Suchergebnisse

The first chapter of this thesis deals with households' consumption-savings tradeoff in monetary policy models. The model's fit with the data can be markedly improved first by the use of households specific interest rates, second by modelling households as inattentive agents. The second chapter proves analytically that the Taylor principle is valid in a model with both staggered prices and wages. According to the Taylor principle, when the central banker overreacts to inflationary shocks, he rules out sun spot equilibria and thus reduces economic fluctuations. This property is well known in models with staggered prices only, it had to be established analytically when wages are staggered. The third chapter quantifies domestic production (house-chores, cooking, handy work.) taking place out of the market. Valuing these activities induces an upward revision of economic activity but a downward revision of its growth. Methodological questions linked to the valuation of domestic work are discussed in details. The fourth chapter deals with the evaluation of structural reforms in institutional models (IMF, European Commission, ECB). The analysis of the core mechanisms of these models and their sensitivity to the calibration bring about two recommendations, first a systematic use of sensitivity analysis and more fundamentally a rethink of their long term mechanisms (in particular for labour supply). ; Le premier chapitre de cette thèse traite de l'arbitrage consommation-épargne des ménages dans les modèles de politique monétaire. L'adéquation entre le modèle et les données peut être notablement améliorée d'une part en prenant en compte les taux d'intérêts auxquels les ménages sont soumis et d'autre part en modélisant ces derniers comme des agents inattentifs. Le second chapitre démontre analytiquement la validité du principe de Taylor dans un modèle à salaires et prix rigides. D'après le principe de Taylor, lorsque le banquier central surréagit aux chocs inflationnistes, ce dernier élimine les équilibres à tâches solaires, ...

The first chapter of this thesis deals with households' consumption-savings tradeoff in monetary policy models. The model's fit with the data can be markedly improved first by the use of households specific interest rates, second by modelling households as inattentive agents. The second chapter proves analytically that the Taylor principle is valid in a model with both staggered prices and wages. According to the Taylor principle, when the central banker overreacts to inflationary shocks, he rules out sun spot equilibria and thus reduces economic fluctuations. This property is well known in models with staggered prices only, it had to be established analytically when wages are staggered. The third chapter quantifies domestic production (house-chores, cooking, handy work.) taking place out of the market. Valuing these activities induces an upward revision of economic activity but a downward revision of its growth. Methodological questions linked to the valuation of domestic work are discussed in details. The fourth chapter deals with the evaluation of structural reforms in institutional models (IMF, European Commission, ECB). The analysis of the core mechanisms of these models and their sensitivity to the calibration bring about two recommendations, first a systematic use of sensitivity analysis and more fundamentally a rethink of their long term mechanisms (in particular for labour supply). ; Le premier chapitre de cette thèse traite de l'arbitrage consommation-épargne des ménages dans les modèles de politique monétaire. L'adéquation entre le modèle et les données peut être notablement améliorée d'une part en prenant en compte les taux d'intérêts auxquels les ménages sont soumis et d'autre part en modélisant ces derniers comme des agents inattentifs. Le second chapitre démontre analytiquement la validité du principe de Taylor dans un modèle à salaires et prix rigides. D'après le principe de Taylor, lorsque le banquier central surréagit aux chocs inflationnistes, ce dernier élimine les équilibres à tâches solaires, ...

This comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA). SSA is a well-known methodology for the analysis and forecasting of time series. Since quite recently, SSA is also being used to analyze digital images and other objects that are not necessarily of planar or rectangular form and may contain gaps. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas, most notably those associated with time series and digital images. An effective, comfortable and accessible implementation of SSA is provided by the R-package Rssa, which is available from CRAN and reviewed in this book. Written by prominent statisticians who have extensive experience with SSA, the book (a) presents the up-to-date SSA methodology, including multidimensional extensions, in language accessible to a large circle of users, (b) combines different versions of SSA into a single tool, (c) shows the diverse tasks that SSA can be used for, (d) formally describes the main SSA methods and algorithms, and (e) provides tutorials on the Rssa package and the use of SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The book is written on a level accessible to a broad audience and includes a wealth of examples; hence it can also be used as a textbook for undergraduate and postgraduate courses on time series analysis and signal processing.

Intro -- Series Editors' Preface for David Torres Irribarra A Pragmatic Perspective of Measurement -- Mathematical Thinking, Social Practices, and the Locus of Science in Psychology -- References -- Preface -- Contents -- 1 Introduction -- 1.1 What Is Pragmatism? -- 1.2 A Brief Overview of Some Pragmatist Ideas -- 1.2.1 The Primacy of Practice -- 1.2.2 Fallibilism and Anti-skepticism -- 1.2.3 Historical and Social Nature of Knowledge -- 1.2.4 Abandoning Representationalism -- 1.2.5 Theories as Tools -- 1.2.5.1 Pragmatism Recapped -- 2 What Is Measurement? -- 2.1 Classical Theory of Measurement -- 2.2 Operationalism -- 2.3 The Representational Theory of Measurement -- 2.3.1 Campbell: Between the Classical and Representational Theories -- 2.3.2 Stevens: Between the Representational and Operationalist Theories -- 2.3.3 Axiomatic Measurement Theory -- 2.4 Latent Variable Modeling -- 2.5 Metrology -- 2.6 Defining Measurement? -- 3 Measurement: Prototypes and Resemblances -- 3.1 The Many Faces of Measurement -- 3.2 Protypes and Resemblances -- 3.3 Prototypical Measurement Practices -- 4 A Pragmatic Perspective of Measurement -- 4.1 Why an Activity? Why Three of Them? -- 4.2 Why According to a Model? -- 4.3 Why a ``relevant'' Attribute? -- 4.4 Why in Service of a Larger Goal? -- 5 Contrasting and Comparing -- 5.1 The Goals of Measurement -- 5.2 The Activities of Measurement -- 5.3 The Attributes We Measure -- 5.4 The Model of the Attribute -- 5.5 Summary -- References -- Index.

1. Introduction; Ryan M. Nefdt, Carita Klippi and Bart Karstens -- Part I. Linguistics and the Formal Sciences -- 2. The Complexities of Syntax; Jaroslav Peregrin -- 3. Negation in Dedekind Orderings and the Logic of Reciprocity; Terence D. Langendoen -- 4. Variations on Abstract Semantic Spaces; Katrin Erk -- 5. Mathematical Transfers in Linguistics: A Dynamic Between Ethos and Formalization as a Process of Scientific Legitimization; Adrien Mathy -- Part II. Linguistics and the Natural Sciences -- 6. Scientific Realism and Linguistics: Two Stories of Scientific Progress; Johannes Woschitz -- 7. Linguistic Change and Biological Evolution; Unni Leino, Kaj Syrjänen, Outi Vesakoski -- 8 Three Models for Linguistics: Newtonian Mechanics, Darwinism, Axiomatics; Esa Itkonen -- Part III. Linguistics and the Cognitive Sciences -- 9. The Role of Language in the Cognitive Sciences; Ryan M. Nefdt -- 10. Linguistics and Brain Science: (Dis-) connections in Nineteenth Century Aphasiology; Els Effers -- 11. Epistemic Transfer Between Linguistics and Neuroscience: Problems and Prospects; Giosuè Baggio -- Part IV. Linguistics and the Humanities -- 12. Linguistics Meets Hermeneutics: Reading Early Greek Epistemological Texts; Anna Novokhatko -- 13. On the History of Models in American Linguistics; Jacqueline Leon -- 14. Poetics and the life of language: Disciplinary Transfer in the Guise of Metaphor; Carita Klippi -- 15. Linguistics and Philosophy: Break up Song; Kate Stanton.

This volume provides in-depth coverage of such topics as multi-reservoir system operation theory and practice, management of aquifer systems connected to streams using semi-analytical models, one-dimensional model of water quality and aquatic ecosystem-ecotoxicology in river systems, environmental and health impacts of hydraulic fracturing and shale gas, bioaugmentation for water resources protection, wastewater renovation by flotation for water pollution control, determination of receiving water's reaeration coefficient in the presence of salinity for water quality management, sensitivity analysis for stream water quality management, river ice process, and computer-aided mathematical modeling of water properties. This critical volume will serve as a valuable reference work for advanced undergraduate and graduate students, designers of water resources systems, and scientists and researchers. The goals of the Handbook of Environmental Engineering series are: (1) to cover entire environmental fields, including air and noise pollution control, solid waste processing and resource recovery, physicochemical treatment processes, biological treatment processes, biotechnology, biosolids management, flotation technology, membrane technology, desalination technology, water resources, natural control processes, radioactive waste disposal, hazardous waste management, and thermal pollution control; and (2) to employ a multimedia approach to environmental conservation and protection since air, water, soil and energy are all interrelated

"Financial Markets and Trading Strategies covers three main parts: Market organization and microstructure theory, which will contain an overview of modern financial markets for equities, FX, and fixed income. There will be a description on various market types and market price formation with different types of traders and orders. Major theoretical microstructure models will be presented, as also concepts of the agent-based modeling of financial markets and important empirical properties of equity and FX markets. Common trading strategies and back-testing will summarize the concepts used in technical analysis and arbitrage trading (such as pairs trading and mean-reversion strategies). There will be a description of performance criteria and back-testing of trading strategies with re-sampling techniques and an outline of other ideas used in optimal order execution, such as optimal order slicing and maker-versus-taker strategies. The appendix will include Probability distributions and time series analysis. For self-contained presentation, there will be a description of the mathematical methods used in formulating trading strategies and their back-testing. There will be a focus on the linear regression, autoregressive and moving average models, trends, co-integration, and conditional heteroskedasticity. There will also be an introduction to resampling techniques, such as bootstrap and MCMC"--

In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one.Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software.Highlights of the Second Edition include: Review of latent change (growth) analysis models at an introductory level Coverage of the popular Mplus program Updated examples of LISREL and EQS A CD that contains all of the text's LISREL, EQS, and Mplus examples.A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

Abstract There is growing evidence of systematic attempts to influence democratic elections by controlled and digitally organized dissemination of fake news. This raises the question of the intrinsic robustness of democratic electoral processes against external influences. Particularly interesting is to identify the social characteristics of a voter population that renders it more resilient against opinion manipulation. Equally important is to determine which of the existing democratic electoral systems is more robust to external influences. Here we construct a mathematical electoral model to address these two questions. We find that, not unexpectedly, biased electorates with clear-cut elections are overall quite resilient against opinion manipulations, because inverting the election outcome requires to change the opinion of many voters. More interesting are unbiased or weakly biased electorates with close elections. We find that such populations are more resilient against opinion manipulations (i) if they are less polarized and (ii) when voters interact more with each other, regardless of their opinion differences, and that (iii) electoral systems based on proportional representation are generally the most robust. Our model qualitatively captures the volatility of the US House of Representatives elections. We take this as a solid validation of our approach.

Abstract
Genotypic and phenotypic adaptation is the consequence of ongoing natural selection in populations and is key to predicting and preventing drug resistance. Whereas classic antibiotic persistence is all-or-nothing, here we demonstrate that an antibiotic resistance gene displays linear dose-responsive selection for increased expression in proportion to rising antibiotic concentration in growing Escherichia coli populations. Furthermore, we report the potentially wide-spread nature of this form of emergent gene expression (EGE) by instantaneous phenotypic selection process under bactericidal and bacteriostatic antibiotic treatment, as well as an amino acid synthesis pathway enzyme under a range of auxotrophic conditions. We propose an analogy to Ohm's law in electricity (V = IR), where selection pressure acts similarly to voltage (V), gene expression to current (I), and resistance (R) to cellular machinery constraints and costs. Lastly, mathematical modeling using agent-based models of stochastic gene expression in growing populations and Bayesian model selection reveal that the EGE mechanism requires variability in gene expression within an isogenic population, and a cellular "memory" from positive feedbacks between growth and expression of any fitness-conferring gene. Finally, we discuss the connection of the observed phenomenon to a previously described general fluctuation–response relationship in biology.

Digital transformation is an interdisciplinary work that has the objective to analyze data in real-time for better decision making, where it involves the modeling and the simulation of physical-mathematical and social processes and the monitoring of machinery that using information and communication technologies. Nowadays the fourth industrial revolution technologies are the driver for this transformation and soon will be quantum computing and other physics and mathematics advances. Therefore, in this academic-scientific work, a curricular framework is proposed for a master's program in digital transformation for science and engineering; the framework follows the literature review and analysis to formulate the denomination and justification for the master's degree. The results are (i) a general three-level model as a roadmap for the specific curricular design of a master's in digital transformation for science and engineering; (ii) the recommendation that a master's program has a research focus in the two categories established by the Colombian standard (research and practitioner); (ii) finally, a political, economic, social, technological, environmental, and legal analysis justifies a master's in digital transformation for science and engineering. The research model details the phases that were carried out to characterize and identify the curriculum framework that will be the basis for formalizing the master's degree document.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARSCoV- 2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-tomiddle-income countries. This will provide important information for planning exit strategies that balance socioeconomic benefits with public health.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.