We present the one-inflated zero-truncated negative binomial (OIZTNB) model, and propose its use as the truncated count distribution in Horvitz-Thompson estimation of an unknown population size. In the presence of unobserved heterogeneity, the zero-truncated negative binomial (ZTNB) model is a natural choice over the positive Poisson (PP) model; however, when one-inflation is present the ZTNB model either suffers from a boundary problem, or provides extremely biased population size estimates. Monte Carlo evidence suggests that in the presence of one-inflation, the Horvitz-Thompson estimator under the ZTNB model can converge in probability to infinity. The OIZTNB model gives markedly different population size estimates compared to some existing truncated count distributions, when applied to several capture-recapture data that exhibit both one-inflation and unobserved heterogeneity.

Summary. In population size estimation, many capture-recapture-type data exhibit a preponderance of '1'-counts. This excess of 1s can arise as subjects gain information from the initial capture that provides a desire and ability to avoid subsequent captures. Existing population size estimators that purport to deal with heterogeneity can be much too large in the presence of 1-inflation, which is a specific form of heterogeneity. To deal with the phenomena of excess 1s, we propose the one-inflated positive Poisson model for use as the truncated count distribution in Horvitz-Thompson estimation of the population size.

Testing for multivariate cointegration when the data exhibit structural breaks is a problem that is encountered frequently in empirical economic analysis. The standard tests must be modified in this situation, and the asymptotic distributions of the test statistics change accordingly. We supply code that allows practitioners to easily calculate both p-values and critical values for the trace tests of Johansen et al. (2000). Access is also provided to tables of critical values for a broad selection of situations.

The Lomax (Pareto II) distribution has found wide application in a variety of fields. We analyze the second-order bias of the maximum likelihood estimators of its parameters for finite sample sizes, and show that this bias is positive. We derive an analytic bias correction which reduces the percentage bias of these estimators by one or two orders of magnitude, while simultaneously reducing relative mean squared error. Our simulations show that this analytic bias correction outperforms a correction based on the parametric bootstrap. Three examples with actual data illustrate the application of our methods.

No abstract