Maximum Entropy Econometrics: Robust Estimation with Limited Data

by Amos Golan, Douglas Miller, George G. Judge

Renowned experts in the field consider the problems of recovering and processing information when the underlying data is limited or partial and the corresponding models that form the basis for estimation and inference are ill-posed or underdetermined. This book presents a nonlinear inversion procedure which provides a foundation for making conservative inferences about an unknown and unobservable number, vector or function. Parts one and two deal with handling pure and noise type stationary and non-stationary inverse problems. The final segment uses entropy techniques to analyze data for a range of underdetermined economic/econometric problems.

329 pages, Hardcover

First published April 19, 1996