Preface ........................................................................................................................... 1
Chapter 1 - Basics of regression models .................................................................. 21.1. Types and applications of regression models. .............................................................................. 21.2. Basic elements of a single-equation linear regression model. ..................................................... 4Chapter 2 - Relevance of outlying and influential observations for regression analysis ..................................................................................................... 72.1. Nature and dangers of univariate and multivariate outlying observations. ................................ 72.2. Tools for detection of outlying observations. ............................................................................. 192.3. Recommended procedure for detection of outlying and influential observations. .................... 322.4. Dealing with detected outlying and influential observations. .................................................... 33Chapter 3 - Basic procedure for multiple regression model building ............. 353.1. Introduction. ............................................................................................................................... 353.2. Preliminary specification of the model. ...................................................................................... 353.3. Detection of potential outliers in the dataset. ........................................................................... 403.4. Selection of explanatory variables (from the set of candidates). ............................................... 483.5. Interpretation of the obtained regression' structural parameters. ............................................ 57Chapter 4 - Verification of multiple regression model ...................................... 604.1. Introduction. ............................................................................................................................... 604.2. Testing general statistical significance of the whole model: F test. ........................................... 614.3. Testing the normality of regression residuals' distribution. ....................................................... 634.4. Testing the autocorrelation of regression residuals. .................................................................. 724.5. Testing the heteroscedasticity of regression residuals. .............................................................. 874.6. Testing the symmetry of regression residuals. ........................................................................... 974.7. Testing the randomness of regression residuals. ..................................................................... 1064.8. Testing the specification of the model: Ramsey's RESET test. ................................................. 1154.9. Testing the multicollinearity of explanatory variables. ............................................................ 1214.10. What to do if the model is not correct? .................................................................................. 1254.11. Summary of verification of our model .................................................................................... 130Chapter 5 - Common adjustments to multiple regressions .............................. 1325.1. Dealing with qualitative factors by means of dummy variables. ............................................. 1325.2. Modeling seasonality by means of dummy variables. ............