Constrained Least Squares Simplicial-Simplicial Regression

Michail Tsagris

No 2402, Working Papers from University of Crete, Department of Economics

Abstract: Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficients themselves lie within the simplex, is proposed. The model is transformation-free but the adoption of a power transformation is straightforward, it can treat more than one compositional datasets as predictors and offers the possibility of weights among the simplicial predictors. Among the model’s advantages are its ability to treat zeros in a natural way and a highly computationally efficient algorithm to estimate its coefficients. Resampling based hypothesis testing procedures are employed regarding inference, such as linear independence, and equality of the regression coefficients to some pre-specified values. The performance of the proposed technique and its comparison to an existing methodology that is of the same spirit takes place u

Keywords: compositional data; regression; quadratic programming (search for similar items in EconPapers)
JEL-codes: C10 C13 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2024-03-31
New Economics Papers: this item is included in nep-ecm
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