Terms
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| Level of Confidence | 1) is the amount of Type I error implied by a test
2) 100 minus the level of significance.
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| Steps in the t test | 1) Set up null and alternative hypotheses.
2) Choose a level of significance and therefore a critical t value.
(d.f. = n-K-1)
3) Run regression and get an calculated t-score.
4) Compare t score with critical t value.
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| Gauss-Markov Theorem | Given Classical Assumptions I through VI, the OLS estimator of βk, is the minimum variance estimator, from among the set of all linear unbiased estimators of βk, for k = 0, 1, . . ., K.
That is, OLS is BLUE
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| BLUE | BEST
LINEAR
UNBIASED
ESTIMATOR
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| Robustness | a goal of econometrics; the degree to which the regression model can produce the similarly qualitative results across multiple data sets
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| Ordinary Least Squares | Regression technique that minimizes the sum of the square of the residuals
(minimize the sum of squared difference between actual data points and the estimated line)
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| Schoastic Error Term | added to regression equation to introduce all of the variation in the dependent variable that cannot be explained by the independent variables
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| Residual | The difference between the observed value and the estimated function value. Difference between observed Y and estimated regression line
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| Error Term | The deviation of the observed value from the (unobservable) true function value. Difference between observed Y and true regression line.
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| T-test | Tests for the significance of explanatory variables.
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| t-value | Coefficient/SE
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| Critical t-value is calculated based on three items: | 1) Degrees of freedom = ?
2) One-sided or two-sided test?
3) Significance level (generally 5% or 1%)?
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| Standard Error Depends on: | 1) Goodness of fit of equation (SEE, or Standard Error of the Equation)
2) Sample size – larger samples make SEE smaller
3) How widespread X’s are from their mean
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| TSS | Total sum of squares (TSS) = Explained sum of squares (ESS) + Residual sum of squares (RSS).
Squared variations of Y around its mean.
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| ESS | Amount of the squared deviation of from its mean that is explained by the estimated regression line.
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| RSS | Amount of the squared deviation of from its mean that is unexplained by the estimated regression line; OLS minimizes RSS
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