Asymptotic normality is a property of an estimator, whereby as the sample size increases, the distribution of the estimator approaches a normal distribution, regardless of the original distribution of the data. This property is crucial for making statistical inferences about population parameters, as it allows for the use of normal distribution-based confidence intervals and hypothesis tests even when the sample size is not large enough to assume normality initially.
Two-Step Estimation is a statistical method used to improve parameter estimation by first obtaining preliminary estimates and then refining them in a second step. This approach is particularly useful in complex models where direct estimation is challenging, allowing for more accurate and efficient computation of parameters.