This paper examines the issue of weak identification in maximum likelihood, motivated by problems with estimation and inference in a multidimensional dynamic stochastic general equilibrium model. We show that two forms of the classical score (Lagrange multiplier) test for a simple hypothesis...

In a model with endogenous regressors, heteroskedastic and autocorrelated (HAC) errors and weak instruments, tests that depend on the data only through the Anderson-Rubin (AR) and Lagrange Multiplier (LM) statistics ignore important information on the regression coefficients. This is in contrast...

Traditional pre-1929 consumption measures understate the extent of serial correlation in the US annual real growth rate of per capita consumption of non-durables and services due to measurement limitations in the construction of their major components. Under alternative measures proposed in this...

In this paper, we consider the estimation problem of individual weights of three objects. For the estimation we use the chemical balance weighing design and the criterion of D-optimality. We assume that the error terms <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$${\varepsilon_{i},\ i=1,2,\dots,n,}$$</EquationSource> </InlineEquation> are a first-order autoregressive...</equationsource></inlineequation>

Binary data latent class analysis is a form of model-based clustering applied in a wide range of fields. A central assumption of this model is that of conditional independence of responses given latent class membership, often referred to as the “local independence” assumption. The results of...

This article investigates the application of depth estimators to crack growth models in construction engineering. Many crack growth models are based on the Paris–Erdogan equation which describes crack growth by a deterministic differential equation. By introducing a stochastic error term,...