Chapter 1-An Introduction to Dierence
Equations
Dek Terrell
August 26, 2014
1
Goals
2
Goals
Introduce basic concept of dierence equations
3
Goals
Introduce basic concept of dierence equations
Provide an example of Dierence Equations
4
Goals
Introduce basi
The Gibbs Sampler
Dek Terrell
September 12, 2013
1
Goals
2
Goals
Introduction to the Gibbs Sampler
3
Goals
Introduction to the Gibbs Sampler
Application on the Soda Example
4
Goals
Introduction to the Gibbs Sampler
Application on the Soda Example
Check ou
Final items from Chapter 3
Dek Terrell
September 17, 2013
1
Goals
2
Goals
Matrix Notation for Random Variables
3
Goals
Matrix Notation for Random Variables
The covariance matrix
4
Goals
Matrix Notation for Random Variables
The covariance matrix
Check our
Chapter 5 Basic Asymptotics
Dek Terrell
October 1, 2013
1
Goals
2
Goals
Binomial Example from Chapter 4
3
Goals
Binomial Example from Chapter 4
Introduce convergence in distribution
4
Goals
Binomial Example from Chapter 4
Introduce convergence in distribu
Chapter 5 Basic Asymptotics (2)
Dek Terrell
October 3, 2013
1
Goals
2
Goals
Order of Convergence in Probability
3
Goals
Order of Convergence in Probability
The Delta Method
4
Goals
Order of Convergence in Probability
The Delta Method
Weak Law of Large Num
Chapter 7 Point Estimation Theory
Dek Terrell
October 10, 2013
1
Goals
2
Goals
Simplication of the Mittelhammers notation
3
Goals
Simplication of the Mittelhammers notation
Estimator
4
Goals
Simplication of the Mittelhammers notation
Estimator
Estimate
5
Chapter 8 Point Estimation Methods
Dek Terrell
October 15, 2013
1
Goals
2
Goals
Maximum Likelihood Estimation
3
Goals
Maximum Likelihood Estimation
Method of Moments
4
Goals
Maximum Likelihood Estimation
Method of Moments
Obtaining a Bayesian Posterior
5
Chapter 8 Point Estimation Methods
Dek Terrell
October 15, 2013
1
Goals
2
Goals
Compute the Bayesian Posterior density for our problem
3
Goals
Compute the Bayesian Posterior density for our problem
Demonstrate the role of the prior density in this Bayesia
EstimationBayesian Approach
Dek Terrell
October 15, 2013
1
Goals
2
Goals
Discuss the philosophy of estimation and inference
3
Goals
Discuss the philosophy of estimation and inference
Introduce Credible sets and Highest Posterior Density Interval
4
Goals
D
Chapter 9/10 Hypothesis Tests and Interval
Estimation
Dek Terrell
October 29, 2013
1
Goals
2
Goals
Introduction to hypothesis testing
3
Goals
Introduction to hypothesis testing
Model of the mean known variance
4
Goals
Introduction to hypothesis testing
Mo
Chapter 9/10 Hypothesis Tests and Interval
Estimation
Dek Terrell
October 29, 2013
1
Goals
2
Goals
Model of the mean in matrix notation
3
Goals
Model of the mean in matrix notation
Idempotent matrix
4
Goals
Model of the mean in matrix notation
Idempotent
Linear Regression (The General Linear Model)
Dek Terrell
November 5,2013
Table of contents
The basic model
Notation
Assumptions
Least Squares
Matrix Derivatives and the Least Squares estimator
Deriving the Least Squares Estimator
Properties of the Least S
Chapters 9 & 10 Three Hypothesis Tests
Dek Terrell
April 25, 2013
We will review three hypothesis tests:
Wald Test
(Generalized) Likelood Ratio Test
Lagrange Multiplier Test
The hypothesis of interest is stated generally as R() = r in the
text, where R
Chapters 2 and 3- Random Variables and
Expectations
Dek Terrell
September 10, 2013
1
Goals
2
Goals
Convert our 10-card discrete example to a more compact
notation
3
Goals
Convert our 10-card discrete example to a more compact
notation
Check for independen
Chapter 2-Multivariate Random Variables
Dek Terrell
September 5, 2013
1
Goals
2
Goals
Introduce the concept of a Multivariate Random Variable
3
Goals
Introduce the concept of a Multivariate Random Variable
Introduce multivariate probability density functi
Lecture 5
More ARMA Models
Lecture 5
Dek Terrell
September 9, 2014
Dek Terrell
Lecture 5
September 9, 2014
1 / 30
Lecture 5
Goals
Goals
Dek Terrell
Lecture 5
September 9, 2014
2 / 30
Lecture 5
Goals
Goals
Introduce autocorrelation
Dek Terrell
Lecture 5
Se
Lecture 6
Evaluation and Application of ARMA Models
Lecture 6
Dek Terrell
September 11, 2014
Dek Terrell
Lecture 6
September 11, 2014
1 / 42
Lecture 6
Goals
Goals
Dek Terrell
Lecture 6
September 11, 2014
2 / 42
Lecture 6
Goals
Goals
Introduce Basis Box Je
Lecture 6
Review and Regroup
Lecture 7
Dek Terrell
September 16, 2014
Dek Terrell ()
Lecture 6
September 16, 2014
1 / 12
Lecture 6
Goals
Goals
Dek Terrell ()
Lecture 6
September 16, 2014
2 / 12
Lecture 6
Goals
Goals
Reminder on basics of maximum likelihoo
Lecture 8
Forecasting with ARIMA models
Lecture 8
Dek Terrell
September 23, 2014
Dek Terrell
Lecture 8
September 23, 2014
1 / 17
Lecture 8
Goals
Goals
Dek Terrell
Lecture 8
September 23, 2014
2 / 17
Lecture 8
Goals
Goals
Forecasts for ARIMA models
Dek Ter
Lecture 13
Point Estimation Methods
Lecture 13 7630
Dek Terrell
Louisiana State University
October 14, 2014
Dek Terrell (LSU)
Lecture 13
October 14, 2014
1/1
Lecture 13
Contents I
Dek Terrell (LSU)
Lecture 13
October 14, 2014
2/1
Lecture 13
Goals of Lectu
Lecture 14
Introduction to Bayesian Inference
Lecture 14 7630
Dek Terrell
Louisiana State University
October 21, 2014
Dek Terrell (LSU)
Lecture 14
October 21, 2014
1 / 14
Lecture 14
Contents I
1. Goals of Lecture 14
2. Bayesian Estimation
2.1. Idea
2.2. T
Lecture 15
More Bayesian Estimation
Lecture 15
Dek Terrell
Louisiana State University
October 23, 2014
Dek Terrell (LSU)
Lecture 15
October 23, 2014
1/1
Lecture 15
Contents I
Dek Terrell (LSU)
Lecture 15
October 23, 2014
2/1
Lecture 15
Goals of Lecture 14
Chapter 1-An Introduction to Probability
Dek Terrell
August 27, 2013
1
Table of contents
2
Goals
3
Goals
Introduce basic denitions and concepts
4
Goals
Introduce basic denitions and concepts
Introduce classical, frequentist, and subjective notions of
prob
Chapter 2-Discrete Random Variables
Dek Terrell
September 3, 2013
1
Goals
2
Goals
Introduce the concept of a Random Variable
3
Goals
Introduce the concept of a Random Variable
Introduce discrete probability density functions and cumulative
density functio
Chapter 2Continuous Probability Densities
Dek Terrell
January 24, 2013
A random variable is called continuous if its range is uncountably
innite and there exists a nonnegative, function f (x), dened for all
x (, ), such that for any event A R(X), PX (A) =