Eviews

Applied Econometrics with Eviews

Jul 1 - Jul 26, 2002

Lecture notes

• Lecture 1: Introduction

  Theories and Models; Random Variables.

• Lecture 2: Multivariate Distributions

  Covariances and Correlations

• Lecture 3: Distribution Theory

  The Normal Distribution and Distributions Related to the Normal Distribution

• Lecture 4: Data Description

  Central Tendency and Dispersion; Frequency Distribution; Histogram

  Scatter Diagrams, Sample Correlations

  Estimation and Sampling Distributions

• Lecture 5: Concepts of Estimation

  Sampling Distributions of Sample Mean and Sample Variance

  Confidence Intervals

• Lecture 6: Hypothesis testing
  Hypotheses about the Population Mean and Variance
  Comparing Two Populations
  Lecture 7: Errors of Inference
  Type 1 and Type 2 Errors in Hypothesis Testing: Power of a Test
  Lecture 8: The Classical Normal Linear Regression Model

  The Classical Assumptions; The Least-Squares Principle

  Estimating the Elasticity of Labor Productivity as a Function of K/L Ratio

• Lecture 9: Simple Linear Regression

  Sampling Distributions of LS Estimators

  Gauss-Markov Theorem

  Coefficient of Determintation

• Lecture 10: Prediction in a Simple Regression Model

  Unbiased Prediction

  Confidence Intervals for Predictions

• Lecture 11: Introduction to Multiple Regression

  The Least-Squares Principle

  The Two-Regressor Case

  Sampling Distributions and Hypothesis Testing

  Coefficient of Determination and Model Selection Criteria

• Lecture 12: Specification Analysis

  Exclusion of Relevant Variables and Omitted Variables Bias

  Inclusion of Irrelevant Variables and Inefficiency

  General-to-Simple Modeling Strategy and Wald Tests

• Lecture 13: Specification analysis
  Functional Form
  Multicollinearity

• Lecture 14: Residual Diagnostics

  Heteroskedasticity

  Weighted Least Squares and White's HCCM

• Lecture 15: Residual Diagnostics

  Autocorrelation

  Correlogram and Partial Correlogram

• Lecture 16: Panel Data

  Linear Models for Panel Data

  Error Covariance Matrix Specifications

• Lecture 17: Limited Dependent Variables
  The Linear Probability Model
  Logit and Probit Models

• Lecture 18: Limited Dependent Variables


Logit with Sample Proportions
Tobit Models