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Econometrics - Thomas Andren

Year 2007



1. Basics of probability and statistics1.1. Random variables and probability distributions1.1.1. Properties of probabilities1.1.2. The probability function - the discrete case1.1.3. The cumulative probability function - the discrete case1.1.4. The probability function - the continuous case1.1.5. The cumulative probability function - the continuous case1.2. The multivariate probability distribution function1.3. Characteristics of probability distributions1.3.1. Measures of central tendency1.3.2. Measures of dispersion1.3.3. Measures of linear relationship1.3.4. Skewness and kurtosis2. Basic probability distributions in econometrics2.1. The normal distribution2.2. The t-distribution2.3. The Chi-square distribution2.4. The F-distribution3. The simple regression model3.1. The population regression model3.1.1. The economic model3.1.2. The econometric model3.1.3. The assumptions of the simple regression model3.2. Estimation of population parameters3.2.1. The method of ordinary least squares3.2.2. Properties of the least squares estimator4. Statistical inference4.1. Hypothesis testing4.2. Confidence interval4.2.1. P-value in hypothesis testing4.3. Type I and type II errors4.4. The best linear predictor5. Model Measures5.1. The coefficient of determination (R2)5.2. The adjusted coefficient of determination (Adjusted R2)5.3. The analysis of variance table (ANOVA)6. The multiple regression model6.1. Partial marginal effects6.2. Estimation of partial regression coefficients6.3. The joint hypothesis test6.3.1. Testing a subset of coefficients6.3.2. Testing the regression equation7. Specification7.1. Choosing the functional form7.1.1. The linear specification7.1.2. The log-linear specification7.1.3. The linear-log specification7.1.4. The log-log specification7.2. Omission of a relevant variable7.3. Inclusion of an irrelevant variable7.4. Measurement errors8. Dummy variables8.1. Intercept dummy variables8.2. Slope dummy variables8.2.1. A model will intercept and slope dummy variable8.3. Qualitative variables with several categories8.4. Piecewise linear regression8.5. Test for structural differences9. Heteroskedasticity and diagnostics9.1. Consequences of using OLS9.2. Detecting heteroskedasticity9.2.1. Graphical methods9.2.2. Statistical tests9.3. Remedial measures9.3.1. Heteroskedasticity-robust standard errors10. Autocorrelation and diagnostics10.1. Definition and the nature of autocorrelation10.2. Consequences10.3. Detection of autocorrelation10.3.1. The Durbin Watson test10.3.2. The Durbins h test statistic10.3.3. The LM-test10.4. Remedial measures10.4.1. GLS when AR(1)10.4.2. GLS when AR(2)11. Multicollinearity and diagnostics11.1. Consequences11.2. Measuring the degree of multicollinearity11.3. Remedial measures12. Simultaneous equation models12.1. Introduction12.2. The structural and reduced form equation12.3. Identification12.3.1. The order condition of identification12.3.2. The rank condition of identification12.4. Estimation methods12.4.1. Indirect Least Squares (ILS)12.4.2. Two Stage Least Squares (2SLS)
 
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