Sensitivity analysis of normative economic models: theoretical framework and practical strategies

Abstract

The parameter values and assumptions of any economic model are subject to change and error. Sensitivity analysis (SA), broadly defined, is the investigation of these potential changes and errors and their impacts on conclusions to be drawn from the model. There is a very large literature on procedures and techniques for SA, but it includes almost nothing from economists. This paper is a selective review and overview of theoretical and methodological issues in SA. There are many possible uses of SA, described here within the categories of decision support, communication, increased understanding or quantification of the system, and model development. The paper focuses somewhat on decision support. It is argued that even the simplest approaches to SA can be theoretically respectable in decision support if they are applied and interpreted in a way consistent with Bayesian decision theory. This is not to say that SA results should be formally subjected to a Bayesian decision analysis, but that an understanding of Bayesian probability revision will help the modeller plan and interpret an SA. Many different approaches to SA are described, varying in the experimental design used and in the way results are processed. Possible overall strategies for conducting SA are suggested. It is proposed that when using SA for decision support, it can be very helpful to attempt to identify which of the following forms of recommendation is most appropriate: (a) do, V, (b) do either X or Y depending on the circumstances, (c) do either X or Y, whichever you like, (d) if in doubt, do X. A system for reporting and discussing SA results is recommended.

An Accurate Reservoir’s Bubble Point Pressure Correlation

“… 35 , 36 In addition, the TA is conducted to make the GMDH model sample by fixing model features that affect the targets or removing redundant parameters that have less or no effect on the studied model. 53 Trend analysis can also identify significant connections between input and outcomes, guiding the improvement of robust models. 54 Consequently, the trend analysis is an essential part of this research.…”

“…Trend analysis (TA) can be an essential method to study the relationships between the features and targets to represent the actual physical behavior. In addition, the TA can show modeling errors to indicate unexpected relationships between the features and targets to prove the reliability of the studied model. , In addition, the TA is conducted to make the GMDH model sample by fixing model features that affect the targets or removing redundant parameters that have less or no effect on the studied model . Trend analysis can also identify significant connections between input and outcomes, guiding the improvement of robust models .…”

The flexural lumber properties of Pinus patula Schiede ex Schltdl. & Cham. improve with decreasing initial tree spacing

“…where y ijk is the MOE of lumber; β 0 , β 1 , β 2 and β 3 are parameters to be estimated from the data; MFA ijk , WD ijk and K ijk are the MFA, WD and knot frequency of each board; b i is the random effect of the ith tree; and b ij is the random effect of the jth board position within the ith tree. Sensitivity analysis was done on the fixed part of the model to determine the relative "influence" of each variable on MOE (Pannell 1997). In Eq.…”

Deterministic and Explicit: A Quantitative Characterization of the Matrix and Collagen Influence on the Stiffening of Peripheral Nerves Under Stretch

“…Once having defined the previous theoretical framework, the range of variation of Equations ( 9) and ( 10) was explored by changing their main numerical parameters (i.e., K 1 , A, D) through the auxiliary set Q = {0, 0.1, 0.5, 1, 5, 10, 50}, to test their suitability for reproducing the classic evolution of the stress/stretch curve [2,7]. Moreover, to assess their quantitative dependence on the numerical parameters, a sensitivity study was performed through the index [31,32]:…”