international econometric journal
in Russian language
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.
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.
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.
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.
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.
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
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.
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