Bootstrapping the O(N ) vector models

Bootstrapping Distributional Feature Vector Quality

Bootstrapping for multilevel models

A single bootstrap sample is obtained by sampling randomly (i.e. according to the assumed mechanism which generated the observations), with replacement, n observations from the original sample. Denote this by X x xn * * * { ,...., } = 1 . Then we can obtain B of these bootstrap samples, X X X B *1 *2 * , ,...., .For each of these we calculate our estimate, say of the mean, and each of these is ...

Bootstrapping INAR Models

Integer-valued autoregressive (INAR) time series form a very useful class of processes suitable to model time series of counts. In the common formulation of Du and Li (1991, JTSA), INAR models of order p share the autocorrelation structure with classical autoregressive time series. This fact allows to estimate the INAR coefficients, e.g., by Yule-Walker estimators. However, contrary to the AR c...

Bootstrapping Macroeconometric Models

This paper outlines a complete bootstrapping approach to the estimation and analysis of macroeconometric models. It combines the bootstrapping literature initiated by Efron (1979) and the stochastic simulation literature initiated by Adelman and Adelman (1959).

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 typ...