In order to incorporate FDI factors for different MNEs, industries and countries, a unique dataset that includes country-, industryand firmlevel data is constructed. This approach avoids the aggregation or disaggregation problem by considering several levels of data simultaneously in to the analysis. The constructed database contains information on 1,223 foreign investment made by EU15 countrie...

Addressing covariate imbalance in causal analysis will be reformulated as an elimination of the nuisance variables problem. We show, within a counterfactual balanced setting, how averaging, conditioning, and marginalization techniques can be used to reduce bias due to a possibly large number of imbalanced baseline confounders. The notions of X-sufficient and X-ancillary quantities are discussed...

Ghost variables are assignable variables that appear in program annotations but do not correspond to physical entities. They are used to facilitate specification and verification, e.g., by using a ghost variable to count the number of iterations of a loop, and also to express extra-functional behaviours. In this paper we give a formal model of ghost variables and show how they can be eliminated...

A new method for the elimination of uninformative variables in multivariate data sets is proposed. To achieve this, artificial (noise) variables are added and a closed form of the PLS or PCR model is obtained for the data set containing the experimental and the artificial variables. The experimental variables that do not have more importance than the artificial variables, as judged from a crite...

We consider behaviors in which we distinguish two types of variables, mani[est variables, the variables that are of interest to the user and latent variables, the variables that are introduced to obtain a first representation. The problem is to find a representation of th,: manifest behavior, that is, we want to eliminate the latent variables. If the original behavior can be represented by line...