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Static Models of Continuous Variables

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Raimundo Soto - Catholic University of Chile

ERF Training on Advanced Panel Data Techniques Applied to Economic Modelling

29 -31 October, 2018
Cairo, Egypt

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Static Models of Continuous Variables

  1. 1. ERF Training Workshop Panel Data 2 Raimundo Soto Instituto de Economía, PUC-Chile
  2. 2. STATIC MODELS OF CONTINUOUS VARIABLES 2
  3. 3. MODEL STRUCTURE • Canonical Model 𝑦𝑖𝑡 = 𝛼𝑖𝑡 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 where • 𝑦𝑖𝑡 is the phenomenon of interest to be modelled, • 𝑥𝑖𝑡 represents all observed controls (regressors) • 𝛼𝑖𝑡 are the individual effects • 𝜀𝑖𝑡 is the non-systematic part (what we choose not to model) 3
  4. 4. DATA STRUCTURE • Let us stack the data in the it structure • 𝑦11 𝑦12 ⋮ 𝑦1𝑇 𝑦21 𝑦22 ⋮ 𝑦2𝑇 ⋮ ⋮ 𝑦 𝑁1 ⋮ 𝑦 𝑁𝑇 4
  5. 5. DATA STRUCTURE EXAMPLE 5
  6. 6. DATA STRUCTURE • Let us stack the data in the it structure • 𝑦11 𝑦12 ⋮ 𝑦1𝑇 𝑦21 𝑦22 ⋮ 𝑦2𝑇 ⋮ ⋮ 𝑦 𝑁1 ⋮ 𝑦 𝑁𝑇 = 𝛼11 𝛼12 ⋮ 𝛼1𝑇 𝛼21 𝛼22 ⋮ 𝛼2𝑇 ⋮ ⋮ 𝛼 𝑁1 ⋮ 𝛼 𝑁𝑇 + 𝑤11 𝑤12 ⋮ 𝑤1𝑇 𝑤21 𝑤22 ⋮ 𝑤2𝑇 ⋮ ⋮ 𝑤 𝑁1 ⋮ 𝑤 𝑁𝑇 𝑣11 𝑣12 ⋮ 𝑣1𝑇 𝑣21 𝑣22 ⋮ 𝑣2𝑇 ⋮ ⋮ 𝑣 𝑁1 ⋮ 𝑣 𝑁𝑇 … 𝑧11 𝑧12 ⋮ 𝑧1𝑇 𝑧21 𝑧22 ⋮ 𝑧2𝑇 ⋮ ⋮ 𝑧 𝑁1 ⋮ 𝑧 𝑁𝑇 𝛽 + 𝜀11 𝜀12 ⋮ 𝜀1𝑇 𝜀21 𝜀22 ⋮ 𝜀2𝑇 ⋮ ⋮ 𝜀 𝑁1 ⋮ 𝜀 𝑁𝑇 6
  7. 7. DATA STRUCTURE • Let us stack the data in the it structure • 𝑦11 𝑦12 ⋮ 𝑦1𝑇 𝑦21 𝑦22 ⋮ 𝑦2𝑇 ⋮ ⋮ 𝑦 𝑁1 ⋮ 𝑦 𝑁𝑇 = 𝛼11 𝛼12 ⋮ 𝛼1𝑇 𝛼21 𝛼22 ⋮ 𝛼2𝑇 ⋮ ⋮ 𝛼 𝑁1 ⋮ 𝛼 𝑁𝑇 + 𝑤11 𝑤12 ⋮ 𝑤1𝑇 𝑤21 𝑤22 ⋮ 𝑤2𝑇 ⋮ ⋮ 𝑤 𝑁1 ⋮ 𝑤 𝑁𝑇 𝑣11 𝑣12 ⋮ 𝑣1𝑇 𝑣21 𝑣22 ⋮ 𝑣2𝑇 ⋮ ⋮ 𝑣 𝑁1 ⋮ 𝑣 𝑁𝑇 … 𝑧11 𝑧12 ⋮ 𝑧1𝑇 𝑧21 𝑧22 ⋮ 𝑧2𝑇 ⋮ ⋮ 𝑧 𝑁1 ⋮ 𝑧 𝑁𝑇 𝛽 + 𝜀11 𝜀12 ⋮ 𝜀1𝑇 𝜀21 𝜀22 ⋮ 𝜀2𝑇 ⋮ ⋮ 𝜀 𝑁1 ⋮ 𝜀 𝑁𝑇 = 𝛼 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 7
  8. 8. ESTIMATION IS IMPOSSIBLE • If we allow for 𝛼 𝑁𝑇 there will be 𝑁𝑇 constants (and, at most, NT observations), not enough degrees of freedom to estimate the parameters. • We will restrict ourselves to: – 𝛼𝑖 , i.e., N constants (that do not change in time) – 𝜆 𝑡 , i.e., T constants (that do not change by individual) 8
  9. 9. POOLED ESTIMATOR • Let us ignore all heterogeneity 𝑦𝑖𝑡 = 𝛼 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 • The OLS estimator is: 𝛽 = 𝑥𝑖𝑡 ′ 𝑥𝑖𝑡 −1 𝑥𝑖𝑡 ′ 𝑦𝑖𝑡 • The variance estimator is 𝑉𝑎𝑟 𝛽 = 𝜎𝜀 2 𝑥𝑖𝑡 ′ 𝑥𝑖𝑡 −1 = 𝜎𝜀 2 𝑉(𝑥𝑖𝑡) • Note the increase in precision (𝑁𝑥𝑇) 9
  10. 10. FIXED EFFECTS ESTIMATOR • Consider that the heterogeneity is only among individuals 𝑦𝑖𝑡 = 𝛼𝑖 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 • 𝛼𝑖 represents individual characteristics that are fixed • We could use binary (dummy) variables to represent fixed characteristics 10
  11. 11. FIXED EFFECTS ESTIMATOR 𝑦11 𝑦12 ⋮ 𝑦1𝑇 𝑦21 𝑦22 ⋮ 𝑦2𝑇 ⋮ ⋮ 𝑦 𝑁1 ⋮ 𝑦 𝑁𝑇 = 𝛼1 𝛼1 ⋮ 𝛼1 0 0 ⋮ 0 ⋮ ⋮ 0 ⋮ 0 0 0 ⋮ 0 𝛼2 𝛼2 ⋮ 0 ⋮ ⋮ 0 ⋮ 0 … 0 0 ⋮ 0 0 0 ⋮ 0 ⋮ ⋮ 𝛼 𝑁 ⋮ 𝛼 𝑁 + 𝑤11 𝑤12 ⋮ 𝑤1𝑇 𝑤21 𝑤22 ⋮ 𝑤2𝑇 ⋮ ⋮ 𝑤 𝑁1 ⋮ 𝑤 𝑁𝑇 𝑣11 𝑣12 ⋮ 𝑣1𝑇 𝑣21 𝑣22 ⋮ 𝑣2𝑇 ⋮ ⋮ 𝑣 𝑁1 ⋮ 𝑣 𝑁𝑇 … 𝑧11 𝑧12 ⋮ 𝑧1𝑇 𝑧21 𝑧22 ⋮ 𝑧2𝑇 ⋮ ⋮ 𝑧 𝑁1 ⋮ 𝑧 𝑁𝑇 𝛽 + 𝜀11 𝜀12 ⋮ 𝜀1𝑇 𝜀21 𝜀22 ⋮ 𝜀2𝑇 ⋮ ⋮ 𝜀 𝑁1 ⋮ 𝜀 𝑁𝑇 11
  12. 12. FIXED EFFECTS ESTIMATOR • 𝑦𝑖𝑡 = 𝛼𝐷 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 • Note: same slope, different intercept (constant) • All classic results on econometric estimation techniques hold: nature of the OLS estimator, optimality, goodness of fit, and asymptotic distributions of estimators and tests. • This estimator is called LSDV least squares dummy variables. 12
  13. 13. FIXED EFFECTS ESTIMATOR Group 2 Group 1 𝑥 𝑦 13
  14. 14. FIXED EFFECTS ESTIMATOR Group 2 Group 1 𝑥 𝑦 𝛼2 𝛼1 𝑦2 = 𝛼2 + 𝑥2β 𝑦1 = 𝛼1 + 𝑥1β 14
  15. 15. FIXED EFFECTS ESTIMATOR Group 2 Group 1 𝑥 𝑦 𝛼2 𝛼1 𝑦2 = 𝛼2 + 𝑥2β 𝑦1 = 𝛼1 + 𝑥1β 𝑦 = 𝛼 + 𝑥β 15
  16. 16. FIXED EFFECTS ESTIMATOR • Example: – Vial y Soto (2002) revise the opinion that “university selection tests (PSU) do not predict student performance (R) in their faculties, only secondary-school marks are important”. – When running the pooled regression: 𝑅𝑖𝑡 = 𝛼 + 𝛽𝑃𝑆𝑈𝑖𝑡 + 𝜇𝑖𝑡 The estimated 𝛽 is small, not significant or displays the “wrong” sign (negative). 16
  17. 17. PREDICTED EFFECT OF SELECTION TESTS ON STUDENTS’ PERFORMANCE Note: * significant at 10% size
  18. 18. PREDICTED EFFECT OF SELECTION TESTS ON STUDENTS’ PERFORMANCE Note: * significant at 10% size
  19. 19. POOLED ESTIMATOR 𝑃𝑆𝑈 𝑅 10 1 19 “Low-quality” faculties “High-quality” faculties
  20. 20. FIXED EFFECTS ESTIMATOR 𝑃𝑆𝑈 𝑅 10 1 20 “Low-quality” faculties “High-quality” faculties
  21. 21. POOLED VS FIXED EFFECTS ESTIMATORS 𝑃𝑆𝑈 𝑅 21 “Low-quality” faculties “High-quality” faculties
  22. 22. POOLED VS FIXED EFFECTS ESTIMATORS 𝑃𝑆𝑈 𝑅 22 “Low-quality” faculties “High-quality” faculties
  23. 23. FIXED EFFECTS ESTIMATOR • LSDV estimator is unfeasible if N is too large – HIECS has 24,000 households • Recall that constants in regressions only take away the means of the variables • It would be much simpler to eliminate the means of the variables and avoid specifying 24,000 dummy variables 23
  24. 24. FIXED EFFECTS ESTIMATOR 𝑦𝑖𝑡 = 𝛼𝑖 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 • Let us take expected value for each individual “i” in time: 𝐸𝑖 𝑦𝑖𝑡 = 𝐸𝑖 𝛼𝑖 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 𝑦𝑖 = 𝛼𝑖 + 𝑥𝑖 𝛽 • and subtract from the original model to eliminate 𝛼𝑖: 𝑦𝑖𝑡 − 𝑦𝑖 = 𝛼𝑖 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 − 𝛼𝑖 − 𝑥𝑖 𝛽 𝑦𝑖𝑡 − 𝑦𝑖 = 𝑥𝑖𝑡 − 𝑥𝑖 𝛽 + 𝜀𝑖𝑡 24
  25. 25. FIXED EFFECTS ESTIMATOR 𝑦𝑖𝑡 − 𝑦𝑖 = 𝑥𝑖𝑡 − 𝑥𝑖 𝛽 + 𝜀𝑖𝑡 • This is a very simple estimation, without the problems derived from dimensionality. • Obviously, we cannot estimate 𝛼𝑖, but they are easily recovered as: 𝛼𝑖 = 𝑦𝑖 − 𝑥𝑖 𝛽 25
  26. 26. FIXED EFFECTS ESTIMATOR Group 2 Group 1 𝑥 𝑦 𝛼2 𝛼1 26
  27. 27. FIXED EFFECTS ESTIMATOR Group 2 𝑥 𝑦 Group 1 w/o means 27
  28. 28. FIXED EFFECTS ESTIMATOR Groups 1 & 2 w/o means 𝑥 𝑦 28
  29. 29. FIXED EFFECTS ESTIMATOR 𝑥 𝑦 29 Groups 1 & 2 w/o means
  30. 30. FIXED EFFECTS ESTIMATOR 𝑦𝑖𝑡 − 𝑦𝑖 = 𝑥𝑖𝑡 − 𝑥𝑖 𝛽 + 𝜀𝑖𝑡 • This estimator uses only information within each group and it is therefore called within-groups estimator • Let us obtain certain useful “sums” in order to better understand the nature of estimators. 30
  31. 31. POOLED ESTIMATOR 𝑆 𝑝 𝑥𝑥 = 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥 ′ 𝑥𝑖𝑡 − 𝑥 𝑆 𝑝 𝑥𝑦 = 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥 ′ 𝑦𝑖𝑡 − 𝑦 𝛽 𝑝 = 𝑆 𝑝 𝑥𝑦 𝑆 𝑝 𝑥𝑥 31
  32. 32. WITHIN-GROUPS ESTIMATOR 𝑆 𝑤 𝑥𝑥 = 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥𝑖 ′ 𝑥𝑖𝑡 − 𝑥𝑖 𝑆 𝑤 𝑥𝑦 = 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥𝑖 ′ 𝑦𝑖𝑡 − 𝑦𝑖 𝛽 𝑤 = 𝑆 𝑤 𝑥𝑦 𝑆 𝑤 𝑥𝑥 32
  33. 33. WITHIN-GROUPS ESTIMATOR • From the pooled estimator 𝑆 𝑝 𝑥𝑥 = 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥 ′ 𝑥𝑖𝑡 − 𝑥 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥𝑖 + 𝑥𝑖 − 𝑥 ′ 𝑥𝑖𝑡 − 𝑥𝑖 + 𝑥𝑖 − 𝑥 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥𝑖 + 𝑥𝑖 − 𝑥 ′ 𝑥𝑖𝑡 − 𝑥𝑖 + 𝑥𝑖 − 𝑥 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥𝑖 ′ 𝑥𝑖 − 𝑥 + 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖 − 𝑥 ′ 𝑥𝑖 − 𝑥 𝑆 𝑝 𝑥𝑥 = 𝑆 𝑤 𝑥𝑥 + 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖 − 𝑥 ′ 𝑥𝑖 − 𝑥 33
  34. 34. WITHIN-GROUPS ESTIMATOR • Thus 𝑆 𝑝 𝑥𝑥 = 𝑆 𝑤 𝑥𝑥 + 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖 − 𝑥 ′ 𝑥𝑖 − 𝑥 𝑆 𝑤 𝑥𝑥 = 𝑆 𝑝 𝑥𝑥 − 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖 − 𝑥 ′ 𝑥𝑖 − 𝑥 • Double sums are either zero or positive (these are squares) hence 𝑆 𝑤 𝑥𝑥 ≤ 𝑆 𝑝 𝑥𝑥 34
  35. 35. WITHIN-GROUPS ESTIMATOR • The variance of the within-groups estimator is 𝑉𝑎𝑟 𝛽 𝑤 = 𝜎2 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖𝑡 − 𝑥𝑖 ′ 𝑥𝑖 − 𝑥 + 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖 − 𝑥 ′ 𝑥𝑖 − 𝑥 𝑉𝑎𝑟 𝛽 𝑤 = 𝜎2 𝑆 𝑝 𝑥𝑥 − 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖 − 𝑥 ′ 𝑥𝑖 − 𝑥 • Therefore, this variance is larger than that of the pooled estimator • The within-groups estimator is less precise than the pooled estimator 35
  36. 36. LET US SEE THIS IN PRACTICE • Open Stata • Open file ERF_Continuous Static.do – Declare Panel Data and Variables • xtset – Panel Data Analysis: commands xt • xtdes • xtsum – Panel Data Regression • xtreg • Let us check the estimation results 36
  37. 37. F test that all u_i=0: F(162, 5270) = 98.59 Prob > F = 0.0000 rho .95557807 (fraction of variance due to u_i) sigma_e .32607847 sigma_u 1.5123647 _cons -7.844568 .2477214 -31.67 0.000 -8.330205 -7.358932 l_popt .0731808 .0285325 2.56 0.010 .0172453 .1291163 l_infl2 -.0281481 .004758 -5.92 0.000 -.0374757 -.0188204 l_realgdp .3846652 .0153326 25.09 0.000 .354607 .4147234 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9200 Prob > F = 0.0000 F(3,5270) = 859.53 overall = 0.0036 max = 55 between = 0.0140 avg = 33.3 R-sq: within = 0.3285 Obs per group: min = 4 Group variable: idwbcode Number of groups = 163 Fixed-effects (within) regression Number of obs = 5436 _cons 3.315492 .0848041 39.10 0.000 3.149242 3.481742 l_popt .0014429 .0061381 0.24 0.814 -.0105903 .0134762 l_infl2 -.1650409 .0078825 -20.94 0.000 -.1804937 -.149588 l_realgdp -.0086728 .0036681 -2.36 0.018 -.0158636 -.0014819 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2454.44502 5435 .451599819 Root MSE = .64482 Adj R-squared = 0.0793 Residual 2258.61346 5432 .415797764 R-squared = 0.0798 Model 195.831563 3 65.2771875 Prob > F = 0.0000 F( 3, 5432) = 156.99 Source SS df MS Number of obs = 5436 37
  38. 38. F test that all u_i=0: F(162, 5270) = 98.59 Prob > F = 0.0000 rho .95557807 (fraction of variance due to u_i) sigma_e .32607847 sigma_u 1.5123647 _cons -7.844568 .2477214 -31.67 0.000 -8.330205 -7.358932 l_popt .0731808 .0285325 2.56 0.010 .0172453 .1291163 l_infl2 -.0281481 .004758 -5.92 0.000 -.0374757 -.0188204 l_realgdp .3846652 .0153326 25.09 0.000 .354607 .4147234 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9200 Prob > F = 0.0000 F(3,5270) = 859.53 overall = 0.0036 max = 55 between = 0.0140 avg = 33.3 R-sq: within = 0.3285 Obs per group: min = 4 Group variable: idwbcode Number of groups = 163 Fixed-effects (within) regression Number of obs = 5436 _cons 3.315492 .0848041 39.10 0.000 3.149242 3.481742 l_popt .0014429 .0061381 0.24 0.814 -.0105903 .0134762 l_infl2 -.1650409 .0078825 -20.94 0.000 -.1804937 -.149588 l_realgdp -.0086728 .0036681 -2.36 0.018 -.0158636 -.0014819 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2454.44502 5435 .451599819 Root MSE = .64482 Adj R-squared = 0.0793 Residual 2258.61346 5432 .415797764 R-squared = 0.0798 Model 195.831563 3 65.2771875 Prob > F = 0.0000 F( 3, 5432) = 156.99 Source SS df MS Number of obs = 5436 Total Observations 38
  39. 39. F test that all u_i=0: F(162, 5270) = 98.59 Prob > F = 0.0000 rho .95557807 (fraction of variance due to u_i) sigma_e .32607847 sigma_u 1.5123647 _cons -7.844568 .2477214 -31.67 0.000 -8.330205 -7.358932 l_popt .0731808 .0285325 2.56 0.010 .0172453 .1291163 l_infl2 -.0281481 .004758 -5.92 0.000 -.0374757 -.0188204 l_realgdp .3846652 .0153326 25.09 0.000 .354607 .4147234 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9200 Prob > F = 0.0000 F(3,5270) = 859.53 overall = 0.0036 max = 55 between = 0.0140 avg = 33.3 R-sq: within = 0.3285 Obs per group: min = 4 Group variable: idwbcode Number of groups = 163 Fixed-effects (within) regression Number of obs = 5436 _cons 3.315492 .0848041 39.10 0.000 3.149242 3.481742 l_popt .0014429 .0061381 0.24 0.814 -.0105903 .0134762 l_infl2 -.1650409 .0078825 -20.94 0.000 -.1804937 -.149588 l_realgdp -.0086728 .0036681 -2.36 0.018 -.0158636 -.0014819 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2454.44502 5435 .451599819 Root MSE = .64482 Adj R-squared = 0.0793 Residual 2258.61346 5432 .415797764 R-squared = 0.0798 Model 195.831563 3 65.2771875 Prob > F = 0.0000 F( 3, 5432) = 156.99 Source SS df MS Number of obs = 5436 Total Groups 39
  40. 40. F test that all u_i=0: F(162, 5270) = 98.59 Prob > F = 0.0000 rho .95557807 (fraction of variance due to u_i) sigma_e .32607847 sigma_u 1.5123647 _cons -7.844568 .2477214 -31.67 0.000 -8.330205 -7.358932 l_popt .0731808 .0285325 2.56 0.010 .0172453 .1291163 l_infl2 -.0281481 .004758 -5.92 0.000 -.0374757 -.0188204 l_realgdp .3846652 .0153326 25.09 0.000 .354607 .4147234 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9200 Prob > F = 0.0000 F(3,5270) = 859.53 overall = 0.0036 max = 55 between = 0.0140 avg = 33.3 R-sq: within = 0.3285 Obs per group: min = 4 Group variable: idwbcode Number of groups = 163 Fixed-effects (within) regression Number of obs = 5436 _cons 3.315492 .0848041 39.10 0.000 3.149242 3.481742 l_popt .0014429 .0061381 0.24 0.814 -.0105903 .0134762 l_infl2 -.1650409 .0078825 -20.94 0.000 -.1804937 -.149588 l_realgdp -.0086728 .0036681 -2.36 0.018 -.0158636 -.0014819 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2454.44502 5435 .451599819 Root MSE = .64482 Adj R-squared = 0.0793 Residual 2258.61346 5432 .415797764 R-squared = 0.0798 Model 195.831563 3 65.2771875 Prob > F = 0.0000 F( 3, 5432) = 156.99 Source SS df MS Number of obs = 5436 Group Characteristics 40
  41. 41. F test that all u_i=0: F(162, 5270) = 98.59 Prob > F = 0.0000 rho .95557807 (fraction of variance due to u_i) sigma_e .32607847 sigma_u 1.5123647 _cons -7.844568 .2477214 -31.67 0.000 -8.330205 -7.358932 l_popt .0731808 .0285325 2.56 0.010 .0172453 .1291163 l_infl2 -.0281481 .004758 -5.92 0.000 -.0374757 -.0188204 l_realgdp .3846652 .0153326 25.09 0.000 .354607 .4147234 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9200 Prob > F = 0.0000 F(3,5270) = 859.53 overall = 0.0036 max = 55 between = 0.0140 avg = 33.3 R-sq: within = 0.3285 Obs per group: min = 4 Group variable: idwbcode Number of groups = 163 Fixed-effects (within) regression Number of obs = 5436 _cons 3.315492 .0848041 39.10 0.000 3.149242 3.481742 l_popt .0014429 .0061381 0.24 0.814 -.0105903 .0134762 l_infl2 -.1650409 .0078825 -20.94 0.000 -.1804937 -.149588 l_realgdp -.0086728 .0036681 -2.36 0.018 -.0158636 -.0014819 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2454.44502 5435 .451599819 Root MSE = .64482 Adj R-squared = 0.0793 Residual 2258.61346 5432 .415797764 R-squared = 0.0798 Model 195.831563 3 65.2771875 Prob > F = 0.0000 F( 3, 5432) = 156.99 Source SS df MS Number of obs = 5436 Different estimates 41
  42. 42. F test that all u_i=0: F(162, 5270) = 98.59 Prob > F = 0.0000 rho .95557807 (fraction of variance due to u_i) sigma_e .32607847 sigma_u 1.5123647 _cons -7.844568 .2477214 -31.67 0.000 -8.330205 -7.358932 l_popt .0731808 .0285325 2.56 0.010 .0172453 .1291163 l_infl2 -.0281481 .004758 -5.92 0.000 -.0374757 -.0188204 l_realgdp .3846652 .0153326 25.09 0.000 .354607 .4147234 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9200 Prob > F = 0.0000 F(3,5270) = 859.53 overall = 0.0036 max = 55 between = 0.0140 avg = 33.3 R-sq: within = 0.3285 Obs per group: min = 4 Group variable: idwbcode Number of groups = 163 Fixed-effects (within) regression Number of obs = 5436 _cons 3.315492 .0848041 39.10 0.000 3.149242 3.481742 l_popt .0014429 .0061381 0.24 0.814 -.0105903 .0134762 l_infl2 -.1650409 .0078825 -20.94 0.000 -.1804937 -.149588 l_realgdp -.0086728 .0036681 -2.36 0.018 -.0158636 -.0014819 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2454.44502 5435 .451599819 Root MSE = .64482 Adj R-squared = 0.0793 Residual 2258.61346 5432 .415797764 R-squared = 0.0798 Model 195.831563 3 65.2771875 Prob > F = 0.0000 F( 3, 5432) = 156.99 Source SS df MS Number of obs = 5436 Different Fit 42
  43. 43. BETWEEN-GROUPS ESTIMATOR • Recall that the regression model goes through the averages (mean) of variables 𝐸𝑖 𝑦𝑖𝑡 = 𝐸𝑖 𝛼𝑖 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 • We can run a regression on the means of each group 𝑦𝑖 = 𝛼 + 𝑥𝑖 𝛽 43
  44. 44. BETWEEN-GROUPS ESTIMATOR Group 2 Group 1 𝑥 𝑦 44
  45. 45. BETWEEN-GROUPS ESTIMATOR Group 2 Group 1 𝑥 𝑦 45
  46. 46. BETWEEN-GROUPS ESTIMATOR Group 2 Group 1 𝑥 𝑦 46 𝑦𝑖 = 𝛼 + 𝑥𝑖 𝛽
  47. 47. BETWEEN-GROUPS ESTIMATOR • Let´s look at the estimator using sums 𝑆 𝑏 𝑥𝑥 = 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖 − 𝑥 ′ 𝑥𝑖 − 𝑥 𝑆 𝑏 𝑥𝑦 = 𝑖=1 𝑁 𝑡=1 𝑇 𝑥𝑖 − 𝑥 ′ 𝑦𝑖 − 𝑦 𝛽 𝑏 = 𝑆 𝑏 𝑥𝑦 𝑆 𝑏 𝑥𝑥 47
  48. 48. BETWEEN-GROUPS ESTIMATOR • Note that 𝑆 𝑝 𝑥𝑥 = 𝑆 𝑤 𝑥𝑥 + 𝑆 𝑏 𝑥𝑥 𝑆 𝑝 𝑥𝑦 = 𝑆 𝑤 𝑥𝑦 + 𝑆 𝑏 𝑥𝑦 • Hence 𝛽 𝑝 = 𝐹 𝑤 𝛽 𝑤 + 𝐼 − 𝐹 𝑤 𝛽 𝑏 𝐹 𝑤 = 𝑆 𝑤 𝑥𝑥 𝑆 𝑤 𝑥𝑥 + 𝑆 𝑏 𝑥𝑥 48
  49. 49. BETWEEN-GROUPS ESTIMATOR 𝛽 𝑝 = 𝐹 𝑤 𝛽 𝑤 + 𝐼 − 𝐹 𝑤 𝛽 𝑏 • The pooled estimator is a weighted average of the between and within-group estimators • Weights depend on the information content of the data: – If groups are very similar, information comes from individuals within groups – If groups are very different, information comes from differences between groups 49
  50. 50. RESULTS BETWEEN-GROUPS ESTIMATOR 50 _cons 2.450055 .4769026 5.14 0.000 1.508174 3.391936 l_popt .008753 .029578 0.30 0.768 -.0496633 .0671694 l_infl2 -.4064124 .0650977 -6.24 0.000 -.53498 -.2778448 l_realgdp -.0062972 .0168565 -0.37 0.709 -.0395887 .0269942 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] sd(u_i + avg(e_i.))= .5167678 Prob > F = 0.0000 F(3,159) = 14.81 overall = 0.0787 max = 55 between = 0.2185 avg = 33.3 R-sq: within = 0.0155 Obs per group: min = 4 Group variable: idwbcode Number of groups = 163 Between regression (regression on group means) Number of obs = 5436 . xtreg l_money l_realgdp l_infl2 l_popt, be
  51. 51. ESTIMATING THE VARIANCE OF RESIDUALS • Compute the sample residuals as: 𝜀𝑖𝑡 = 𝑦𝑖𝑡 − 𝛼𝑖 − 𝑥𝑖𝑡 𝛽 • The residual variance estimator is simply: 𝜎2 = 𝑖=1 𝑁 𝑡=1 𝑇 𝑦𝑖𝑡 − 𝛼𝑖 − 𝑥𝑖𝑡 𝛽 2 𝑁𝑇 − 𝑁 − 𝐾 51
  52. 52. HYPOTHESIS TESTING • Having the estimated parameters and the residual variance estimator hypotheses testing is straightforward – Individual parameter tests distribute t in small samples and Normal in large samples – Multiple parameter tests distribute 𝜒2 or 𝐹(𝑚, 𝑛) 52
  53. 53. TWO-WAY FIXED EFFECTS ESTIMATOR • Model with fixed individual effects and fixed time effects 𝑦𝑖𝑡 = 𝛼𝑖 + 𝜆 𝑡 + 𝑥𝑖𝑡 𝛽 + 𝜀𝑖𝑡 where • 𝜆 𝑡 is a time effect affecting equally all individuals • 𝛼𝑖 is, again, an individual effect for all times 53
  54. 54. RESULTS OF THE TWO-WAY ESTIMATOR 54 1966 .1359131 .0705045 1.93 0.054 -.0023053 .2741315 1965 .1098147 .0730354 1.50 0.133 -.0333653 .2529947 1964 .1550872 .0736255 2.11 0.035 .0107504 .2994241 1963 .1448196 .0739148 1.96 0.050 -.0000844 .2897236 1962 .0419273 .0757742 0.55 0.580 -.1066219 .1904766 1961 -.0976766 .0752323 -1.30 0.194 -.2451635 .0498103 year l_popt -.1974149 .0380194 -5.19 0.000 -.2719489 -.1228809 l_infl2 -.0301174 .0054168 -5.56 0.000 -.0407367 -.0194982 l_realgdp .2112165 .0187937 11.24 0.000 .1743731 .24806 l_money Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 2454.44502 5435 .451599819 Root MSE = .31621 Adj R-squared = 0.7786 Residual 521.554775 5216 .09999133 R-squared = 0.7875 Model 1932.89024 219 8.82598285 Prob > F = 0.0000 F(219, 5216) = 88.27 Source SS df MS Number of obs = 5436 ⋮ ⋮ ⋮ ⋮ ⋮⋮ ⋮

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