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In this paper we discuss the similarity between the Anderson-Rubin test for overidentification in a Simultaneous Equations Model and the Johansen test for cointegration in a Vector Autoregressive model. The similar structure of the two models is shown to be important in this respect. An...

Correspondence analysis (CA) is a popular method that can be used to analyze relationships between categorical variables. It is closely related to several popular multivariate analysis methods such as canonical correlation analysis and principal component analysis. Like principal component...

This report is the fourth part of a PhD project called "The Econometrics of Maritime Safety – Recommendations to Enhance Safety at Sea" and is based on 183,000 port state control inspections and 11,700 casualties from various data sources. Its overall objective is to provide...

In correspondence analysis, rows and columns of a data matrix are depicted as points in low-dimensional space. The row and column profiles are approximated by minimizing the so-called weighted chi squared distance between the original profiles and their approximations, see or example, Greenacre...

Classical multivariate analysis techniques such as principal components analysis and correspondence analysis use inner products to estimate data values. The results of these techniques may be visualized by representing the row and column points jointly in a biplot where the projection of a row...

In this paper we consider the use of correspondence analysis (CA) of rating data. CA of rating data allows a joint representation of the rated items (e.g. attributes or products) and individuals. However, as the number of individuals increases, the interpretation of the CA map becomes difficult....