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This book examines unemployment insurance policy through a survey, taking stock of the theoretical work in the field of labor economics. It closely follows and assesses developments in the modelling of optimal unemployment insurance (UI) policies, beginning with the initial analytical findings produced in the second half of the 1970s. A main part of the survey is devoted to the two basic strands of analysis about, respectively, the optimal level of UI benefits and the optimal time profile of UI policy. The book has two different objectives. The first is to provide an essential summary of the individual models, with the intention of underscoring how a number of specific messages for the policy-maker can be derived from analytical constructions. It further emphasizes and comments on what the models deliver to UI policy-makers. The second objective is to stress the importance and extension of open questions in the field of the theoretical approach to the unemployment insurance issue. The survey discusses the multiplicity of heterogeneities of the labor world in particular as relevant for UI issues on the one side, and on the other hand, the independence of the two basic choices of UI policy, its meaning and its limits, and the possible forms of complementarity between these choices. The book is a must-read for researchers, students, and policy-makers interested in a better understanding of the field of labor economics in general, as well as unemployment insurance policies in particular.
Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.