source:Jounal of Economtrics. 17.10.2020
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source:google trend
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The title of the book | Author |
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• Introductory econometrics: A modern approach. Nelson Education 2015. | Jeffrey M. Wooldridge |
• Introduction to Bayesian Econometrics 2nd Edition | Edward Greenberg |
• Econometrics by Example 2nd Edition | Damodar Gujarati |
• Introduction to Econometrics | Christopher Dougherty |
• Econometric Analysis of Panel Data 5th Edition | Badi H. Baltagi |
• Panel Data Econometrics | Donggy Sul |
• A Primer in Econometric Theory | John Stachurski |
• Econometrics For Dummies | Roberto Pedace |
• Time Series Econometrics | John D.Levendis |
• Principles of Econometrics, 5th Edition | R. Carter Hill, William E. Griffths, Guay C.Lim |
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Softwares | Web-pages |
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• R and RStudio | Go to the link_1,Go to the link_2 |
• Python | Go to the link |
• SAS | Go to the link |
• SPSS | Go to the link |
• MATLAB | Go to the link |
• Eviews | Go to the link |
• Stata | Go to the link |
• Excel | Go to the link |
• GAUSS | Go to the link |
• OriginLab | Go to the link |
• Prism | Go to the link |
• Minitab | Go to the link |
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Options - Analysis ToolPak - Check Analysis ToolPak
After activating Analysis ToolPak, we enter Data - Analysis - Regression
Fill in the range of independent Y and dependent X variables. Pressing the Enter button will bring up the resulting field. As shown in the photo.
• Multiple R | Multiple correlation coefficient |
• R Square | Determination Rate |
• Adjusted R Square | Corrected Determination Rate |
• Standard Error | Standard Error |
• Observations | Observation |
• ANOVA | Analysis of variance |
• Regression | - |
• Residual | - |
• Total | - |
• df | degrees of freedom |
• SS | sum of squares |
• MSS | mean square |
• F | Fisher Statistics - MSregression / MSresidual |
• Significance F | Significance of Regression |
• Intercept | Free Ratio |
• Coefficients | Regression Ratios |
• t Stat | Student Statistics |
• P-value | Probability that regression coefficients will match theoretical coefficients |
• Lower 95% | Lower limit of regression coefficients |
• Upper 95% | Upper limit of regression coefficients |
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assumptions | i = 1,2, . . . , n and j = 1,2,. . . , m |
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1. The model is linear and the random member is independent of the explanatory variables | Y = ꞵ0 + ꞵ1X1 + ꞵ2X2 + . . . + ꞵmXm |
2. the mean of random member equal to zero | E(ui) = 0 |
3. Independent variables and random member are uncorrelated | cov(ui,xj) = 0 |
4. Random member variation matches theoretical variation | D(ui) = 𝛔2(u) |
5. Random members are independent of each other | E(uqup) = 0 , q ≠ p |
6. The rank of the independent variable matrix corresponds to the number of its columns and is less than the number of observations | r(X) = m+1 < n |
7. Random member is normally distributed | Ui ~ N(0, 𝛔2(u)) |
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