State‐dependent evaluation of predictive ability

In this paper, we examine the dynamic interdependence via growth spillovers between financial cycle represented by real bank credit growth and business cycle as real GDP growth using dynamic spillover index of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012). Using quarterly data on five SAARC (the South Asian Association for Regional Cooperation) countries over the period 1975Q4-2013Q4, the study reports following empirical regularities: First, there is a limited interdependence between business and financial cycles within SAARC nations. The pattern of dependence is almost homogeneous. Second, the results of across SAARC indicate that the magnitude of dynamic relationship is not as high as studies have reported in the case of other economic interests groups. Third, to credit-growth spillover, India appears to be the primary driver of economic and financial integrations followed by Bangladesh and Pakistan. Fourth, the spillover analysis between SAARC countries and China reveals that the directional interdependence is relatively higher than the SAARC alone. Among macroeconomic indicators, we find that government debt to GDP, inflation and interest rate are the primary determinants of credit growth spillover. Among financial development indicators, the number of mobile subscribers and number of bank branches as financial inclusion variables appears as significant determinants of credit growth spillover than the portfolio equity assets and remittances.

Clinical trial data can be presented in ways that exaggerate treatment effectiveness. Physicians consider therapy more effective, and may be more likely to make inappropriate practice changes, when data are presented in relative terms such as relative risk reduction rather than in absolute terms such as absolute risk reduction and number needed to treat. Our purpose was to determine (1) how frequently continuing medical education (CME) speakers present research data in relative terms compared to absolute terms; (2) how knowledgeable CME speakers and learners are about these terms; and (3) how CME learners want these terms presented. Analysis of videotapes and PowerPoint slides of 26 CME presentations, questionnaire survey of CME speakers and learners, and focus groups with learners. Speakers presented data more frequently in relative than absolute terms, but most frequently in general terms such as frequencies, percentages, graphs, and P-values with no data. Of 1367 PowerPoint slides, 269 presented research data, and of these, 225 (84%) presented data in general terms, 50 (19%) in relative terms and 19 (7%) in absolute terms. CME speakers understood relative and absolute terms better than learners. Approximately 25-35% of speakers and 45-65% of learners could not correctly calculate relative risk reduction, absolute risk reduction, and number needed to treat. Learners wished to have these terms presented in CME programs in a consistent and easily understood format and requested a brief review of them at the beginning of CME programs. Presentation of research data in most CME programs is inadequate to allow learners to make fully informed therapeutic decisions. Speakers and learners need professional development to improve their presentation and understanding of research data.

This chapter introduces a framework and a methodology to manage a SaaS application on top of a PaaS infrastructure. This framework utilizes PaaS policy sets to implement the SaaS provider’s elasticity policy for its application server tier. Adaptation is based on strategy-trees, which allow for systematic capture, representation and reasoning about adaptation variability, based on hierarchically organizing different levels of temporal granularity. Thus, a strategy-tree is utilized at the SaaS layer to actively guide policy set selection at runtime in order to maintain alignment with the SaaS provider’s business objectives. This way, the SaaS provider’s attitudes and preferences reflecting their general business needs are incorporated into the adaptation mechanism in an organized and accessible manner. Results from an experiment conducted on a real cloud are presented in support of this approach.

The evolution of social media popularity exhibits rich temporality, i.e., popularities change over time at various levels of temporal granularity. This is influenced by temporal variations of public attentions or user activities. For example, popularity patterns of street snap on Flickr are observed to depict distinctive fashion styles at specific time scales, such as season-based periodic fluctuations for Trench Coat or one-off peak in days for Evening Dress. However, this fact is often overlooked by existing research of popularity modeling. We present the first study to incorporate multiple time-scale dynamics into predicting online popularity. We propose a novel computational framework in the paper, named Multi-scale Temporalization, for estimating popularity based on multi-scale decomposition and structural reconstruction in a tensor space of user, post, and time by joint low-rank constraints. By considering the noise caused by context inconsistency, we design a data rearrangement step based on context aggregation as preprocessing to enhance contextual relevance of neighboring data in the tensor space. As a result, our approach can leverage multiple levels of temporal characteristics and reduce the noise of data decomposition to improve modeling effectiveness. We evaluate our approach on two large-scale Flickr image datasets with over 1.8 million photos in total, for the task of popularity prediction. The results show that our approach significantly outperforms state-of-the-art popularity prediction techniques, with a relative improvement of 10.9%-47.5% in terms of prediction accuracy.

For companies acting on a global scale, the necessity to monitor and analyze news channels and consumer-generated media on the Web, such as weblogs and n news-groups, is steadily increasing. In particular the identification of novel trends and upcoming issues, as well as their dynamic evolution over time, is of utter importance to corporate communications and market analysts. Automated machine learning systems using clustering techniques have only partially succeeded in addressing these newly arising requirements, failing in their endeavor to properly assign short-term hype topics to long-term trends. We propose an approach which allows to monitor news wire on different levels of temporal granularity, extracting key-phrases that reflect short-term topics as well as longer-term trends by means of statistical language modelling. Moreover, our approach allows for assigning those windows of smaller scope to those of longer intervals.

KNAVE-II is an intelligent interface to a distributed web-based architecture that enables users (e.g., physicians) to query, visualize and explore clinical time-oriented databases. Based on prior studies, we have defined a set of requirements for provision of a service for interactive exploration of time oriented clinical data. The main requirements include the visualization, interactive exploration and explanation of both raw data and multiple levels of concepts abstracted from these data; the exploration of clinical data at different levels of temporal granularity along both absolute (calendar-based) and relative (clinically meaningful) time-lines; the exploration and dynamic visualization of the effects of simulated hypothetical modifications of raw data on the derived concepts; and the provision of generic services (such as statistics, documentation, fast search and retrieval of clinically significant concepts, amongst others). KNAVE-II has been implemented and is currently evaluated by expert clinicians in several medical domains, such as oncology, involving monitoring of chronic patients.

Accurate prediction of citywide crowd activity levels (CALs), i.e., the numbers of participants of citywide crowd activities under different venue categories at certain time and locations, is essential for the city management, the personal service applications, and the entrepreneurs in commercial strategic planning. Existing studies have not thoroughly taken into account the complex spatial and temporal interactions among different categories of CALs and their extreme occurrences, leading to lowered adaptivity and accuracy of their models. To address above concerns, we have proposed IE-CALP , a novel spatio-temporal I nteractive attention-based and E xtreme-aware model for C rowd A ctivity L evel P rediction. The tasks of IE-CALP consist of (a) forecasting the spatial distributions of various CALs at different city regions (spatial CALs), and (b) predicting the number of participants per category of the CALs (categorical CALs). To realize above, we have designed a novel spatial CAL-POI interaction-attentive learning component in IE-CALP to model the spatial interactions across different CAL categories, as well as those among the spatial urban regions and CALs. In addition, IE-CALP incorporate the multi-level trends (e.g., daily and weekly levels of temporal granularity) of CALs through a multi-level temporal feature learning component. Furthermore, to enhance the model adaptivity to extreme CALs (e.g., during extreme urban events or weather conditions), we further take into account the extreme value theory and model the impacts of historical CALs upon the occurrences of extreme CALs. Extensive experiments upon a total of 738,715 CAL records and 246,660 POIs in New York City (NYC), Los Angeles (LA), and Tokyo have further validated the accuracy, adaptivity, and effectiveness of IE-CALP ’s interaction-attentive and extreme-aware CAL predictions.

Race, Business Cycles, and Investment in Graduate Education

The study examines the co-movement between oil price and macroeconomic indicators such as exchange rate and stock indices of major oil importing countries. We differ from previous studies by examining the macroeconomic dynamics of major oil-importing countries during all economic cycles across different frequencies, using wavelet coherence analysis. We use nominal price rather than real price to make the results more meaningful for traders and institutional investors. Our analysis is based on 3012 observations covering the period 2003-14 for fifteen major oil importing countries. Wavelet coherence analysis indicates a high coherence between oil price and macroeconomic indicators across all the countries during the financial crisis. The nominal exchange rates tend to have a negative relationship with benchmark oil prices except Japan in the long run and South Korea in the medium run. Stock indices tend to have a positive relationship with benchmark oil prices in both long and medium run. S&P is leading the oil price, whereas SSE50, Nikkei 225, NIFTY, KOPSI, DAX, CAC, IBEX, FTSSI, FTSEMIB, AEX, TWSE, XU 100, LQ45 and BEL 20 are lagging the oil price in the long run. In the medium term, except for NIFTY, oil price is leading the stock market index. Overall, the results indicate that the oil price and stock indices of the major oil-importing countries are correlated in the long and medium term, but not in the short term. The lead–lag relationship between oil price and macroeconomic indicators are observed to change across frequency and time. While exchange rate offers diversification benefits, stock market indices provide no diversification avenues, since the pattern of co-movement of stock market indices and oil prices are similar across all oil importing countries. The results have implications for individual traders and institutional investors while designing their portfolio for short, medium and long term time horizons.

The paper develops a non-parametric, non-stationary framework for business-cycle dating based on an innovative statistical methodology known as Adaptive Weights Smoothing (AWS). The methodology is used both for the study of the individual macroeconomic time series relevant to the dating of the business cycle as well as for the estimation of their joint dynamic. Since the business cycle is defined as the common dynamic of some set of macroeconomic indicators, its estimation depends fundamentally on the group of series monitored. We apply our dating approach to two sets of US economic indicators including the monthly series of industrial production, nonfarm payroll employment, real income, wholesale-retail trade and gross domestic product (GDP). We find evidence of a change in the methodology of the NBER’s Business-Cycle Dating Committee an extended set of five monthly macroeconomic indicators replaced in the dating of the last recession the set of indicators emphasized by the NBER’s Business- Cycle Dating Committee in recent decades. This change seems to seriously affect the continuity in the outcome of the dating of business cycle. Had the dating been done on the traditional set of indicators, the last recession would have lasted one year and a half longer. We find that, independent of the set of coincident indicators monitored, the last economic contraction began in November 2000, four months before the date of the NBER’s Business-Cycle Dating Committee.

This study presents an assessment of global climate model (GCM) performances for the regions Arabian Peninsula, Brazil and Ukraine, all facing problems related to water availability as well as quality and so are the focus regions for investigations within the Integrated Water Resources Management Project ‘International Water Research Alliance Saxony’. Results from the model performance assessment have been applied to derive estimates of the future climate in the regions by means of ensemble averaging. The study uses GCM data from the third Climate Model Intercomparison Project. Model performances were assessed by the Model Performance Index (MPI) as well as Taylor diagrams applying different reference data sets like reanalysis data as well as observational data sets. The model performances were analysed for different domain sizes. Climate variables of different tropospheric levels were classified by applying projections of the overall model ensemble as well as projections of a subset of models (selected according to the MPI) in variables with different levels of certainty of change. The MPI as well as the reliability ensemble averaging method were employed to calculate ensemble averages for variables with high certainties of change. Results are presented for mean values within the time periods 2040–2060 an 2080–2100. Applying different data sets as reference data shows that model performance crucially depends on the reference data set. The size of the model domain is of minor importance. Model rankings vary between regions. Certain changes are found for temperatures and geopotential heights in all levels but, e.g., not for precipitation.

The article examines modeling the impact of news shocks of US monetary policy on developing countries. The study analyzes the transmission mechanism of the Federal Reserve's interest rate news shock. The authors argue that the volatility of macroeconomic indicators in emerging economies can be explained by changes in monetary policy in other countries, and highlight the main channels of influence: financial sector indicators and trade indicators. In this paper the authors first use the LSDV-estimator of the panel vector autoregression and identify unexpected and news shocks. To achieve this goal, quarterly data was collected from the first quarter of 1998 to the third quarter of 2017 for Russia, along with data starting from the first quarter of 1995 for several developing countries. The results obtained indicate that the expected tightening of US monetary policy leads to a decrease in output, investment, and weakening of exchange rate in Russia. The monetary news regarding an increase in the US Federal Reserve's interest rate in two quarters also results in a short-term increase in trade balance and interest rates. News about future economic developments affects macroeconomic indicators up to two quarters before the implementation of monetary policy changes. Additionally, the news shock accounts for up to 11% of the variation in macroeconomic indicators in Russia. Accordingly, the monetary policy news shock is found to be as significant as an unexpected shock. The paper concludes with the analysis of the role played by various financial and trade channels.

Energy budgets for juvenile Pacific whiteleg shrimp, Litopenaeus vannamei (1.7-4.0 g ) fed with different diets were assessed. The energy partitioning between growth, respiration, ammonia excretion, feces and exuvia was calculated to estimate for total food energy intake. Shrimp oxygen consumption and ammonia excretion were quantified in sealed chambers (25°C, 34 ppt). Energy allocated for growth and feces were obtained from the wet combustion of whole body and feces samples, exuvia was estimated as 10% growth energy. Three diets were tested: 100% chopped fish (Fish), 100% commercial feed (Feed), and 50-50% chopped fish and commercial feed (Mix). Most of the energy from the diets was channeled into respiration (49.7-70.5%). Shrimp fed the Feed diet used more energy in growth (24.4%) than those fed Mix (13.4%) or Fish diets (13.2%), either in absolute (joule) or relative (% of ingested energy) terms. Conversely, energy loss as ammonia excretion was lower in shrimp fed Feed (1.0%) compared to shrimp fed Mix (4.2%) or Fish diets (7.8%). Less energy was lost in feces by shrimp fed Fish diet (7.3%) compared to Mix (20.2%) and Feed diets (22.2%). The calculated energy intake by shrimp fed Fish, Mix and Feed were 0.995, 1.100, and 1.255 kJ ind-1day-1, respectively. According to the O:N atomic ratios (oxygen consumed to nitrogen excreted), protein tented to be the predominant substrate catabolized by shrimp fed Fish (O:N=16±5.2), and Mix (O:N=25±11.6), with increasing use of carbohydrates and/or lipids in the Feed (O:N=74±37.3). Results suggest diet composition may affect energy budget and partitioning differently between metabolism and growth either in absolute or relative terms, as shrimp fed Fish and Mix diets used protein as main substrate for metabolism, whereas shrimp fed Feed diet channeled protein for growth, and lipids and carbohydrates for other metabolic functions.

Nordic strength and history of hamstring injury in Australian Football League players

Soil pH predominantly controls the forms of organic phosphorus in topsoils under natural broadleaved forests along a 2500 km latitudinal gradient