This preview shows pages 1–5. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: Large Sample Theory: Lecture XIV Charles B. Moss October 3, 2010 Charles B. Moss () Large Sample Theory October 3, 2010 1 / 13 1 Basic Sample Theory 2 Modes of Convergence Charles B. Moss () Large Sample Theory October 3, 2010 2 / 13 Basic Sample Theory The problems set up is that we want to discuss sample theory. I First assume that we want to make an inference, either estimation or some test, based on a sample. I We are interested in how well parameters or statistics based on that sample represent the parameters or statistics of the whole population. The complete statistical term is known as convergence. I Specifically, we are interested in whether or not the statistics calculated on the sample converge toward the population estimates. I Let { X n } be a sequence of samples. We want to demonstrate that statistics based on { X n } converge toward the population statistics for X . Charles B. Moss () Large Sample Theory October 3, 2010 3 / 13 Taking a slightly different tack: The classical assumptions for ordinary least squares (OLS) as presented in White, Halbert Asymptotic Theory for Econometricians. I Theorem 1.1 : The following are the assumptions of the classical linear model F The model is known to be y = X + , < ....
View
Full
Document
 Spring '10
 Staff

Click to edit the document details