This paper considers tests for structural instability of short duration, such as at the end of the sample. The key feature of the testing problem is that the number, m, of observations in the period of potential change is relatively small-possibly as small as one. The well-known F test of Chow...

Part I. Identification and Efficient Estimation: 1. Incredible structural inference Thomas J. Rothenberg; 2. Structural equation models in human behavior genetics Arthur S. Goldberger; 3. Unobserved heterogeneity and estimation of average partial effects Jeffrey M. Wooldridge; 4. On specifying...

This paper considers tests of the parameter on endogenous variables in an instrumental variables regression model. The focus is on determining tests that have some optimal power properties. We start by considering a model with normally distributed errors and known error covariance matrix. We...

This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of a general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva's (2007a) modification of...

The local Whittle (or Gaussian semiparametric) estimator of long range dependence, proposed by Künsch (1987) and analyzed by Robinson (1995a), has a relatively slow rate of convergence and a finite sample bias that can be large. In this paper, we generalize the local Whittle estimator to...

This paper establishes the asymptotic distribution of extremum estimators when the true parameter lies on the boundary of the parameter space. The boundary may be linear, curved, and/or kinked. Typically the asymptotic distribution is a function of a multivariate normal distribution in models...

In this paper, we propose a simple bias--reduced log--periodogram regression estimator, "ˆd-sub-r", of the long--memory parameter, "d", that eliminates the first-- and higher--order biases of the Geweke and Porter--Hudak (1983) (GPH) estimator. The bias--reduced estimator is the same as the GPH...

The least squares estimator for the linear regression model is shown to converge to the true parameter vector either with probability one or with probability zero. In the latter case, it either converges to a point not equal to the true parameter with probability one, or it diverges with...