This preview shows pages 1–3. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: Bayesian Estimation Confidence Intervals: Lecture XXII Charles B. Moss October 21, 2010 I. Bayesian Estimation A. Implicitly in our previous discussions about estimation, we adopted a classical viewpoint. 1. We had some process generating random observations. 2. This random process was a function of fixed, but unknown. 3. We then designed procedures to estimate these unknown pa- rameters based on observed data. B. Specifically, if we assumed that a random process such as students admitted to the University of Florida, generated heights. This height process can be characterized by a normal distribution. 1. We can estimate the parameters of this distribution using maximum likelihood. 2. The likelihood of a particular sample can be expressed as L X 1 , X 2 , X n | , 2 = 1 (2 ) n/ 2 n exp " 1 2 2 n X i =1 ( X i ) 2 # (1) 3. Our estimates of and 2 are then based on the value of each parameter that maximizes the likelihood of drawing that sample. 1 AEB 6571 Econometric Methods I Professor Charles B. Moss Lecture XXII Fall 2010 C. Turning this process around slightly, Bayesian analysis assumes that we can make some kind of probability statement about pa- rameters before we start. The sample is then used to update our prior distribution. 1. First, assume that our prior beliefs about the distribution function can be expressed as a probability density function ( ) where is the parameter we are interested in estimating....
View Full Document
- Spring '10