international econometric journal
in Russian language
Anatolyev, Stanislav. The basics of bootstrapping
This essay is an
introduction to principles and methodology of the bootstrap. The basics of
bootstrap inference, resampling and asymptotic refinement are given. The
narration is accompanied with clarifying examples. There is also a brief
description of other methodological essays of the current issue of Quantile and references to non-included
material.
Davidson, Russell. Bootstrapping econometric models
The
bootstrap is a statistical technique used more and more widely in econometrics.
While it is capable of yielding very reliable inference, some precautions
should be taken in order to ensure this. Two “Golden Rules” are formulated
that, if observed, help to obtain the best the bootstrap can offer.
Bootstrapping always involves setting up a bootstrap data-generating process
(DGP). The main types of bootstrap DGP in current use are discussed, with
examples of their use in econometrics. The ways in which the bootstrap can be
used to construct confidence sets differ somewhat from methods of hypothesis
testing. The relation between the two sorts of problem is discussed.
Bühlmann, Peter. Bootstrap schemes for time series
We
review and compare block, sieve and local bootstraps for time series and
thereby illuminate theoretical aspects of the procedures as well as their performance
on finite-sample data. Our view is selective with the intention of providing a
new and fair picture of some particular aspects of bootstrapping time series.
The generality of the block bootstrap is contrasted with the sieve bootstrap.
We discuss implementational advantages and disadvantages, and argue that the
sieve often outperforms the block method. Local bootstraps, designed for
nonparametric smoothing problems, are easy to use and implement but exhibit in
some cases low performance.
Corradi, Valentina. Bootstrap refinements for GMM based tests
This
essay provides a brief review about bootstrap higher order refinements for
tests based on generalized method of moments estimators. First, we briefly
describe the asymptotic behavior of two-step GMM estimators. Second, we give a
heuristic argument for why inference based on bootstrap critical values is more
accurate than that based on asymptotic normality. Third, we briefly summarize nonparametric
resampling methods. Fourth, we outline how critical values based on the block
bootstrap reduce the error in the rejection probability for t-tests based on
GMM estimators. Finally, we give a overview of some alternative bootstrap
procedures which provide improvements over the block bootstrap refinements.
Tsyplakov, Alexander. A mini-dictionary of English econometric
terminology I
This
dictionary comments on some English econometric terms: dummy, sample,
population, score, inference. Emphasis is placed on accurate definitions of
their meaning to avoid possible confusion and incorrect interpretation.
Anatolyev, Stanislav. Review of English textbooks in econometrics
This
is a survey of some popular econometric texts written in English. The essay
reflects the author's opinion, as well as opinions of notable econometricians
expressed in published book re
Danilov, Dmitry; Magnus, Jan R. Some equivalences in linear estimation
Under
normality, the Bayesian estimation problem, the best linear unbiased estimation
problem, and the restricted least-squares problem are all equivalent. As a
result we need not compute pseudo-inverses and other complicated functions,
which will be impossible for large sparse systems. Instead, by reorganizing the
inputs, we can rewrite the system as a new but equivalent system which can be
solved by ordinary least-squares methods.
Mukhamediyev, Bulat. Monetary policy rules of the National Bank of
Kazakhstan
The
issue of monetary policy rules in