(Wiley Series in Probability and Statistics) Volume 2 Edition
by Marilena Furno (Author), Domenico Vistocco (Author)
Contains an overview of several technical topics of Quantile Regression
Volume two of Quantile Regression
offers an important guide for applied researchers that draws on the
same example-based approach adopted for the first volume. The text
explores topics including robustness, expectiles, m-quantile,
decomposition, time series, elemental sets and linear programming.
Graphical representations are widely used to visually introduce several
issues, and to illustrate each method. All the topics are treated
theoretically and using real data examples. Designed as a practical
resource, the book is thorough without getting too technical about the
statistical background.
The authors cover a wide range of QR
models useful in several fields. The software commands in R and Stata
are available in the appendixes and featured on the accompanying
website. The text:
- Provides an overview of several
technical topics such as robustness of quantile regressions, bootstrap
and elemental sets, treatment effect estimators
- Compares quantile regression with alternative estimators like expectiles, M-estimators and M-quantiles
- Offers
a general introduction to linear programming focusing on the simplex
method as solving method for the quantile regression problem
- Considers
time-series issues like non-stationarity, spurious regressions,
cointegration, conditional heteroskedasticity via quantile regression
- Offers an analysis that is both theoretically and practical
- Presents real data examples and graphical representations to explain the technical issues
Written
for researchers and students in the fields of statistics, economics,
econometrics, social and environmental science, this text offers guide
to the theory and application of quantile regression models.
