## 4.9 Consistency

An estimator \(\hat{\theta}\) of \(\theta\) is said to be **consistent** if \(\hat{\theta}\) gets close to \(\theta\) for large values of \(n\).

The main tool for studying consistency is the **law of large numbers**. The law of large numbers says that sample averages converge to population averages as the sample size gets large. In math, this is

\[ \frac{1}{n} \sum_{i=1}^n Y_i \rightarrow \mathbb{E}[Y] \quad \textrm{as } n \rightarrow \infty \] In my view, the law of large numbers is very intuitive. If you have a large sample and calculate a sample average, it should be close to the population average.

**Example 4.2 **Let’s consider the same three estimators as before and whether or not they are consistent. First, the LLN implies that

\[ \hat{\mu} = \frac{1}{n} \sum_{i=1}^n Y_i \rightarrow \mathbb{E}[Y] \] This implies that \(\hat{\mu}\) is consistent. Next,

\[ \hat{\mu}_1 = Y_1 \] doesn’t change depending on the size of the sample (you just use the first observation), so this is not consistent. This is an example of an unbiased estimator that is not consistent. Next,

\[ \hat{\mu}_\lambda = \lambda \bar{Y} \rightarrow \lambda \mathbb{E}[Y] \neq \mathbb{E}[Y] \] which implies that (as long as \(\lambda\) doesn’t change with \(n\)), \(\hat{\mu}_{\lambda}\) is not consistent. Let’s give one more example. Consider the estimator

\[ \hat{\mu}_c := \bar{Y} + \frac{c}{n} \] where \(c\) is some constant (this is a strange estimate of \(\mathbb{E}[Y]\) where we take \(\bar{Y}\) and add a constant divided by the sample size). In this case,

\[ \hat{\mu}_c \rightarrow \mathbb{E}[Y] + 0 = \mathbb{E}[Y] \]

which implies that it is consistent. It is interesting to note that

\[ \mathbb{E}[\hat{\mu}_c] = \mathbb{E}[Y] + \frac{c}{n} \] which implies that it is biased. This is an example of a biased estimator that is consistent.