Schedule of topics and readings for Fall 2004

Page references are to the Studenmund text unless specified otherwise

0. Intro to the course

Course outline, methods, mark allocation, and resources

1. Introduction to econometrics/regression analysis (Studenmund ch. 1)

Description, hypothesis testing, forecasting

Single equation regression analysis as one approach

Specifying a regression equation

Estimating the coefficients of a regression equation

Uncertainty from sampling variation of the coefficients (16.4.2)

2. Dealing with uncertainty: review of probability (ch. 16 or Gary Smith, Statistical Reasoning 3/E chs. 2-5)

Data Generating Processes (DGPs) and other basic jargon (handout)

Defining probability as relative frequency in the population

Probability and odds ratios (odds handouts on web page)

Describing probability: (16.2 and Smith, ch. 4)

probability distributions, discrete or continuous, marginal or cumulative

mean/expectation/expected value/first moment – the centre point

variance/standard deviation/second moment – the width

normal, uniform, binomial, Bernoulli, … - the shape

standardized distributions

rules for means and variances (web page: degrees of freedom)

How probabilities combine and interact (joint prob. distributions):

joint and conditional probabilities (Smith 3.3)

Special application to horse-racing wagers (Smith, 159-163)

Means, variances and covariances (web page: covariances)

Special applications:

risk/return analysis in financial markets (web page: variances)

Capital Asset Pricing Model as a special case (Smith, 14.2)

The Law of Large Numbers (Smith, p. 210)

The Central Limit Theorem (p. 540)

Estimating probabilities in real life

sample probabilities and sequential learning

Bayes’ Rule for revising subjective probabilities (web page and Smith, 120-129)

3. Using probability in statistical inference (16.5, and Smith, ch. 10)

Estimators (of means and other parameters) and their sampling distributions

Hypothesis tests (classical inference)

Type I and II errors (handout on web page; Smith 446-449)

Confidence intervals

Applications: tests / confidence intervals for differences between means

Tests / confidence intervals for regression coefficients (ch. 5)

Mid-term exam about here

4. Fundamentals of regression analysis:

Simple and multiple regression (chs. 1- 4)

Steps in estimating a regression equation (ch. 3)

Least Squares estimators (formulae) for coefficients (ch. 2)

Evaluating the quality and fit of a regression model (ch. 2.3-2.5, and 6.2.3)

Best-Case Scenario: the Classical Model where OLS is BLUE (ch. 4)

Residuals and the constant term coefficient (web page: zero means)

Using dummy variables (ch. 7.4 - 7.5)

5. Testing and Improving regression models (mainly in 18.318 in Term 2)

Mis-Specification (chs. 6-7)

Multicollinearity (ch. 8)

Serial Correlation of the residuals (ch. 9)

Heteroscedasticity (ch. 10)