Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

Synopsis of ‘onychocellid’ cheilostome bryozoan genera

Genera assigned to the cheilostome bryozoan family Onychocellidae are revised based on the skeletal morphology of the type species and, when possible, the type material of these species. All genera are illustrated using scanning electron micrographs, some for the first time. Onychocellidae, which ranges from the Cenomanian stage of the Cretaceous to the Recent, has been a particularly troublesome family because of poorly defined generic concepts correlating at least in part with a paucity of morphological characters. Thirty-five genera are described in this review. Of these, two are recognised as subjective synonyms of other onychocellid genera (Rhebasia and Semieschara), one cannot be sufficiently characterised from the type material (Collura), and two are new: Aechmellina gen. nov. (type species Aechmella falcifera) and Kamilocella gen. nov. (type species Eschara latilabris). A neotype is chosen for Rhagasostoma hexagonum, the type species of Rhagasostoma. A key is provided to assist in the identification of onychocellid genera. Phylogenetic relationships between genera remain obscure and are unlikely to be fully resolved based on skeletal morphology alone. The family as an entity is loosely circumscribed and almost certainly paraphyletic, containing stem genera of other anascan familes such as Lunulitidae, Coscinopleuridae and Aspidostomatidae.

www.zoobank.org/urn:lsid:org:pub:63A31AD2-F049-42CB-A45B-557014DC286E     

体育消费的学理脉络、统计内容及其实施策略

采用文献资料法，依次从学理脉络、统计内容及其实施策略展开关于体育消费以及体育消费统计的讨论。认为：从学理上看，体育消费作为一个复杂概念，既牵涉宏观经济社会发展，也与文化意识形态紧密联系；而作为准确把握体育市场、把脉体育发展情况的基础性工作之一，体育消费统计则应该避免将体育消费概念泛化。应以体育政策为导向，面向人民的美好生活需要，对接国民经济统计制度，考虑建构以“体育教育与培训、体育文化与娱乐、体育保健与康复、体育传媒与信息、体育衣着耐用品、其他体育用品与服务、个人间接体育消费”为核心指标的统计框架，并通过长效工作方案的设计使体育消费统计工作制度化，通过统计调查制度的建构使体育消费统计执行有效化，通过统计数据的测算推算使体育消费统计结果精准化。     

Aristotle Said “Happiness is a State of Activity” — Predicting Mood Through Body Sensing with Smartwatches

We measure and predict states of Activation and Happiness using a body sensing application connected to smartwatches. Through the sensors of commercially available smartwatches we collect individual mood states and correlate them with body sensing data such as acceleration, heart rate, light level data, and location, through the GPS sensor built into the smartphone connected to the smartwatch. We polled users on the smartwatch for seven weeks four times per day asking for their mood state. We found that both Happiness and Activation are negatively correlated with heart beats and with the levels of light. People tend to be happier when they are moving more intensely and are feeling less activated during weekends. We also found that people with a lower Conscientiousness and Neuroticism and higher Agreeableness tend to be happy more frequently. In addition, more Activation can be predicted by lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find that tracking people’s geographical coordinates might play an important role in predicting Happiness and Activation. The methodology we propose is a first step towards building an automated mood tracking system, to be used for better teamwork and in combination with social network analysis studies.     

The modernity of Dedekind’s anticipations contained in What are numbers and what are they good for?

Archive for History of Exact Sciences - We show that Dedekind, in his proof of the principle of definition by mathematical recursion, used implicitly both the concept of an inductive cone from an...     

Long-term increases in clutch size in common snapping turtles (Chelydra serpentina) and diamondback terrapins (Malaclemys terrapin)

Clutch size (CS) is a well-studied life history characteristic, and optimal egg size theory predicts that CS should correlate positively with reproductive investment. Turtles are good subjects for studies of reproductive strategies because few turtle species exhibit parental care; therefore quantifying their short-term reproductive investment is mostly limited to measuring egg size and number. Clutch size is usually reported as an average value for each turtle population, and where CS variation is noted, it is usually attributable to variation in adult body size. In two long-term studies of ecologically dissimilar species we detected a dramatic increase in CS in a common snapping turtle (Chelydra serpentina) population and in a diamondback terrapin (Malaclemys terrapin) population. It is unknown whether these changes are due to either variation in adult body size or resource availability; but the temporal patterns we observed have apparently never been reported previously. These trends remain unexplained and should be explored further.