TITLE:
Estimating a Falsified Model
AUTHORS:
Andrew J. Buck, George M. Lady
KEYWORDS:
Qualitative Analysis, Regression, Model Falsification
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.6 No.8,
July
14,
2016
ABSTRACT: It is common econometric practice to propose a system of equations, termed the “structure,” estimate each endogenous variable in the structure via a linear regression with all of the exogenous variables as arguments, and then employ one of variety of regression techniques to recapture the coefficients in the (Jacobian) arrays of the structure. A recent literature, e.g., [1], has shown that a qualitative analysis of a model’s structural and estimated reduced form arrays can provide a robust procedure for assessing if a model’s hypothesized structure has been falsified. This paper shows that the even weaker statement of the model’s structure provided by zero restrictions on the structural arrays can be falsified, independent of the proposed nonzero entries. When this takes place, multi-stage least squares, or any procedure for estimating the structural arrays with the zero restrictions imposed, will present estimates that could not possibly have generated the data upon which the estimated reduced form is based. The examples given in the paper are based upon a Monte Carlo sampling procedure.