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1 Nonlinear Regression Functions (SW Chapter 8). 2 The TestScore – STR relation looks linear (maybe)…

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Presentation on theme: "1 Nonlinear Regression Functions (SW Chapter 8). 2 The TestScore – STR relation looks linear (maybe)…"— Presentation transcript:

1 1 Nonlinear Regression Functions (SW Chapter 8)

2 2 The TestScore – STR relation looks linear (maybe)…

3 3 But the TestScore – Income relation looks nonlinear...

4 4 The general nonlinear population regression function

5 5

6 6 Nonlinear Functions of a Single Independent Variable (SW Section 8.2)

7 7 1. Polynomials in X

8 8 Example: TestScore vs. Income

9 9 Estimation in STATA

10 10 Interpretation. graph twoway scatter testscr avginc || connected yhat avginc, sort msymbol(none) || connected yhat2 avginc, sort msymbol(none) See Chapter 3 of Statistics with Stata, especially pages 79 & 118

11 11 Interpretation

12 12

13 13 Interpretation

14 14 Marginal effects in STATA

15 15 Estimation of a cubic in STATA

16 16 Plotting a cubic in STATA. graph twoway scatter testscr avginc || connected yhat2 avginc, sort msymbol(none) || connected yhat3 avginc, sort msymbol(T). reg testscr avginc avginc2 avginc3, rob. predict yhat3 (option xb assumed; fitted values)

17 17 Marginal effects in STATA

18 18 Marginal effects in STATA

19 19 Ramsey’s RESET Test: REgression Specification Error Test Consider the model (1) General test for misspecification of functional form If LSA #1 holds, then no non-linear function of the X’s should be significant when added to the model. Consider (2) Null hypothesis is that (1) is correctly specified How many powers of predicted values to include? Conduct F-test on powers of predicted values J.B. Ramsey (1969), Tests for Specification Error in Classical Linear Least Squares Regression Analysis. Journal of the Royal Statistical Society, Series B 31, 350–371

20 20 Ramsey’s RESET Test. reg test str avginc, r Linear regression Number of obs = 420 F( 2, 417) = 132.65 Prob > F = 0.0000 R-squared = 0.5115 Root MSE = 13.349 ------------------------------------------------------------------------------ | Robust testscr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- str | -.6487401.3533403 -1.84 0.067 -1.34329.04581 avginc | 1.839112.114733 16.03 0.000 1.613585 2.064639 _cons | 638.7292 7.301234 87.48 0.000 624.3773 653.081 ------------------------------------------------------------------------------. estat ovtest (can just type. ovtest) Ramsey RESET test using powers of the fitted values of testscr Ho: model has no omitted variables F(3, 414) = 18.36 Prob > F = 0.0000

21 21 Ramsey’s RESET Test. reg test str avginc avginc2, r Linear regression Number of obs = 420 F( 3, 416) = 286.55 Prob > F = 0.0000 R-squared = 0.5638 Root MSE = 12.629 ------------------------------------------------------------------------------ | Robust testscr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- str | -.9099512.3545374 -2.57 0.011 -1.606859 -.2130432 avginc | 3.881859.2709564 14.33 0.000 3.349245 4.414474 avginc2 | -.044157.0049606 -8.90 0.000 -.053908 -.034406 _cons | 625.2308 7.087793 88.21 0.000 611.2984 639.1631 ------------------------------------------------------------------------------. estat ovtest Ramsey RESET test using powers of the fitted values of testscr Ho: model has no omitted variables F(3, 413) = 2.48 Prob > F = 0.0605

22 22 Ramsey’s RESET Test. reg test str avginc avginc2 avginc3, r Linear regression Number of obs = 420 F( 4, 415) = 207.23 Prob > F = 0.0000 R-squared = 0.5663 Root MSE = 12.608 ------------------------------------------------------------------------------ | Robust testscr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- str | -.9277523.3562919 -2.60 0.010 -1.628114 -.2273905 avginc | 5.124736.7045403 7.27 0.000 3.739824 6.509649 avginc2 | -.1011073.0287052 -3.52 0.000 -.157533 -.0446815 avginc3 |.0007293.0003414 2.14 0.033.0000582.0014003 _cons | 617.8974 7.926373 77.95 0.000 602.3165 633.4782 ------------------------------------------------------------------------------. estat ovtest Ramsey RESET test using powers of the fitted values of testscr Ho: model has no omitted variables F(3, 412) = 1.79 Prob > F = 0.1490

23 23 Ramsey’s RESET Test. reg test str el_pct meal_pct, r Linear regression Number of obs = 420 F( 3, 416) = 453.48 Prob > F = 0.0000 R-squared = 0.7745 Root MSE = 9.0801 ------------------------------------------------------------------------------ | Robust testscr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- str | -.9983092.2700799 -3.70 0.000 -1.529201 -.4674178 el_pct | -.1215733.0328317 -3.70 0.000 -.18611 -.0570366 meal_pct | -.5473456.0241072 -22.70 0.000 -.5947328 -.4999583 _cons | 700.15 5.56845 125.74 0.000 689.2042 711.0958 ------------------------------------------------------------------------------. estat ovtest Ramsey RESET test using powers of the fitted values of testscr Ho: model has no omitted variables F(3, 413) = 6.29 Prob > F = 0.0004

24 24 Ramsey’s RESET Test. reg test str el_pct meal_pct avginc, r Linear regression Number of obs = 420 F( 4, 415) = 467.42 Prob > F = 0.0000 R-squared = 0.8053 Root MSE = 8.4477 ------------------------------------------------------------------------------ | Robust testscr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- str | -.5603892.2550641 -2.20 0.029 -1.061768 -.0590105 el_pct | -.1943282.0332445 -5.85 0.000 -.2596768 -.1289795 meal_pct | -.3963661.0302302 -13.11 0.000 -.4557895 -.3369427 avginc |.674984.0837161 8.06 0.000.5104236.8395444 _cons | 675.6082 6.201865 108.94 0.000 663.4172 687.7992 ------------------------------------------------------------------------------. estat ovtest Ramsey RESET test using powers of the fitted values of testscr Ho: model has no omitted variables F(3, 412) = 0.47 Prob > F = 0.7014

25 25 Ramsey’s RESET Test: replicated. predict yh (option xb assumed; fitted values). sum yh Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- yh | 420 654.1565 17.09817 614.9183 702.8387. gen yhz = (yh-r(mean))/r(sd). sum yh* Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- yh | 420 654.1565 17.09817 614.9183 702.8387 yhz | 420 1.22e-09 1 -2.294882 2.847214. gen yhz2=yhz*yhz. gen yhz3=yhz^3. gen yhz4=yhz^4

26 26 Ramsey’s RESET Test: replicated. reg test str el meal avginc yhz2 yhz3 yhz4 Source | SS df MS Number of obs = 420 -------------+------------------------------ F( 7, 412) = 244.48 Model | 122595.145 7 17513.5921 Prob > F = 0.0000 Residual | 29514.4488 412 71.6370116 R-squared = 0.8060 -------------+------------------------------ Adj R-squared = 0.8027 Total | 152109.594 419 363.030056 Root MSE = 8.4639 ------------------------------------------------------------------------------ testscr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- str | -.5500585.2336368 -2.35 0.019 -1.009327 -.0907896 el_pct | -.2170374.0407058 -5.33 0.000 -.2970544 -.1370204 meal_pct | -.400967.0289303 -13.86 0.000 -.4578364 -.3440976 avginc |.6476592.1505253 4.30 0.000.3517657.9435527 yhz2 |.7652051.915534 0.84 0.404 -1.034495 2.564906 yhz3 | -.0822669.3243362 -0.25 0.800 -.7198272.5552933 yhz4 | -.0650369.1767693 -0.37 0.713 -.412519.2824453 _cons | 675.8077 5.443279 124.15 0.000 665.1076 686.5077 ------------------------------------------------------------------------------. test yhz2 yhz3 yhz4 ( 1) yhz2 = 0 ( 2) yhz3 = 0 ( 3) yhz4 = 0 F( 3, 412) = 0.47 Prob > F = 0.7014

27 27 2. Logarithmic functions of Y and/or X

28 28 The 3 log specifications

29 29 I. Linear-log population regression function

30 30 Linear-log case, continued

31 31 Example: TestScore vs. ln(Income)

32 32 Linear-log vs. Cubic models

33 33 II. Log-linear population regression function

34 34 Log-linear case, continued

35 35 III. Log-log population regression function

36 36 Log-log case, continued

37 37 Example: ln( TestScore) vs. ln( Income)

38 38 Example: ln( TestScore) vs. ln( Income), ctd.

39 39 The log-linear and log-log specifications:

40 40

41 41

42 42

43 43

44 44

45 45 Other nonlinear functions (and nonlinear least squares) (SW App. 8.1)

46 46 Negative exponential growth

47 47 Nonlinear Least Squares

48 48

49 49

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