Econometrics

Applying mathematical and statistical practices to economics, econometrics enables economists to test theoretical hypotheses with real world data. This Econometrics e-book is available as a free download. It provides simple explanations of key concepts in the field, with numerous examples and clear statistical tables for reference. You can download the book for free via the link below. 1. Basics of probability and statistics 1.1 Random variables and probability distributions 1.1.1 Properties of probabilities 1.1.2 The probability function – the discrete case 1.1.3 The cumulative probability function – the discrete case 1.1.4 The probability function – the continuous case 1.1.5 The cumulative probability function – the continuous case 1.2 The multivariate probability distribution function 1.3 Characteristics of probability distributions 1.3.1 Measures of central tendency 1.3.2 Measures of dispersion 1.3.3 Measures of linear relationship 1.3.4 Skewness and kurtosis 2. Basic probability distributions in econometrics 2.1 The normal distribution 2.2 The t-distribution 2.3 The Chi-square distribution 2.4 The F-distribution 3. The simple regression model 3.1 The population regression model 3.1.1 The economic model 3.1.2 The econometric model 3.1.3 The assumptions of the simple regression model 3.2 Estimation of population parameters 3.2.1 The method of ordinary least squares 3.2.2 Properties of the least squares estimator 4. Statistical inference 4.1 Hypothesis testing 4.2 Confidence interval 4.2.1 P-value in hypothesis testing 4.3 Type I and type II errors 4.4 The best linear predictor 5. Model measures 5.1 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 model 6.1 Partial marginal effects 6.2 Estimation of partial regression coefficients 6.3 The joint hypothesis test 6.3.1 Testing a subset of coefficients 6.3.2 Testing the regression equation 7. Specification 7.1 Choosing the functional form 7.1.1 The linear specification 7.1.2 The log-linear specification 7.1.3 The linear-log specification 7.1.4 The log-log specification 7.2 Omission of a relevant variable 7.3 Inclusion of an irrelevant variable 7.4 Measurement errors 8. Dummy variables 8.1 Intercept dummy variables 8.2 Slope dummy variables 8.3 Qualitative variables with several categories 8.4 Piecewise linear regression 8.5 Test for structural differences 9. Heteroskedasticity and diagnostics 9.1 Consequences of using OLS 9.2 Detecting heteroskedasticity 9.2.1 Graphical methods 9.2.2 Statistical tests 9.3 Remedial measures 9.3.1 Heteroskedasticity-robust standard errors 10. Autocorrelation and diagnostics 10.1 Definition and the nature of autocorrelation 10.2 Consequences 10.3 Detection of autocorrelation 10.3.1 The Durbin Watson test 10.3.2 The Durbins h test statistic 10.3.3 The LM-test 10.4 Remedial measures 10.4.1 GLS with AR(1) 10.4.2 GLS with AR(2) 11. Multicollinearity and diagnostics 11.1 Consequences 11.2 Measuring the degree of multicollinearity 11.3 Remedial measures 12. Simultaneous equation models 12.1 Introduction 12.2 The structural and reduced form equation 12.3 Identification 12.3.1 The order condition of identification 12.3.2 The rank condition of identification 12.4 Estimation methods 12.4.1 Indirect Least Squares (ILS) 12.4.2 Two Stage Least Squares (2SLS) A. Statistical tables A1 Area below the standard normal distribution A2 Right tail critical values for the t-distribution A3 Right tail critical value of the Chi-Square distribution A4 Right tail critical for the F-distribution: 5 percent level Sisältö Basics of probability and statistics Random variables and probability distributions The multivariate probability distribution function Characteristics of probability distributions Basic probability distributions in econometrics The normal distribution The t-distribution The Chi-square distribution The F-distribution The simple regression model The population regression model Estimation of population parameters Statistical inference Hypothesis testing Confidence interval Type I and type II errors The best linear predictor Model measures The coefficient of determination (R2) The adjusted coefficient of determination (Adjusted R2) The analysis of variance table (ANOVA) The multiple regression model Partial marginal effects Estimation of partial regression coefficients The joint hypothesis test Specification Choosing the functional form Omission of a relevant variable Inclusion of an irrelevant variable Measurement errors Dummy variables Intercept dummy variables Slope dummy variables Qualitative variables with several categories Piecewise linear regression Test for structural differences Heteroskedasticity and diagnostics Consequences of using OLS Detecting heteroskedasticity Remedial measures Autocorrelation and diagnostics Definition and the nature of autocorrelation Consequences Detection of autocorrelation Remedial measures Multicollinearity and diagnostics Consequences Measuring the degree of multicollinearity Remedial measures Simultaneous equation models Introduction The structural and reduced form equation Identification Estimation methods Statistical tables Area below the standard normal distribution Right tail critical values for the t-distribution Right tail critical value of the Chi-Square distribution Right tail critical for the F-distribution: 5 percent level Obsah Basics of probability and statistics Random variables and probability distributions The multivariate probability distribution function Characteristics of probability distributions Basic probability distributions in econometrics The normal distribution The t-distribution The Chi-square distribution The F-distribution The simple regression model The population regression model Estimation of population parameters Statistical inference Hypothesis testing Confidence interval Type I and type II errors The best linear predictor Model measures The coefficient of determination (R2) The adjusted coefficient of determination (Adjusted R2) The analysis of variance table (ANOVA) The multiple regression model Partial marginal effects Estimation of partial regression coefficients The joint hypothesis test Specification Choosing the functional form Omission of a relevant variable Inclusion of an irrelevant variable Measurement errors Dummy variables Intercept dummy variables Slope dummy variables Qualitative variables with several categories Piecewise linear regression Test for structural differences Heteroskedasticity and diagnostics Consequences of using OLS Detecting heteroskedasticity Remedial measures Autocorrelation and diagnostics Definition and the nature of autocorrelation Consequences Detection of autocorrelation Remedial measures Multicollinearity and diagnostics Consequences Measuring the degree of multicollinearity Remedial measures Simultaneous equation models Introduction The structural and reduced form equation Identification Estimation methods Statistical tables Area below the standard normal distribution Right tail critical values for the t-distribution Right tail critical value of the Chi-Square distribution Right tail critical for the F-distribution: 5 percent level