1. Part 6: Multiple Regression 6-1/35 Regression Models Professor William Greene Stern School of Business IOMS Department Department of Economics

2. Part 6: Multiple Regression 6-2/35 Regression and Forecasting Models Part 6 – Multiple Regression

3. Part 6: Multiple Regression 6-3/35

4. Part 6: Multiple Regression 6-4/35

5. Part 6: Multiple Regression 6-5/35

6. Part 6: Multiple Regression 6-6/35

7. Part 6: Multiple Regression 6-7/35

8. Part 6: Multiple Regression 6-8/35

9. Part 6: Multiple Regression 6-9/35

10. Part 6: Multiple Regression 6-10/35

11. Part 6: Multiple Regression 6-11/35

12. Part 6: Multiple Regression 6-12/35

13. Part 6: Multiple Regression 6-13/35 Multiple Regression Agenda The concept of multiple regression Computing the regression equation Multiple regression model Using the multiple regression model Building the multiple regression model Regression diagnostics and inference

14. Part 6: Multiple Regression 6-14/35 Concept of Multiple Regression Different conditional means Application: Monets signature Holding things constant Application: Price and income effects Application: Age and education Sales promotion: Price and competitors The general idea of multiple regression

15. Part 6: Multiple Regression 6-15/35 Monet in Large and Small Log of $price = a + b log surface area + e Logs of Sale prices of 328 signed Monet paintings The residuals do not show any obvious patterns that seem inconsistent with the assumptions of the model.

16. Part 6: Multiple Regression 6-16/35 How much for the signature? The sample also contains 102 unsigned paintings Average Sale Price Signed $3,364,248 Not signed $1,832,712 Average price of a signed Monet is almost twice that of an unsigned one.

17. Part 6: Multiple Regression 6-17/35 Can we separate the two effects? Average Prices Small Large Unsigned 346,845 5,795,000 Signed 689,422 5,556,490 What do the data suggest? (1) The size effect is huge (2) The signature effect is confined to the small paintings.

18. Part 6: Multiple Regression 6-18/35 Thought experiments: Ceteris paribus Monets of the same size, some signed and some not, and compare prices. This is the signature effect. Consider signed Monets and compare large ones to small ones. Likewise for unsigned Monets. This is the size effect.

19. Part 6: Multiple Regression 6-19/35 A Multiple Regression Ln Price = b 0 + b1 ln Area + b2 (0 if unsigned, 1 if signed) + e b2

20. Part 6: Multiple Regression 6-20/35 Monet Multiple Regression Regression Analysis: ln (US$) versus ln (SurfaceArea), Signed The regression equation is ln (US$) = 4.12 + 1.35 ln (SurfaceArea) + 1.26 Signed Predictor Coef SE Coef T P Constant 4.1222 0.5585 7.38 0.000 ln (SurfaceArea) 1.3458 0.08151 16.51 0.000 Signed 1.2618 0.1249 10.11 0.000 S = 0.992509 R-Sq = 46.2% R-Sq(adj) = 46.0% Interpretation (to be explored as we develop the topic): (1) Elasticity of price with respect to surface area is 1.3458 – very large (2) The signature multiplies the price by exp(1.2618) (about 3.5), for any given size.

21. Part 6: Multiple Regression 6-21/35 Ceteris Paribus in Theory Demand for gasoline: G = f(price,income) Demand (price) elasticity: e P = %change in G given %change in P holding income constant. How do you do that in the real world? The percentage changes How to change price and hold income constant?

22. Part 6: Multiple Regression 6-22/35 The Real World Data

23. Part 6: Multiple Regression 6-23/35 U.S. Gasoline Market, 1953-2004

24. Part 6: Multiple Regression 6-24/35 Shouldnt Demand Curves Slope Downward?

25. Part 6: Multiple Regression 6-25/35 A Thought Experiment The main driver of gasoline consumption is income not price Income is growing over time. We are not holding income constant when we change price! How do we do that?

26. Part 6: Multiple Regression 6-26/35 How to Hold Income Constant? Multiple Regression Using Price and Income Regression Analysis: G versus GasPrice, Income The regression equation is G = 0.134 - 0.00163 GasPrice + 0.000026 Income Predictor Coef SE Coef T P Constant 0.13449 0.02081 6.46 0.000 GasPrice -0.0016281 0.0004152 -3.92 0.000 Income 0.00002634 0.00000231 11.43 0.000 It looks like the theory works.

27. Part 6: Multiple Regression 6-27/35 Application: WHO WHO data on 191 countries in 1995-1999. Analysis of Disability Adjusted Life Expectancy = DALE EDUC = average years of education PCHexp = Per capita health expenditure DALE = α + β 1 EDUC + β 2 HealthExp + ε

28. Part 6: Multiple Regression 6-28/35 The (Famous) WHO Data

29. Part 6: Multiple Regression 6-29/35

30. Part 6: Multiple Regression 6-30/35 Specify the Variables in the Model

31. Part 6: Multiple Regression 6-31/35

32. Part 6: Multiple Regression 6-32/35 Graphs

33. Part 6: Multiple Regression 6-33/35 Regression Results

34. Part 6: Multiple Regression 6-34/35 Practical Model Building Understanding the regression: The left out variable problem Using different kinds of variables Dummy variables Logs Time trend Quadratic

35. Part 6: Multiple Regression 6-35/35 A Fundamental Result What happens when you leave a crucial variable out of your model? Regression Analysis: g versus GasPrice (no income) The regression equation is g = 3.50 + 0.0280 GasPrice Predictor Coef SE Coef T P Constant 3.4963 0.1678 20.84 0.000 GasPrice 0.028034 0.002809 9.98 0.000 Regression Analysis: G versus GasPrice, Income The regression equation is G = 0.134 - 0.00163 GasPrice + 0.000026 Income Predictor Coef SE Coef T P Constant 0.13449 0.02081 6.46 0.000 GasPrice -0.0016281 0.0004152 -3.92 0.000 Income 0.00002634 0.00000231 11.43 0.000

36. Part 6: Multiple Regression 6-36/35 An Elaborate Multiple Loglinear Regression Model

37. Part 6: Multiple Regression 6-37/35 A Conspiracy Theory for Art Sales at Auction Sothebys and Christies, 1995 to about 2000 conspired on commission rates.

38. Part 6: Multiple Regression 6-38/35 If the Theory is Correct… Sold from 1995 to 2000 Sold before 1995 or after 2000

39. Part 6: Multiple Regression 6-39/35 Evidence The statistical evidence seems to be consistent with the theory.

40. Part 6: Multiple Regression 6-40/35 A Production Function Multiple Regression Model Sales of (Cameras/Videos/Warranties) = f(Floor Space, Staff)

41. Part 6: Multiple Regression 6-41/35 Production Function for Videos How should I interpret the negative coefficient on logFloor?

42. Part 6: Multiple Regression 6-42/35 An Application to Credit Modeling

43. Part 6: Multiple Regression 6-43/35 Age and Education Effects on Income

44. Part 6: Multiple Regression 6-44/35 A Multiple Regression +----------------------------------------------------+ | LHS=HHNINC Mean =.3520836 | | Standard deviation =.1769083 | | Model size Parameters = 3 | | Degrees of freedom = 27323 | | Residuals Sum of squares = 794.9667 | | Standard error of e =.1705730 | | Fit R-squared =.07040754 | +----------------------------------------------------+ +--------+--------------+--+--------+ |Variable| Coefficient | Mean of X| +--------+--------------+-----------+ Constant| -.39266196 AGE |.02458140 43.5256898 EDUC |.01994416 11.3206310 +--------+--------------+-----------+

45. Part 6: Multiple Regression 6-45/35 Education and Age Effects on Income Effect on log Income of 8 more years of education