Contexts: data analytic: Often means 'algebraic', as opposed to 'numeric'.
Or maybe they like "a priori" because it is so short. abnormal returns: Used in the context of stock returns; means the return to a portfolio in excess of the return to a market portfolio.
Contexts: phrases A-D equilibrium: abbreviation for Arrow-Debreu equilibrium. Contrast excess returns which means something else. Example: Suppose average market return to a stock was 10% for some calendar year, meaning stocks overall were 10% higher at the end of the year than at the beginning, and suppose that stock S had risen 12% in that period. Contexts: finance absolute risk aversion: An attribute of a utility function. Contexts: micro theory; finance absorptive capacity: A limit to the rate or quantity of scientific or technological information that a firm can absorb. "Absorptive capacity: a new perspective on learning and innovation." Administrative Science Quarterly 35(1) pp 128-152.
If such limits exist they provide one explanation for firms to develop internal R&D capacities. Contexts: IO; organizations; theory of the firm abstracting from: a phrase that generally means "leaving out".
R&D departments can not only conduct development along lines they are already familiar with, but they have formal training and external professional connections that make it possible for them to evaluate and incorporate externally generated technical knowledge into the firm better than others in the firm can. A model abstracts from some elements of the real world in its demonstration of some specific force.
Source: Branson Contexts: macro acceptance region: Occurs in the context of hypothesis testing. Possible values of T can be divided into two regions, the acceptance region and the rejection region.
If the value of T comes out to be in the acceptance region, the null hypothesis being tested is not rejected.
This editor is advised that there is some mathematical difference.
Source: Milgrom and Weber, Econometrica, 1982, p 1096.
The AIC is a number associated with each model: AIC=ln (s = (sum of squared residuals for model m)/T. The criterion may be minimized over choices of m to form a tradeoff between the fit of the model (which lowers the sum of squared residuals) and the model's complexity, which is measured by m.
Thus an AR(m) model versus an AR(m 1) can be compared by this criterion for a given batch of data. 5-18 Contexts: econometrics; time series alienation: A Marxist term.
An affine function may have a nonzero value when the independent variables are zero.