Econometrics Lecture Notes

by

Andrew J. Buck

Professor of Economics

Temple University

These lectures are for use in a 2 semester econometrics sequence at the graduate level. Feel free to download any sections you wish, but please give attribution when you do so. Some sections are nearly .5Mb, including the *.gif files which contain equations and diagrams.

Some of the links are straight HTML.  Some links have MathView documents embedded in them.  To see these you need to get the viewer from www.livemath.com.  Some links take you to a MathCAD worksheet.  For the MathCAD worksheets you can use the viewer available from www.mathsoft.com.

For a fine collection of econometrics links visit Erasmus University.

Topics in the Notes

• A Review of Descriptive Statistics and Probability; An Interactive Venn Diagram in MathView.  The same Venn Diagram in MathCAD
  • Descriptive Statistics
    An interactive look at the center of mass
  • Probability Part 1
  • Probability Part 2
  • Probability Part 3
• Random Variables and Distributions: Discrete and Continuous
  • Discrete Distributions
  • Continuous Distributions
  • What does the bivariate normal distribution look like? (MVI required)
• Sampling Distributions
  • Sampling
  • Order Statistics
• Probability and Distribution Theory: Solutions to Econometric Analysis, Chapter 3, William Greene, 4th Edition
  • Questions 1 through 11
  • Question 12
  • Questions 14 through 17
  • Questions 18 through 20
  • Questions 21 through 29
• Estimating Scalar Population Parameters: Methods and Properties
• Testing Hypotheses about the Mean and Variance
• An Introduction to Asymptotic Tests: Likelihood Ratio, Wald and Lagrange Multiplier
• Workouts in Hypothesis Testing
  • Small Sample t- and F-tests (pdf)or in MathCAD
  • Large Sample Tests: LR, Wald and LM
    • Bernoulli and Normal Examples (pdf) or in MathCAD
    • An Exponential Example (pdf) or in MathCAD
    • A Weibull Example (pdf) or in MathCAD
  • Summary
• A Review of Analysis of Variance A MathCAD example
• Simple Regression: Choosing an Estimator, its Properties, Goodness of Fit, and Hypothesis Testing
  • Ordinary Least Squares Projections: Part 1 and Part 2.
  • An Interactive Simple Regression Notebook. MathView Internet required.
• Multiple Regression: More of What You Saw in the Previous Section
  • An Application of Multiple Regression (MVI required)
  • An Example using MathCAD
• Generalized Least Squares: The Aitken Estimator, Heteroscedasticity and Autocorrelation
  • An Interactive Look at Heteroscedasticity
• Another Pass at Hypothesis Testing in Multiple Regression: The t- and F-statistics; the Wald, Lagrange Multiplier, and Likelihood Ratio Tests; Decision Theoretic Selection Rules
• Topics in Multiple Regression
  • Measurement Error
  • Missing Data
  • Multicollinearity
  • Specification Problems: Omitted Variables, Linearity, Dummy Variables and Splines, and Non-nested Hypotheses
    • An Interactive Look at Dummy Variables and Splines
    • An Interactive Look at Various Functional Forms
• Autoregressive Conditional Heteroscedasticity (ARCH) or get the Self-extracting download
• Zellner's Seemingly Unrelated Regression and an illustration of the efficiency gain.
• An Introduction and lecture notes on Random Effects Models with a REM Example: Choosing Between OLS, LSDV and REM
  • REM Example: Individual Effects Plot
  • REM Example: Time Effects Plot
  • REM Example: Random Effects Plot
  • REM Example: Summary of Specification Tests
• Simultaneous Equations Models
  • Identification
    • A Three Dimensional View of the Identification Problem
    • A Two Dimensional View of Identification and the Observed Data
    • Consistency and LS Over-estimation
  • System Estimation and Specification
  • A Numerical Example

  To put to work what you have learned here you might want to visit the Fair Model
  Another good site is the Cambridge University Small U.K. Model Distributed Lags

• An Introduction to Time Series
  • Playing with stationarity
  • MathCAD provides and easy way to simulate the Autoregressive, the Moving Average and ARMA models discussed in the lecture notes.
• Stationarity: Testing for Unit Roots
• An Introduction to Vector Autoregressions
• An Introduction to Cointegration:   A money - GNP example (using RATS) and accompanying data.

Appendices

• Introduction to Linear Algebra
  • Interactive (MV) Problems in Linear Algebra
  • Interactive problems in linear algebra with MathCAD
• Some Matrix and Distribution Theorems
• An Introduction to Asymptotics

I'm always happy to hear from people interested in economics and Temple University. You can call me at 215-204-1985. You can send me a FAX at 215-204-8173. Or you can send email to buck@astro.temple.edu