|
Showing 1 - 2 of
2 matches in All Departments
Interest in nonparametric methodology has grown considerably over
the past few decades, stemming in part from vast improvements in
computer hardware and the availability of new software that allows
practitioners to take full advantage of these numerically intensive
methods. This book is written for advanced undergraduate students,
intermediate graduate students, and faculty, and provides a
complete teaching and learning course at a more accessible level of
theoretical rigor than Racine's earlier book co-authored with Qi
Li, Nonparametric Econometrics: Theory and Practice (2007). The
open source R platform for statistical computing and graphics is
used throughout in conjunction with the R package np. Recent
developments in reproducible research is emphasized throughout with
appendices devoted to helping the reader get up to speed with R, R
Markdown, TeX and Git.
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 a discussion 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 time series 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 are available for instructors.
|
You may like...
Back Together
Michael Ball & Alfie Boe
CD
(1)
R48
Discovery Miles 480
|