Non-extensive Entropy Econometrics for Low Frequency Series provides a new and robust
power-law-based non-extensive entropy econometrics approach to the economic modelling of
ill-behaved inverse problems. Particular attention is paid to national account-based general
equilibrium models known for their relative complexity. In theoretical terms the approach
generalizes Gibbs-Shannon-Golan entropy models which are useful for describing ergodic
phenomena. In essence this entropy econometrics approach constitutes a junction of two
distinct concepts: Jayne's maximum entropy principle and the Bayesian generalized method of
moments. Rival econometric techniques are not conceptually adapted to solving complex inverse
problems or are seriously limited when it comes to practical implementation. Recent literature
showed that amplitude and frequency of macroeconomic fluctuations do not substantially diverge
from many other extreme events natural or human-related once they are explained in the same
time (or space) scale. Non-extensive entropy is a precious device for econometric modelling
even in the case of low frequency series since outputs evolving within the Gaussian attractor
correspond to the Tsallis entropy limiting case of Tsallis q-parameter around unity. This book
introduces a sub-discipline called Non-extensive Entropy Econometrics or using a recent
expression Superstar Generalised Econometrics. It demonstrates using national accounts-based
models that this approach facilitates solving nonlinear complex inverse problems previously
considered intractable such as the constant elasticity of substitution class of functions.
This new proposed approach could extend the frontier of theoretical and applied econometrics.
