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.

Source: econterms

Of Ljung-Box. A test for higher-order serial correlation in residuals from a regression.

Source: econterms

Quarterly Journal of Economics

Source: econterms

quasi-likelihood ratio statistic

Source: econterms

Stands for quasi-maximum likelihood.

Source: econterms

Stands for quasi-non-governmental organization, such as the U.S. Federal Reserve. The term is British.

Source: econterms

The quartic kernel is this function: (15/16)(1-u²)² for -1<u<1 and zero for u outside that range. Here u=(x-x_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.

Source: econterms

returns in excess of the short-run opportunity cost of the resources devoted to the activity

Source: econterms

a process that makes GLS easier, computationally, in a fixed-effects 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.

Source: econterms

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.

Source: econterms

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. Quasi-maximum 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 quasi-true estimate; they have a quasi-score function which produces quasi-scores, and a quasi-information matrix. Each has maximum likelihood analogues.

Source: econterms

A function f(x) mapping from the reals to the reals is quasiconcave if it is nondecreasing for all values of x below some x₀ and nonincreasing for all values of x above x₀. x₀ 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 x₁ and x₂, the value of f(x) on all points between them satisfies:
f(x) >= min{f(x₁), f(x₂)}.

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.

Source: econterms

A function f(x) mapping from the reals to the reals is quasiconvex if it is nonincreasing for all values of x below some x₀ and nondecreasing for all values of x above x₀. x₀ 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 x₁ and x₂, the value of f(x) on all points between them satisfies:
f(x) <= max{f(x₁), f(x₂)}.

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).

Source: econterms