Dhrymes (1994, Econometric Theory, 10, 254-285) demonstrates the arising identification and estimation problems in singular equation systems when the error vector obeys an autoregressive scheme, as an extension of restricted least squares. Unfortunately, his main theorem concerning the...

This paper addresses the combination of incomplete prior and sample information. In difference to the mixed estimation approach developed by H. Theil and A.S. Goldberger, dealing with prior knowledge of regression coefficients, we consider prior information on future observations of the...

This paper presents a comprehensive approach to estimation and hypothesis testing under a set of full restrictions, some of these arising from adding-up conditions on the endogenous variable. In contrast to the existing statistical literature, this paper uses an argumentation style familiar from...

We discuss inference for additive models with random scaling factors. The additive effects are of the form (1+g)f(z) where f is a nonlinear function of the continuous covariate z modeled by P(enalized)-splines and 1+g is a random scaling factor. Additionally, monotonicity constraints on the...

Kalyanam and Shively (1998) and van Heerde et al. (2001) have proposed semiparametric models to estimate the influence of price promotions on brand sales, and both obtained superior performance for their models compared to strictly parametric modeling. Following these researchers, we suggest...

The multinomial logit model (MNL) is one of the most frequently used statistical models in marketing applications. It allows to relate an unordered categorical response variable, for example representing the choice of a brand, to a vector of covariates such as the price of the brand or variables...

Generalized additive models have become a widely used instrument for flexible regression analysis. In many practical situations, however, it is desirable to restrict the flexibility of nonparametric estimation in order to accommodate a presumed monotonic relationship between a covariate and the...

Gaussian Structured Additive Regression provides a flexible framework for additive decomposition of the expected value with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity, and complex interactions between covariates of different types....