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It is possible that any missing variable as well as errors in values of included variables can lead to erroneous results. Model risk: There is a significant amount of model risk inherent in the current mathematical modeling approaches to economics that one must take into account when using them. A good economic theory should be built on sound ...
For example, individual demand can be aggregated to market demand if and only if individual preferences are of the Gorman polar form (or equivalently satisfy linear and parallel Engel curves). Under this condition, even heterogeneous preferences can be represented by a single aggregate agent simply by summing over individual demand to market ...
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.
If the linear model is applicable, a scatterplot of residuals plotted against the independent variable should be random about zero with no trend to the residuals. [5] If the data exhibit a trend, the regression model is likely incorrect; for example, the true function may be a quadratic or higher order polynomial.
In an economic model, an exogenous variable is one whose measure is determined outside the model and is imposed on the model, and an exogenous change is a change in an exogenous variable. [1]: p. 8 [2]: p. 202 [3]: p. 8 In contrast, an endogenous variable is a variable whose measure is determined by the model. An endogenous change is a change ...
The endogeneity problem is particularly relevant in the context of time series analysis of causal processes. It is common for some factors within a causal system to be dependent for their value in period t on the values of other factors in the causal system in period t − 1.
The consequences of such misinterpretations can be quite severe. For example, in medical science, correcting a falsehood may take decades and cost lives. Misuses can be easy to fall into. Professional scientists, mathematicians and even professional statisticians, can be fooled by even some simple methods, even if they are careful to check ...
A variable omitted from the model may have a relationship with both the dependent variable and one or more of the independent variables (causing omitted-variable bias). [3] An irrelevant variable may be included in the model (although this does not create bias, it involves overfitting and so can lead to poor predictive performance).