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