Some evidence of purchasing power parity

Empirical support for purchasing power parity is mixed with results dependent on the time frame and countries under examination, the methodology employed, attempts to control for aggregation bias in the data, and whether adjustments are made to account for productivity differences across nations. As a central component of macroeconomic thinking, purchasing power parity is battered and battle-worn. Using methods that allow for breaking means and trends, this research note provides irrefutable evidence in favor of purchasing power parity for a wide range of countries since the late 1800s. The results suggest managers can consider purchasing power parity a long-term anchor around which they can build their strategic plans

Revising Fiscal Policy and Growth in Saudi Arabia

This article empirically investigates how private investment and different categories of public expenditure (defense, education, health care, and housing) impact real non-oil GDP in Saudi Arabia. The econometric analysis couples unit root, stationarity, and cointegration analysis with vector error correction models. Impulse response functions are applied to examine the impacts of different shocks to the system. We find that public expenditures on health care and defense have decreased real non-oil GDP while public expenditure on education and housing have very little impact. Interestingly, public expenditures on health crowds-out private investment

Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations

Using Association Rule Mining to Predict Opponent Deck Content in Android: Netrunner

As part of their design, card games often include information that is hidden from opponents and represents a strategic advantage if discovered. A player that can discover this information will be able to alter their strategy based on the nature of that information, and therefore become a more competent opponent. In this paper, we employ association rule-mining techniques for predicting item multisets, and show them to be effective in predicting the content of Netrunner decks. We then apply different modifications based on heuristic knowledge of the Netrunner game, and show the effectiveness of techniques which consider this knowledge during rule generation and prediction

Narrative Bytes : Data-Driven Content Production in Esports

Esports - video games played competitively that are broadcast to large audiences - are a rapidly growing new form of mainstream entertainment. Esports borrow from traditional TV, but are a qualitatively different genre, due to the high flexibility of content capture and availability of detailed gameplay data. Indeed, in esports, there is access to both real-time and historical data about any action taken in the virtual world. This aspect motivates the research presented here, the question asked being: can the information buried deep in such data, unavailable to the human eye, be unlocked and used to improve the live broadcast compilations of the events? In this paper, we present a large-scale case study of a production tool called Echo, which we developed in close collaboration with leading industry stakeholders. Echo uses live and historic match data to detect extraordinary player performances in the popular esport Dota 2, and dynamically translates interesting data points into audience-facing graphics. Echo was deployed at one of the largest yearly Dota 2 tournaments, which was watched by 25 million people. An analysis of 40 hours of video, over 46,000 live chat messages, and feedback of 98 audience members showed that Echo measurably affected the range and quality of storytelling, increased audience engagement, and invoked rich emotional response among viewers

Win Prediction in Multi-Player Esports: Live Professional Match Prediction

Esports are competitive videogames watched by audiences. Most esports generate detailed data for each match that are publicly available. Esports analytics research is focused on predicting match outcomes. Previous research has emphasised pre-match prediction and used data from amateur games, which are more easily available than professional level. However, the commercial value of win prediction exists at the professional level. Furthermore, predicting real-time data is unexplored, as is its potential for informing audiences. Here we present the first comprehensive case study on live win prediction in a professional esport. We provide a literature review for win prediction in a multi-player online battle arena (MOBA) esport. The paper evaluates the first professional-level prediction models for live DotA 2 matches, one of the most popular MOBA games and trials it at a major international esports tournament. Using standard machine learning models, feature engineering and optimization, our model is up to 85\% accurate after 5 minutes of gameplay. Our analyses highlight the need for algorithm evaluation and optimization. Finally, we present implications for the esports/game analytics domains, describe commercial opportunities, practical challenges, and propose a set of evaluation criteria for research on esports win prediction

Psychometric properties of the Dutch Five Facet Mindfulness Questionnaire (FFMQ) in patients with fibromyalgia

Mindfulness-based interventions are increasingly being used in clinical populations to reduce psychological distress and improve functioning. The Five Facet Mindfulness Questionnaire (FFMQ) is a questionnaire that measures five facets of mindfulness: observe, describe, actaware, nonjudge and nonreact. The goal of this study was to examine the psychometric properties of the FFMQ in a clinical population of fibromyalgia patients. A total of 141 patients completed an online questionnaire on mindfulness (FFMQ) and theoretically related (e.g. acceptance, openness, alexithymia) and unrelated (physical health) constructs. Thirty-eight patients filled in the FFMQ twice. A confirmatory factor analysis (CFA) was conducted to test the five-factor structure of the FFMQ. Internal consistency and test–retest reliability were respectively assessed with Cronbach’s α and intraclass correlation coefficients. Construct validity was examined by correlating FFMQ facets with theoretically related and unrelated constructs. Incremental validity in predicting mental health and psychological symptoms was examined with regression analyses. CFA confirmed the correlated five-factor structure of the FFMQ. Internal consistency of the five facets was satisfactory and test–retest reliability was good to excellent. Construct validity was excellent, as shown by the moderate to large correlations with related constructs (except observe facet) and weak correlation with a theoretically unrelated construct. Two of the five facets (actaware and nonjudge) had incremental validity over the others in predicting mental health and psychological symptoms. After controlling for related constructs, the actaware facet remained a significant predictor. This study showed satisfactory psychometric properties of the Dutch FFMQ in fibromyalgia patients. The observe facet, however, should be used with caution given its deviant relationship with theoretically related constructs

Aqueous alteration processes in Jezero crater, Mars—implications for organic geochemistry

The Perseverance rover landed in Jezero crater, Mars, in February 2021. We used the Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument to perform deep-ultraviolet Raman and fluorescence spectroscopy of three rocks within the crater. We identify evidence for two distinct ancient aqueous environments at different times. Reactions with liquid water formed carbonates in an olivine-rich igneous rock. A sulfate-perchlorate mixture is present in the rocks, which probably formed by later modifications of the rocks by brine. Fluorescence signatures consistent with aromatic organic compounds occur throughout these rocks and are preserved in minerals related to both aqueous environments