In this paper, we develop an exact method for computing the minimum coverage probability of Wald interval for estimation of binomial parameters. Similar approach can be used for other type of confidence intervals. 1 Wald Interval Let X be a Bernoulli random variable with distribution Pr{X = 1} = 1−Pr{X = 0} = p ∈ (0, 1). Let X1, · · · ,Xn be i.i.d. random samples of X. Let K = ∑n i=1 Xi. The wi...

In this paper, we focus on the design of adaptive receivers for nonhomogeneous scenarios. More precisely, at the design stage we assume a mismatch between the covariance matrix of the noise in the cell under test and that of secondary data. Under the above assumption, we show that the Wald test is the adaptive matched filter, while the Rao test coincides with the receiver obtained by using the ...

This paper presents an overview of some new results regarding an easily implementable Wald test-statistic (EFDF test) of the null hypotheses that a time-series process is I(1) or I(0) against fractional I(d) alternatives, with d ∈ (0, 1), allowing for unknown deterministic components and serial correlation in the error term. Specifically, we argue that the EFDF test has better power properties ...

Most confidence intervals, whether based on asymptotic theory or the bootstrap, are implicitly based on inverting a Wald test. Since Wald test statistics are not invariant under nonlinear reparametrizations of the restrictions they test, confidence intervals based on them are not invariant either. This fact explains the well-known non invariance of bootstrap confidence intervals obtained by Hal...

Many economic models imply that ratios, simple differences, or "spreads" of variables are I(O). In these models, cointegrating vectors are composed of l's, O's, and l's and contain no unknown parameters. In this paper, we develop tests for cointegration that can be applied when some of the cointegrating vectors are prespecified under the null or under the alternative hypotheses. These tests are...

Previously, we have employed a program (Jtest) to test an hypothesis involving many parameters (multiple constraints). This test, which involves comparing parameter estimates with hypothesized values, is known as a Wald test. We also used the Jtest program to compare two di¤erent estimators in a Hausman test. There is an alternative method for conducting joint tests, which can be implemented ea...