

Q ratio
Or, "Tobin's Q". The ratio of the market value of a firm to the replacement cost of everything in the firm. In Tobin's model this was the driving force behind investment decisions.

Qstatistic
Of LjungBox. A test for higherorder serial correlation in residuals from a regression.

QJE
Quarterly Journal of Economics

QLR
quasilikelihood ratio statistic

QML
Stands for quasimaximum likelihood.

quango
Stands for quasinongovernmental organization, such as the U.S. Federal Reserve. The term is British.

quartic kernel
The quartic kernel is this function: (15/16)(1u^{2})^{2} for 1<u<1 and zero for u outside that range. Here u=(xx_{i})/h, where h is the window width and x_{i} are the values of the independent variable in the data, and x is the value of the independent variable for which one seeks an estimate. For kernel estimation.

quasi rents
returns in excess of the shortrun opportunity cost of the resources devoted to the activity

quasidifferencing
a process that makes GLS easier, computationally, in a fixedeffects kind of case. One generates a (delta) with an equation [see B. Meyer's notes, installment 2, page 3] then subtracts delta times the average of each individual's x from the list of x's, and delta times each individual's y from the list of y's, and can run OLS on that. The calculation of delta requires some estimate of the idiosyncratic (epsilon) error variance and the individual effects (mu) error variance.

quasihyperbolic discounting
A way of accounting in a model for the difference in the preferences an agent has over consumption now versus consumption in the future.
Let b and d be scalar real parameters greater than zero and less than one. Events t periods in the future are discounted by the factor bd^{t}.
This formulation comes from a 1999 working paper of C. Harris and D. Laibson which cites Phelps and Pollak (1968) and Zeckhauser and Fels (1968) for this function.
Contrast hyperbolic discounting, and see more information on discount rates at that entry.

quasimaximum likelihood
Often abbreviated QML. Maximum likelihood estimation can't be applied to a econometric model which has no assumption about error distributions, and may be difficult if the model has assumptions about error distributions but the errors are not normally distributed. Quasimaximum likelihood is maximum likelihood applied to such a model with the alteration that errors are presumed to be drawn from a normal distribution. QML can often make consistent estimates.
QML estimators converge to what can be called a quasitrue estimate; they have a quasiscore function which produces quasiscores, and a quasiinformation matrix. Each has maximum likelihood analogues.

quasiconcave
A function f(x) mapping from the reals to the reals is quasiconcave if it is nondecreasing for all values of x below some x_{0} and nonincreasing for all values of x above x_{0}. x_{0} can be infinity or negative infinity: that is, a function that is everywhere nonincreasing or nondecreasing is quasiconcave.
Quasiconcave functions have the property that for any two points in the domain, say x_{1} and x_{2}, the value of f(x) on all points between them satisfies: f(x) >= min{f(x_{1}), f(x_{2})}.
Equivalently, f() is quasiconcave iff f() is quasiconvex.
Equivalently, f() is quasiconcave iff for any constant real k, the set of values x in the domain of f() for which f(x) >= k is a convex set.
The most common use in economics is to say that a utility function is quasiconcave, meaning that in the relevant range it is nondecreasing.
A function that is concave over some domain is also quasiconcave over that domain. (Proven in Chiang, p 390).
A strictly quasiconcave utility function is equivalent to a strictly convex set of preferences, according to Brad Heim and Bruce Meyer (2001) p. 17.

quasiconvex
A function f(x) mapping from the reals to the reals is quasiconvex if it is nonincreasing for all values of x below some x_{0} and nondecreasing for all values of x above x_{0}. x_{0} can be infinity or negative infinity: that is, a function that is everywhere nonincreasing or nondecreasing is quasiconvex.
Quasiconvex functions have the property that for any two points in the domain, say x_{1} and x_{2}, the value of f(x) on all points between them satisfies: f(x) <= max{f(x_{1}), f(x_{2})}.
Equivalently, f() is quasiconvex iff f() is quasiconcave.
Equivalently, f() is quasiconvex iff for any constant real k, the set of values x in the domain of f() for which f(x) <= k is a convex set.
A function that is convex over some domain is also quasiconvex over that domain. (Proven in Chiang, p 390).
