Technical efficiency techs and misspesification in stochastic

Essentially there are two main methodologies for measuring technical efficiency: the econometric (or parametric) approach, and the mathematical (or non-parametric) approach. The two techniques use different methods to envelop data, and in doing so they make different accommodation for random noise and for flexibility in the structure of production technology. Hence they differ in many ways, but the advantages of one approach over the other boil down to two characteristics:

• the econometric approach is stochastic and attempts to distinguish between the effects of noise and the effects of inefficiency, while the linear programming approach is deterministic and under the voice inefficiency melt noise and real inefficiency;

• the econometric approach is parametric and as a result suffers from functional form misspecification, while the programming approach is non-parametric and so it is immune to any form of functional misspecification.

Specification (regression)

In regression analysis and related fields such as econometrics, specification is the process of converting a theory into a regression model. This process consists of selecting an appropriate functional form for the model and choosing which variables to include. Model specification is one of the first steps in regression analysis. If an estimated model is misspecified, it will be biased and inconsistent.[1]

Specification error and bias

Specification error occurs when an independent variable is correlated with the error term. There are several different causes of specification error:

  • incorrect functional form
  • a variable omitted from the model may have a relationship with both the dependent variable and one or more of the independent variables (omitted-variable bias);[2]
  • an irrelevant variable may be included in the model
  • the dependent variable may be part of a system of simultaneous equations (simultaneity bias)

measurement errors may affect the independent variables


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