GPS ambiguity resolution is the process of resolving the unknown cycle ambiguities of double differenced carrier phase data as integers. It is the key to highprecision relative GPS positioning when only short observation time spans are used. Once the integer ambiguities have been resolved, the carrier phase measurements will start to act as if they were highprecision pseudorange measurements, t...

Previous research on dynamic panel estimation has focused on panels that, unlike a typical panel of macroeconomic data, have small time dimensions and large individual dimensions. We use a Monte Carlo approach to investigate the performance of several different methods designed to reduce the bias of the estimated coefficients for the longer, narrower panels commonly found for macro data. We fin...

This paper presents an extension of fixed effects binary choice models for panel data, to the case of heterogeneous linear trends. Two estimators are proposed: a Logit estimator based on double conditioning and a semiparametric, smoothed maximum score estimator based on double differences. We investigate small-sample properties of these estimators with a Monte Carlo simulation experiment, and c...

This paper considers the estimation of dynamic models for panel data. It shows how to count and express the moment conditions implied by a variety of covariance restrictions. These conditions can be imposed in a GMM framework. Many of the moment conditions are nonlinear in the parameters. We derive a simple linearized estimator that is asymptotically as efficient as the nonlinear GMM estimator,...

We examine bias corrections which have been proposed for the Fixed Effects Panel Probit model with exogenous regressors, using several different data generating processes to evaluate the performance of the estimators in different situations. We find a best estimator across all cases for coefficient estimates, but when the marginal effects are the quantity of interest no analytical correction is...

This paper considers a Hausman and Taylor (1981) panel data model that exhibits a Cliff and Ord (1973) spatial error structure. We analyze the small sample properties of a Generalized Moments estimation approach for that model. This spatial Hausman-Taylor estimator allows for endogeneity of the time-varying and time-invariant variables with the individual effects. For this model, the spatial fi...

The central limit theorem says that, provided an estimator fulfills certain weak conditions, then, for reasonable sample sizes, the sampling distribution of the estimator converges to normality. We propose a procedure to find out what a “reasonably large sample size” is. The procedure is based on the properties of Gini’s mean difference decomposition. We show the results of implementations of t...