Slutsky's theorem

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] Slutsky's theorem is also attributed to Harald Cramér.^[3]

Let ${\displaystyle X_{n},Y_{n}}$ be sequences of scalar/vector/matrix random elements. If ${\displaystyle X_{n}}$ converges in distribution to a random element ${\displaystyle X}$ and ${\displaystyle Y_{n}}$ converges in probability to a constant ${\displaystyle c}$, then

• ${\displaystyle X_{n}+Y_{n}\ {\xrightarrow {d}}\ X+c;}$
• ${\displaystyle X_{n}Y_{n}\ \xrightarrow {d} \ Xc;}$
• ${\displaystyle X_{n}/Y_{n}\ {\xrightarrow {d}}\ X/c,}$   provided that c is invertible,

where ${\displaystyle {\xrightarrow {d}}}$ denotes convergence in distribution.

Notes:

1. The requirement that Y_n converges to a constant is important — if it were to converge to a non-degenerate random variable, the theorem would be no longer valid. For example, let ${\displaystyle X_{n}\sim {\rm {Uniform}}(0,1)}$ and ${\displaystyle Y_{n}=-X_{n}}$. The sum ${\displaystyle X_{n}+Y_{n}=0}$ for all values of n. Moreover, ${\displaystyle Y_{n}\,\xrightarrow {d} \,{\rm {Uniform}}(-1,0)}$, but ${\displaystyle X_{n}+Y_{n}}$ does not converge in distribution to ${\displaystyle X+Y}$, where ${\displaystyle X\sim {\rm {Uniform}}(0,1)}$, ${\displaystyle Y\sim {\rm {Uniform}}(-1,0)}$, and ${\displaystyle X}$ and ${\displaystyle Y}$ are independent.^[4]
2. The theorem remains valid if we replace all convergences in distribution with convergences in probability.

This theorem follows from the fact that if X_n converges in distribution to X and Y_n converges in probability to a constant c, then the joint vector (X_n, Y_n) converges in distribution to (X, c) (see here).

Next we apply the continuous mapping theorem, recognizing the functions g(x,y) = x + y, g(x,y) = xy, and g(x,y) = x y⁻¹ are continuous (for the last function to be continuous, y has to be invertible).

• Convergence of random variables

• Casella, George; Berger, Roger L. (2001). Statistical Inference. Pacific Grove: Duxbury. pp. 240–245. ISBN 0-534-24312-6.
• Grimmett, G.; Stirzaker, D. (2001). Probability and Random Processes (3rd ed.). Oxford.
• Hayashi, Fumio (2000). Econometrics. Princeton University Press. pp. 92–93. ISBN 0-691-01018-8.