This dissertation offers some theoretical and practical contributions to the relatively sparse
literature on the bootstrap in state space models. In the first chapter, we present the rationale …

Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and
state-space models have been widely adopted to model and forecast economic data. While …

Bootstrapping is now a well established procedure for assessing the bias and standard error
of parameters in statistical models (Davison and Hinckley, 1997). Given a fitted model and …

The bootstrap is a method of computational inference that simulates the creation of new data
by resampling from a single data set. Although developed first for cross-sectional data, in …

This paper investigates the potentials of the bootstrap as a tool for inference on the
parameters of macroeconometric models which admit a state space representation. We …

Bootstrap methods have become one of the most widely used statistical techniques due to
their simplicity and good properties. In this thesis, we introduce a novel bootstrap method …

In this paper, we study the performance of the most popular bootstrap schemes for multilevel
data. Also, we propose a modified version of the wild bootstrap procedure for hierarchical …

In this paper, we propose a model‐free bootstrap method for the empirical process under
absolute regularity. More precisely, consistency of an adapted version of the so‐called …

In the forthcoming chapters we will develop statistical methods to infer knowledge about the
model distribution and encounter several sample statistics to do this. In the previous chapter …

We note, however, that even as the number of bootstrap replications tends to infinity, the
estimate of the population density function that is used to generate the bootstrap samples is …