Search results
Results from the WOW.Com Content Network
In probability theory, Slutsky's theorem extends some properties of algebraic operations on convergent sequences of real numbers to sequences of random variables. [ 1 ] The theorem was named after Eugen Slutsky . [ 2 ]
Convergence in distribution is the weakest form of convergence typically discussed, since it is implied by all other types of convergence mentioned in this article. However, convergence in distribution is very frequently used in practice; most often it arises from application of the central limit theorem .
There are two parts of the Slutsky equation, namely the substitution effect and income effect. In general, the substitution effect is negative. Slutsky derived this formula to explore a consumer's response as the price of a commodity changes. When the price increases, the budget set moves inward, which also causes the quantity demanded to decrease.
A more rigorous definition takes into account the fact that θ is actually unknown, and thus, the convergence in probability must take place for every possible value of this parameter. Suppose { p θ : θ ∈ Θ } is a family of distributions (the parametric model ), and X θ = { X 1 , X 2 , …
Slutsky is principally known for work in deriving the relationships embodied in the Slutsky equation widely used in microeconomic consumer theory for separating the substitution effect and the income effect of a price change on the total quantity of a good demanded following a price change in that good, or in a related good that may have a cross-price effect on the original good quantity.
This page lists articles related to probability theory.In particular, it lists many articles corresponding to specific probability distributions.Such articles are marked here by a code of the form (X:Y), which refers to number of random variables involved and the type of the distribution.
For instance, ideally the solution of a differential equation discretized via a regular grid will converge to the solution of the continuous equation as the grid spacing goes to zero, and if so the asymptotic rate and order of that convergence are important properties of the gridding method.
The rate of convergence must be chosen carefully, though, usually h ∝ n −1/5. In many cases, highly accurate results for finite samples can be obtained via numerical methods (i.e. computers); even in such cases, though, asymptotic analysis can be useful. This point was made by Small (2010, §1.4), as follows.