Automatic Specification Testing for Vector Autoregressions and Multivariate Nonlinear Time Series Models

This article introduces an automatic test for the correct specification of a vector autoregression (VAR) model. The proposed test statistic is a Portmanteau statistic with an automatic selection of the order of the residual serial correlation tested. The test presents several attractive characteristics: simplicity, robustness, and high power in finite samples. The test is simple to implement since the researcher does not need to specify the order of the autocorrelation tested and the proposed critical values are simple to approximate, without resorting to bootstrap procedures. In addition, the test is robust to the presence of conditional heteroscedasticity of unknown form and accounts for estimation uncertainty without requiring the computation of large-dimensional inverses of near-to-singularity covariance matrices. The basic methodology is extended to general nonlinear multivariate time series models. Simulations show that the proposed test presents higher power than the existing ones for models commonly employed in empirical macroeconomics and empirical finance. Finally, the test is applied to the classical bivariate VAR model for GNP (gross national product) and unemployment of Blanchard and Quah (1989 Blanchard, O. and Quah, D. 1989. “The Dynamic Effects of Aggregate Demand and Supply Disturbances,”. The American Economic Review, 79: 655–673. [Web of Science ®] , [Google Scholar]) and Evans (1989 Evans, G. W. 1989. “Output and Unemployment Dynamics in the United States: 1950–1985,”. Journal of Applied Econometrics, 4: 213–238. [Crossref], [Web of Science ®] , [Google Scholar]). Online supplementary material includes proofs and additional details.     

Challenges in telemedicine equivalence studies

As the number of telemedicine programs continues to grow, the accuracy of diagnosis over interactive televideo is a central concern. Although investigators have begun to address diagnostic equivalency in telehealth clinics, few published studies reflect strong research design. The two presented telehealth programs completed randomized controlled trials in real-world clinical settings that addressed some of the methodologic shortcomings of prior studies. Diagnostic equivalency studies were completed across five telehealth specialty clinics: physical therapy, speech therapy, ambulatory pediatrics, child psychiatry, and developmental disabilities services. The two research teams encountered similar decision points in designing and implementing the equivalency protocols. This article addresses methodologic issues in choosing design, participants, technology, and evaluation measures. Although the paper focuses on interactive televideo, the issues raised are pertinent across telehealth technologies.     

小额信贷的征信规范与立法探讨——一种法律和经济的视角

我国自1993 年由中国社会科学:引入国际小额信贷模式的15 年间，在中央相关文件和央行、银监会的政策指导下，小额信贷取得了一定的发展，但速度相对比较缓慢。小额信贷作为一项金融创新产品，在为农民和低收入者提供贷款解决生产、生活问题的过程中发挥了重要作用。借鉴国际小额信贷的典型经验，开展中国小额信贷项目，首先要明确其合法性地位和穷人享有平等的信贷权利等问题。文章从小额信贷在中国发展所需的政策环境、制度监管、信用基础、征信规范和法律保障等方面谈几点看法。     

Divergence versus decision P-values: A distinction worth making in theory and keeping in practice: Or,how divergence P-values measure evidence even when decision P-values do not

There are two distinct definitions of “P-value” for evaluating a proposed hypothesis or model for the process generating an observed dataset. The original definition starts with a measure of the divergence of the dataset from what was expected under the model, such as a sum of squares or a deviance statistic. A P-value is then the ordinal location of the measure in a reference distribution computed from the model and the data, and is treated as a unit-scaled index of compatibility between the data and the model. In the other definition, a P-value is a random variable on the unit interval whose realizations can be compared to a cutoff α to generate a decision rule with known error rates under the model and specific alternatives. It is commonly assumed that realizations of such decision P-values always correspond to divergence P-values. But this need not be so: Decision P-values can violate intuitive single-sample coherence criteria where divergence P-values do not. It is thus argued that divergence and decision P-values should be carefully distinguished in teaching, and that divergence P-values are the relevant choice when the analysis goal is to summarize evidence rather than implement a decision rule.