This appendix contains additional analyses that we mention but do not report in the paper to preserve space.  Table IA.1: Cross-sectional gravity models (baseline equation (2)) on a sample that includes the natural resources industries.  Table IA.2: Cross-sectional gravity models (baseline equation (2)) with acquirer and target country fixed effects (instead of acquirer and target country cha...

This paper provides a root-n consistent, asymptotically normal weighted least squares estimator of the coefficients in a truncated regression model. The distribution of the errors is unknown and permits general forms of unknown heteroskedasticity. Also provided is an instrumental variables based two stage least squares estimator for this model, which can be used when some regressors are endogen...

This paper considers prediction intervals for a future observation in the context of mixed linear models. For such prediction problems, it is reasonable to assume that the future observation is independent of the current ones. Our approach is distribution-free, that is, we do not assume that the distributions of the random effects and errors are normal or specified up to a finite number of para...

This article proposes a new class of rating scale models, which merges advantages and overcomes shortcomings of the traditional linear and ordered latent regression models. Both parametric and semi-parametric estimation is considered. The insights of an empirical application to satisfaction data are threefold. First, the methods are easily implementable in standard statistical software. Second,...

Abstract: We consider M estimation of a regression model with a nuisance parameter and a vector of other parameters. The unknown distribution of the residuals is not assumed to be normal or symmetric. Simple and easily estimated formulas are given for the dominant terms of the bias and skewness of the parameter estimates. For the linear model these are proportional to the skewness of the ‘indep...

The engagement of external IS professionals to supplement in-house resources is a widespread and growing practice. Limited prior research on consultant engagement suggests client involvement is a key factor of success. With the objective of better understanding the antecedents of client involvement in computer system selection consultancies, several variations on Ajzen and Madden’s theory of pl...

This paper studies two types of seasonal time series models with periodic variances. Covariance structures of the noise component in the models are discussed. For parameters in the regression component, the performance of the least squares estimates relative to the best linear unbiased estimates is investigated, and some lower bounds for the eecient coeecient deened by the covariance matrices o...

We consider nonlinear and heteroscedastic autoregressive models whose residuals are martingale increments with conditional distributions that fulfill certain constraints. We treat two classes of constraints: residuals depending on the past through some function of the past observations only, and residuals that are invariant under some finite group of transformations. We determine the efficient ...

We consider semiparametric additive regression models with a linear parametric part and a nonparametric part, both involving multivariate covariates. For the nonparametric part we assume two models. In the first, the regression function is unspecified and smooth; in the second, the regression function is additive with smooth components. Depending on the model, the regression curve is estimated ...

The article investigates the finite sample properties of estimators for spatial autoregressive models where the disturbance terms may follow a spatial autoregressive process. In particular we investigate the finite sample behavior of the feasible generalized spatial two-stage least squares (FGS2SLS) estimator introduced by Kelejian and Prucha (1998), the maximum likelihood (ML) estimator, as we...