Affiliations: [a] Professor of Economics, Fordham University, 441 E
Fordham Rd, Bronx, New York, 10458, USA
Correspondence:
[*]
Corresponding author: Hrishikesh D. Vinod,
Professor of Economics, Fordham University, 441 E Fordham Rd, Bronx, New York,
10458, USA. Tel.: +1 201 568 5976; E-mail:
[email protected]
Abstract: Replications merely check whether the results reported by authors are
independently verifiable, not whether they are reliable, robust and stable.
Statistical inference deals with specification and sampling errors whereas
subject matter knowledge is needed to avoid errors in interpretation of the
model. Vinod and Ullah [24] suggested perturbing the data beyond the available
digits to evaluate the numerical stability of model results. This paper extends
the idea into a simple algorithm to create random perturbations for checking
perturbation sensitivity (=α
_{p}) of a model, its software and
interpretations. We illustrate the proposed algorithm with archived replication
examples from the Journal of Money Credit and Banking (JMCB) and Journal of
Applied Econometrics. Some journals (against our sentiment) still allow some
parts of the code to be hidden in a black box. Unfortunately, black boxes may
also hide shortcuts and dishonesty, unless all black box users are required to
post the α_{p}
developed here. There is a new requirement at Econometrica to report the
sensitivity of empirical results, and other journals may soon follow. Our
α _{p}
provides a tool for meeting such requirements.
Keywords: Random perturbation, sensitivity analysis, measurement error, seasonality
