Search results for "Jeffrey Craven"
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Introductory Econometrics
Introductory Econometrics: Intuition, Proof, and Practice attempts to distill econometrics into a form that preserves its essence, but that is acceptable—and even appealing—to the student's intellectual palate. This book insists on rigor when it is essential, but it emphasizes intuition and seizes upon entertainment wherever possible. Introductory Econometrics is motivated by three beliefs. First, students are, perhaps despite themselves, interested in questions that only econometrics can answer. Second, through these answers, they can come to understand, appreciate, and even enjoy the enterprise of econometrics. Third, this text, which presents select innovations in presentation and practice, can provoke readers' interest and encourage the responsible and insightful application of econometric techniques.In particular, author Jeffrey S. Zax gives readers many opportunities to practice proofs—which are challenging, but which he has found to improve student comprehension. Learning from proofs gives readers an organic understanding of the message behind the numbers, a message that will benefit them as they come across statistics in their daily lives. An ideal core text for foundational econometrics courses, this book is appropriate for any student with a solid understanding of basic algebra—and a willingness to use that tool to investigate complicated issues.
R 3,674.00
Nonparametric Econometrics
Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data--nominal and ordinal--in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists.Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types--continuous, nominal, and ordinal--within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
R 4,899.87
Reproducible Econometrics Using R
Across the social sciences there has been increasing focus on reproducibility, i.e., the ability to examine a study's data and methods to ensure accuracy by reproducing the study. Reproducible Econometrics Using R combines an overview of key issues and methods with an introduction to how to use them using open source software (R) and recently developed tools (R Markdown and bookdown) that allow the reader to engage in reproducible econometric research. Jeffrey S. Racine provides a step-by-step approach, and covers five sets of topics, i) linear time series models, ii) robust inference, iii) robust estimation, iv) model uncertainty, and v) advanced topics. The time series material highlights the difference between time-series analysis, which focuses on forecasting, versus cross-sectional analysis, where the focus is typically on model parameters that have economic interpretations. For the time series material, the reader begins with adiscussion of random walks, white noise, and non-stationarity. The reader is next exposed to the pitfalls of using standard inferential procedures that are popular in cross sectional settings when modelling time series data, and is introduced to alternative procedures that form the basis for linear timeseries analysis. For the robust inference material, the reader is introduced to the potential advantages of bootstrapping and the Jackknifing versus the use of asymptotic theory, and a range of numerical approaches are presented. For the robust estimation material, the reader is presented with a discussion of issues surrounding outliers in data and methods for addressing their presence. Finally, the model uncertainly material outlines two dominant approaches for dealing with model uncertainty,namely model selection and model averaging.Throughout the book there is an emphasis on the benefits of using R and other open source tools for ensuring reproducibility. The advanced material covers machine learning methods (support vector machines that are useful for classification) and nonparametric kernel regression which provides the reader with more advanced methods for confronting model uncertainty. The book is well suited for advanced undergraduate and graduate students alike. Assignments, exams, slides, and a solution manual areavailable for instructors.
R 2,707.74
