

Validity
Validity tells us the degree to which a test really measures the behaviour
it was designed for

value added
A measure of output. Value added by an organization or industry is, in principle:
revenue  nonlabor costs of inputs
where revenue can be imagined to be price*quantity, and costs are usually described by capital (structures, equipment, land), materials, energy, and purchased services.
Treatment of taxes and subsidies can be nontrivial.
Valueadded is a measure of output which is potentially comparable across countries and economic structures.

value function
Often denoted v() or V(). Its value is the present discounted value, in consumption or utility terms, of the choice represented by its arguments. The classic example, from Stokey and Lucas, is: v(k) = max_{k'} { u(k, k') + bv(k') } where k is current capital, k' is the choice of capital for the next (discrete time) period, u(k, k') is the utility from the consumption implied by k and k', b is the periodtoperiod discount factor, and the agent is presumed to have a timeseparable function, in a discrete time environment, and to make the choice of k' that maximizes the given function.

VAR
Vector Autoregression, a kind of model of related time series. In the simplest example, the vector of data points at each time t (y_{t}) is thought of as a parameter vector (say, phi1) times a previous value of the data vector, plus a vector of errors about which some distribution is assumed. Such a model may have autoregression going back further in time than t1 too.

var()
An operator returning the variance of its argument

variance
The variance of a distribution is the average of squares of the distances from the values drawn from the mean of the distribution: var(x) = E[(xEx)^{2}]. Also called 'centered second moment.' Nick Cox attributes the term to R.A. Fisher, 1918.

variance decomposition
In a VAR, the variance decomposition at horizon h is the set of R^{2} values associated with the dependent variable y_{t} and each of the shocks h periods prior.

variance ratio statistic
discussed thoroughly on BollerslevHodrick 1992 p. 19. Equations and estimation there.

VARs
Vector Autoregressions. "Vector autoregressive models are _atheoretical_ models that use only the observed time series properties of the data to forecast economic variables." Unlike structural models there are no assumptions/restrictions that theorists of different stripes would object to. But a VAR approach only test LINEAR relations among the time series.

vec
An operator. For a matrix C, vec(C) is the vector constructed by stacking all of the columns of C, the second below the first and so on. So if C is n x k, then vec(C) is nk x 1.

vega
As used with respect to options: "The vega of a portfolio of derivatives is the rate of change fo the value of the portfolio with respect to the volatility of the underlying asset."  Hull (1997) p 328. Formally this is a partial derivative.
A portfolio is veganeutral if it has a vega of zero.

verifiable
Observable to outsiders, in the context of a model of information.
Models commonly assume that some the values of some variables are known to both of the parties to a contract but are NOT verifiable, by which we mean that outsiders cannot see them and so references to those variables in a contract between the two parties cannot be enforced by outside authorities.
Examples: .....

vintage model
One in which technological change is 'embodied' in Solow's language.

vNM
Abbreviation for von NeumannMorgenstern, which describes attributes of some utility functions.

volatility clustering
In a time series of stock prices, it is observed that the variance of returns or logprices is high for extended periods and then low for extended periods. (E.g. the variance of daily returns can be high one month and low the next.) This occurs to a degree that makes an iid model of logprices or returns unconvincing. This property of time series of prices can be called 'volatility clustering' and is usually approached by modeling the price process with an ARCHtype model.

von NeumannMorgenstern utility
Describes a utility function (or perhaps a broader class of preference relations) that has the expected utility property: the agent is indifferent between receiving a given bundle or a gamble with the same expected value.
There may be other, or somewhat stronger or weaker assumptions in the vNM phrasing but this is a basic and important one. It does not seem to be the case that such a utility representation is required to be increasing in all arguments or concave in all arguments, although these are also common assumptions about utility functions. The name refers to John von Neumann and Oskar Morgenstern's Theory of Games and Economic Behavior. Kreps (1990), p 76, says that this kind of utility function predates that work substantially, and was used in the 1700s by Daniel Bernoulli.
