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Week 4

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Week 4. Bivariate Regression, Least Squares and Hypothesis Testing. Lecture Outline. Method of Least Squares Assumptions Normality assumption Goodness of fit Confidence Intervals Tests of Significance alpha versus p. Recall . . .

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Week 4

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  1. Week 4 Bivariate Regression, Least Squares and Hypothesis Testing

  2. Lecture Outline • Method of Least Squares • Assumptions • Normality assumption • Goodness of fit • Confidence Intervals • Tests of Significance • alpha versus p IS 620 Spring 2006

  3. Recall . . . • Regression curve as “line connecting the mean values” of y for a given x • No necessary reason for such a construction to be a line • Need more information to define a function IS 620 Spring 2006

  4. Method of Least Squares • Goal:describe the functional relationship between y and x • Assume linearity (in the parameters) • What is the best line to explain the relationship? • Intuition:The line that is “closest” or “fits best” the data IS 620 Spring 2006

  5. “Best” line, n = 2 IS 620 Spring 2006

  6. “Best” line, n = 2 IS 620 Spring 2006

  7. “Best” line, n > 2 ? IS 620 Spring 2006

  8. “Best” line, n > 2 IS 620 Spring 2006

  9. u2 u3 u1 Least squares: intuition IS 620 Spring 2006

  10. Least squares, n > 2 IS 620 Spring 2006

  11. Why sum of squares? • Sum of residuals may be zero • Emphasize residuals that are far away from regression line • Better describes spread of residuals IS 620 Spring 2006

  12. Least-squares estimates Intercept Residuals Effect of x on y (slope) IS 620 Spring 2006

  13. Gauss-Markov Theorem • Least-squares method produces best, linear unbiased estimators (BLUE) • Also most efficient (minimum variance) • Provided classic assumptions obtain IS 620 Spring 2006

  14. Classical Assumptions • Focus on #3, #4, and #5 in Gujarati • Implications for estimators of violations • Skim over #1, #2, #6 through #10 IS 620 Spring 2006

  15. #3: Zero mean value of ui • Residuals are randomly distributed around the regression line • Expected value is zero for any given observation of x • NOTE: Equivalent to assuming the model is fully specified IS 620 Spring 2006

  16. #3: Zero mean value of ui IS 620 Spring 2006

  17. #3: Zero mean value of ui IS 620 Spring 2006

  18. #3: Zero mean value of ui IS 620 Spring 2006

  19. #3: Zero mean value of ui IS 620 Spring 2006

  20. #3: Zero mean value of ui IS 620 Spring 2006

  21. #3: Zero mean value of ui IS 620 Spring 2006

  22. #3: Zero mean value of ui IS 620 Spring 2006

  23. #3: Zero mean value of ui IS 620 Spring 2006

  24. Violation of #3 • Estimated betas will be • Unbiased but • Inconsistent • Inefficient • May arise from • Systematic measurement error • Nonlinear relationships (Phillips curve) IS 620 Spring 2006

  25. #4: Homoscedasticity • The variance of the residuals is the same for all observations, irrespective of the value of x • “Equal variance” • NOTE: #3 and #4 imply (see “Normality Assumption”) IS 620 Spring 2006

  26. #4: Homoscedasticity IS 620 Spring 2006

  27. #4: Homoscedasticity IS 620 Spring 2006

  28. #4: Homoscedasticity IS 620 Spring 2006

  29. #4: Homoscedasticity IS 620 Spring 2006

  30. #4: Homoscedasticity IS 620 Spring 2006

  31. Violation of #4 • Estimated betas will be • Unbiased • Consistent but • Inefficient • Arise from • Cross-sectional data IS 620 Spring 2006

  32. #5: No autocorrelation • The correlation between any two residuals is zero • Residual for xi is unrelated to xj IS 620 Spring 2006

  33. #5: No autocorrelation IS 620 Spring 2006

  34. #5: No autocorrelation IS 620 Spring 2006

  35. #5: No autocorrelation IS 620 Spring 2006

  36. #5: No autocorrelation IS 620 Spring 2006

  37. Violations of #5 • Estimated betas will be • Unbiased • Consistent • Inefficient • Arise from • Time-series data • Spatial correlation IS 620 Spring 2006

  38. Other Assumptions (1) • Assumption 6: zero covariance between xi and ui • Violations cause of heteroscedasticity • Hence violates #4 • Assumption 9: model correctly specified • Violations may violate #1 (linearity) • May also violate #3: omitted variables? IS 620 Spring 2006

  39. Other Assumptions (2) • #7: n must be greater than number of parameters to be estimated • Key in multivariate regression • King, Keohane and Verba’s (1996) critique of small n designs IS 620 Spring 2006

  40. Normality Assumption • Distribution of disturbance is unknown • Necessary for hypothesis testing of I.V.s • Estimates a function of ui • Assumption of normality is necessary for inference • Equivalent to assuming model is completely specified IS 620 Spring 2006

  41. Normality Assumption • Central Limit Theorem: M&Ms • Linear transformation of a normal variable itself is normal • Simple distribution (mu, sigma) • Small samples IS 620 Spring 2006

  42. Assumptions, Distilled • Linearity • DV is continuous, interval-level • Non-stochastic: No correlation between independent variables • Residuals are independently and identically distributed (iid) • Mean of zero • Constant variance IS 620 Spring 2006

  43. If so, . . . • Least-squares method produces BLUE estimators IS 620 Spring 2006

  44. Goodness of Fit • How “well” the least-squares regression line fits the observed data • Alternatively: how well the function describes the effect of x on y • How much of the observed variation in y have we explained? IS 620 Spring 2006

  45. Coefficient of determination • Commonly referred to as “r2” • Simply, the ratio of explained variation in y to the total variation in y IS 620 Spring 2006

  46. Components of variation explained total residual IS 620 Spring 2006

  47. Components of variation • TSS: total sum of squares • ESS: explained sum of squares • RSS: residual sum of squares IS 620 Spring 2006

  48. Hypothesis Testing • Confidence Intervals • Tests of significance • ANOVA • Alpha versus p-value IS 620 Spring 2006

  49. Confidence Intervals • Two components • Estimate • Expression of uncertainty • Interpretation: • Gujarati, p. 121: “The probability of constructing an interval that contains Beta is 1-alpha” • NOT: “The p that Beta is in the interval is 1-alpha” IS 620 Spring 2006

  50. C.I.s for regression • Depend upon our knowledge or assumption about the sampling distribution • Width of interval proportional to standard error of the estimators • Typically we assume • The t distribution for Betas • The chi-square distribution for variances • Due to unknown true standard error IS 620 Spring 2006

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