In this paper, we develop a general method of testing for independence when unobservable generalized errors are involved. Our method can be applied to testing for serial independence of generalized errors, and testing for independence between the generalized errors and observable covariates. The...

Increasingly, people use the internet to connect with one another, access information, and purchase products and services. Along with the growth in the online marketplace have come concerns, as well, particularly regarding both the privacy of personal information as well as competition issues...

The concept of autoregressive sieve bootstrap is investigated for the case of vector autoregressive (VAR) time series. This procedure fits a finite‐order VAR model to the given data and generates residual‐based bootstrap replicates of the time series. The paper explores the range of validity...

We develop some asymptotic theory for applications of block bootstrap resampling schemes to multivariate integrated and cointegrated time series. It is proved that a multivariate, continuous‐path block bootstrap scheme applied to a full rank integrated process succeeds in estimating...

Leshno and Levy (2002) introduce the concept of the first and second order of almost stochastic dominance (ASD) for most decision makers. There are many studies investigating the properties of this concept. Many empirical applications are also conducted based on it. However, there is no formal...

Microeconomic data often have within-cluster dependence. This dependence affects standard error estimation and inference in regression models, including the instrumental variables model. Standard corrections assume that the number of clusters is large, but when this is not the case, Wald and...

Contextual factors usually assume an important role in determining firms' productive efficiencies. Nevertheless, identifying them in a regression framework might be complicated. The problem arises from the efficiencies being correlated with each other when estimated by Data Envelopment Analysis...

We study the validity of the pairs bootstrap for Lasso estimators in linear regression models with random covariates and heteroscedastic error terms. We show that the naive pairs bootstrap may have some issues in approximating the sampling distribution of the Lasso estimator. In particular, we...