# Author lectures on econometrics

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## Product description

Author lectures on "Econometrics"

Contents
Foreword 3
Introduction
V.1.Opredelenie Econometrics 6
V.2.Znachenie econometrics economic 6
Chapter I. Basic aspects of econometric modeling
1.1. The concept of the model system 8
1.2. Adequacy of the model 8
1.3. Model type black box 9
1.4. The basic premise of the econometric analysis 10
1.5. Construction of a parametric regression model 11
1.6. Classification of econometric models. 13
1.6.1. The structure regression equations 13
1.6.2. By way of consideration of the dynamics 13
1.6.3. By type of relationship between. 14
1.6.4. According to the algorithm evaluation of the model parameters. 14
1.7. Data Types 14
1.7.1. Spatial data types 14
1.7.2. Time (dynamic) number 15
1.8. Stages of building an econometric model 15
Chapter II. Correlation analysis
2.1. The purpose of the correlation analysis 16
2.2. Numerical measures of correlation 16
2.2.1. Covariance. 16
2.2.2. Custom estimated coefficient of linear steam
correlation 16
2.2.3. Mathematical meaning of the coefficient of linear correlation pair 17
2.2.4. The statistical significance of the coefficient of linear correlation pair 18
2.2.5. A geometric interpretation of the correlation coefficient 18
2.3. Checking the statistical significance of the correlation coefficient 19
2.4. Multiple correlation analysis 19
2.4.1. The correlation matrix 19
2.4.2. Selective linear multiple correlation coefficient 20
2.4.3. Partial correlation coefficient 20
2.4.4. The coefficient of determination 21
2.4.5. Valuing the multiple rate
determination 21
2.4.6. The index correlations in the nonlinear coupling of two random
values \u200b\u200b21
2.4.7. The index of multiple correlation 22
2.5. Rank correlation coefficient 23
Chapter III. Multiple regression analysis
3.1. Statement of the problem 24
3.2. The method of least squares (OLS) in scalar form 24
3.3. The matrix form of the least squares method. 25
3.3.1.Uravnenie observation matrix form 25
3.3.2.Normalnye regression equation and the formula for the parameters of the equation 26
3.4. BACKGROUND method of least squares 27
3.5. Properties estimates obtained by the method of least squares 28
3.6. Assessing the adequacy of the regression equation (testing hypotheses about the prerequisites
least squares method) 29
3.6.1.Gipoteza of near zero mathematical expectation
29 residues
3.6.2. The hypothesis about the statistical significance of the coefficients
Regression bj 29
3.6.3. The hypothesis about the statistical significance of the whole equation
regression as a whole 30
3.6.4. Assessment of the quality of the regression equation 30
3.6.5. The adjusted coefficient of determination 32
3.6.6. Testing the hypothesis of a purely random nature of residues 32
3.6.7. Testing the hypothesis of normal distribution
residues 35
3.7. Spot Forecast and evaluation of the forecast confidence intervals 35
3.8. Error estimation for the regression equation 37
3.9. The coefficient of elasticity and coefficient beta
delta-factor linear regression equation 37
Chapter IV. Time series
4.1. The concept of time series classification. 39
4.2. Component analysis of time series 39
4.3. Stochastic process 39
4.4. The concept of correlation coefficient in the time series, the autocorrelation function (ACF) 40
4.5. Sample score autocorrelation coefficient 41
4.6. Private autocorrelation coefficient. 41
4.7. Smoothing time series 42
4.8. AR model. 42
4.9. Autoregressive moving average model. 43
4.10. The difference equations with variable lags 43
4.11. Evaluation of the regression coefficients. 44
4.12. Prediction of the difference AR model 44
Chapter V. Some of the practical construction of regression models
5.1.Problema specification variables. Multicollinearity 45
5.2.Sposoby eliminate mu

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