In the paper row-wise periodically correlated triangular arrays are considered. The period length is assumed to grow in time. The Fourier decomposition of the mean and autocovariance functions for each row of the matrix is presented. To construct bootstrap estimators of the Fourier coefficients two block bootstrap techniques are used. These are the circular version of the Generalized Seasonal B...

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...

This paper focuses on different methods of estimation and forecasting in first-order integer-valued autoregressive processes with Poisson-Lindley (PLINAR(1)) marginal distribution. For this purpose, the parameters of the model are estimated using Whittle, maximum empirical likelihood and sieve bootstrap methods. Moreover, Bayesian and sieve bootstrap forecasting methods are proposed and predict...

The single bootstrap already is popular in economics, though the double bootstrap has better convergence properties. We discuss the theory and implementation of the double bootstrap, both with and without the pivotal transformation, and give detailed examples of each. One example is a nonlinear double bootstrap of a Cobb-Douglas production function, and explains the use of Gauss-Newton Regressi...

Nonlinear system identi cation involves selecting the order of the given model based on the input-output data. A bootstrap model selection procedure which selects the model by minimising bootstrap estimates of the prediction error is developed. Bootstrap based model selection procedures are attractive because the bootstrap observations generated for the model selection can also be used in subse...

In practice, bootstrap tests must use a finite number of bootstrap samples. This means that the outcome of the test will depend on the sequence of random numbers used to generate the bootstrap samples, and it necessarily results in some loss of power. We examine the extent of this power loss and propose a simple pretest procedure for choosing the number of bootstrap samples so as to minimize ex...

The standard error and sampling distribution of robust estimates can, in principle, be estimated using the bootstrap. However, two problems arise when we want to use bootstrap with robust estimates on moderately large data sets: the bootstrap estimates may be unrealiable because the proportion of outliers in many bootstrap samples could be higher than that in the original data set, and the high...

The robust lasso-type regularized regression is a useful tool for simultaneous estimation and variable selection even in the presence of outliers. Crucial issues in the robust modeling procedure include the selection of regularization parameters and also a tuning constant in outlier detection. Although the performance of the robust sparse regression strongly depends on the proper choice of thes...

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets—which are increasingly prevalent— the computation of bootstrap-based quantities can be prohibitively demanding computationally. While variants such as subsampling and the m out of n bootstrap can be used in principle to reduce the cost of bootstrap computation...