, rather than lists of numbers with many decimal digits¹.