This paper discusses ways to reduce the bias of consistent estimators that are biased in finite samples. It is … necessary that the bias function, which relates parameter values to bias, should be estimable by computer simulation or by some … other method. If so, bias can be reduced or, in some cases that may not be unrealistic, even eliminated. In general, several …

new tests are bootstrap implementations of score-based tests for the order of integration of a fractionally integrated … using a standard i.i.d. bootstrap admit pivotal asymptotic null distributions in the presence of heteroskedasticity, but … that the corresponding tests based on the wild bootstrap principle do. A Monte Carlo simulation study demonstrates that …

the others are based on inverting t statistics or the bootstrap P values associated with them. We propose a new method for … constructing bootstrap confidence sets based on t statistics. In large samples, the procedures that generally work best are CLR … confidence sets using asymptotic critical values and bootstrap confidence sets based on LIML estimates. …

There are many bootstrap methods that can be used for econometric analysis. In certain circumstances, such as … regression models with independent and identically distributed error terms, appropriately chosen bootstrap methods generally work … very well. However, there are many other cases, such as regression models with dependent errors, in which bootstrap methods …

In this paper we analyze the influence of observed and unobserved initial values on the bias of the conditional maximum … second-order bias of d as a function of the initial values, X_t, t=-N_0+1,…,N, and we investigate the effect on the bias of … parameter μ picks up the effect of the initial values even for the choice N=0. If N_0=0, we show that the second-order bias can …

for measuring finite sample performance are bias and root mean squared error. Most importantly, the simulations reveal … estimators are fairly robust to conditionally heteroscedastic errors, 3) the local polynomial Whittle and bias reduced log …