233,654 research outputs found

    The Relevance of Supply Shocks for Inflation: The Spanish Case

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    This paper analyses the effects of supply shocks on the Spanish inflation rate. The methodology applied is based on Ball and Mankiw (1995). These authors assume that a good proxy for supply shocks is the third moment of the distribution of price changes, and show that nominal rigidities imply a positive relation between inflation and skewness, that is magnified by the variance of the distribution. The main data used are the monthly consumer price indexes of each region, disaggregated in 57 categories, for the 1993-2005 period. We estimate the relation between mean inflation and the higher moments of the distribution, including several control variables. The analysis has been carried out in two ways: firstly, each region is analysed separately and, secondly, we have used panel data techniques in order to test the homogeneity across regions. Our results point out that Spanish regions show a common pattern with regard to the nominal rigidities detected, and that the Spanish economy is vulnerable to supply shocks.Inflation, nominal rigidities, skewness, supply shocks, Spanish regions

    Evidence-based instruction: a classroom experiment comparing nominal and brainstorming groups

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    Interactive brainstorming groups consistently produce fewer ideas, and fewer high quality ideas, than nominal groups, whose members work alone before pooling their ideas. Yet, brainstorming continues to be regarded as an effective method for enhancing creativity. This paper describes an engaging classroom ‘‘experiment’’ that reliably demonstrates the superiority of nominal over brainstorming groups for generating more ideas. Analyses of data from 105 student groups, collected from 12 classes, show that typical differences between the two group methods are sizable. Beyond lessons about group techniques, this exercise shows students the limits of intuition and the value of evidence-based management practices

    Propogation of Shocks in a High-Inflation Economy: Israel, 1980-85

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    The purpose of this paper is to provide empirical answers to questions related to the propagation of shocks in a high-inflation economy. Do one-time inflationary shocks give rise to long-term persistence, or inertia? Do balance of payments' shocks trigger a process that, through indexation and monetary accommodation, results in long-term changes in inflation? Within the context of a specific hypothesis, influential both in policy discussions and in economic analyses, the paper addresses these issues using Israeli data and vector-autoregression techniques. The evidence does not support the hypothesis that one-time nominal shocks have a persistent effect on the inflation rate, or the hypothesis that long-term changes in inflation are triggered by autonomous fluctuations in the trade balance.

    THE CHARACTERISTICS DIDACTIC GESTURES OF A STUDY AND RESEARCH PATH INVOLVING MATHEMATICS AND PHYSICS AT SECONDARY SCHOOL

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    This paper analyses the development of a Study and Research Path (SRP) involving mathematics and physics by means of the theoretical construct proposed by the Anthropological Theory of Didactics (ATD) called dialectics. The SRP was implemented in five secondary school math courses and 116 students participated in total. The data analysis was performed using qualitative techniques and constructing nominal variables and their modalities, which also allows the use of descriptive statistical techniques. In this work, absolute frequencies are analysed and some considerations are made - in the light of the theory - to interpret these results.  Article visualizations

    Modeling and Simulation of the Second-Generation Orion Crew Module Air Bag Landing System

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    Air bags were evaluated as the landing attenuation system for earth landing of the Orion Crew Module (CM). Analysis conducted to date shows that airbags are capable of providing a graceful landing of the CM in nominal and off-nominal conditions such as parachute failure, high horizontal winds, and unfavorable vehicle/ground angle combinations, while meeting crew and vehicle safety requirements. The analyses and associated testing presented here surround a second generation of the airbag design developed by ILC Dover, building off of relevant first-generation design, analysis, and testing efforts. In order to fully evaluate the second generation air bag design and correlate the dynamic simulations, a series of drop tests were carried out at NASA Langley s Landing and Impact Research (LandIR) facility in Hampton, Virginia. The tests consisted of a full-scale set of air bags attached to a full-scale test article representing the Orion Crew Module. The techniques used to collect experimental data, develop the simulations, and make comparisons to experimental data are discussed

    Trajectory Clustering for Air Traffic Categorisation

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    Availability of different types of data and advances in data-driven techniques open the path to more detailed analyses of various phenomena. Here, we examine the insights that can be gained through the analysis of historical flight trajectories, using data mining techniques. The goal is to learn about usual (or nominal) choices airlines make in terms of routing, and their relation with aircraft types and operational flight costs. The clustering is applied to intra-European trajectories during one entire summer season, and a statistical test of independence is used to evaluate the relations between the variables of interest. Even though about half of all flights are less than 1000 km long, and mostly operated by one airline, along one trajectory, the analysis shows that, for longer flights, there exists a clear relation between the trajectory clusters and the operating airlines (in about 49% of city pairs) and/or the aircraft types (30%), and/or the flight costs (45%)

    Data-Driven Morphological Analysis for Uralic Languages

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    This paper describes an initial set of experiments in data-driven morpholog-ical analysis of Uralic languages. The paper differs from previous work in thatour work covers both lemmatization and generating ambiguous analyses. Whilehand-crafted finite-state transducers represent the state of the art in morpholog-ical analysis for most Uralic languages, we believe that there is a place for data-driven approaches, especially with respect to making up for lack of completenessin the шlexicon. We present results for nine Uralic languages that show that, atleast for basic nominal morphology for six out of the nine languages, data-drivenmethods can achieve an F-score of over 90%, providing results that approach thoseof finite-state techniques. We also compare our system to an earlier approach toFinnish data-driven morphological analysis (Silfverberg and Hulden,2018) andshow that our system outperforms this baseline.Peer reviewe

    The Application of Data Mining Techniques to Learning Analytics and Its Implications for Interventions with Small Class Sizes

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    There has been significant progress in the development of techniques to deliver effective technology enhanced learning systems in education, with substantial progress in the field of learning analytics. These analyses are able to support academics in the identification of students at risk of failure or withdrawal. The early identification of students at risk is critical to giving academic staff and institutions the opportunity to make timely interventions. This thesis considers established machine learning techniques, as well as a novel method, for the prediction of student outcomes and the support of interventions, including the presentation of a variety of predictive analyses and of a live experiment. It reviews the status of technology enhanced learning systems and the associated institutional obstacles to their implementation and deployment. Many courses are comprised of relatively small student cohorts, with institutional privacy protocols limiting the data readily available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. I present an experiment conducted on a final year university module, with a student cohort of 23, where the data available for prediction is limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. I apply and compare a variety of machine learning analyses to assess and predict student performance, applied at appropriate points during module delivery. Despite some mixed results, I found potential for predicting student performance in small student cohorts with very limited student attributes, with accuracies comparing favourably with published results using large cohorts and significantly more attributes. I propose that the analyses will be useful to support module leaders in identifying opportunities to make timely academic interventions. Student data may include a combination of nominal and numeric data. A large variety of techniques are available to analyse numeric data, however there are fewer techniques applicable to nominal data. I summarise the results of what I believe to be a novel technique to analyse nominal data by making a systematic comparison of data pairs. In this thesis I have surveyed existing intelligent learning/training systems and explored the contemporary AI techniques which appear to offer the most promising contributions to the prediction of student attainment. I have researched and catalogued the organisational and non-technological challenges to be addressed for successful system development and implementation and proposed a set of critical success criteria to apply. This dissertation is supported by published work

    Methods of small group research

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