------------------------------------------------------------------------------------------------------ log: c:\Imbook\bwebpage\Section5\mma21p3panresiduals.txt log type: text opened on: 23 May 2005, 13:01:06 . . ********** OVERVIEW OF MMA21P3PANRESIDUALS.DO ********** . . * STATA Program . * copyright C 2005 by A. Colin Cameron and Pravin K. Trivedi . * used for "Microeconometrics: Methods and Applications" . * by A. Colin Cameron and Pravin K. Trivedi (2005) . * Cambridge University Press . . * Chapter 21.3.4 pages 713-15 Residual analysis . * This program . * (1) estimates correlations for . * - dependent variable . * - regressors variable . * - residuals from pooled ols [Table 21.3] . * - residuals from within estimation [Table 21.4] . * - residuals from random effects estimation . * (2) separately estimates correlations for . * - residuals from first differences estiamtion . * (3) gets correlations for each individual observation . . * The code is very limited: . * - it considers only one regressor . * - it assumes a balanced data set with exactly 10 years of data per obnservations . * - it does not use loops for transformations which would generalize code . . * The four basic linear panel programs are . * mma21p1panfeandre.do Linear fixed and random effects using xtreg . * mma21p2panfeandre.do Linear fe and re using transformation and regress . * plus also has valid Hausman test . * mma21p3panresiduals.do Residual analysis after linear fe and re . * mma21p4panpangls.do Pooled panel OLS and GLS . . * To run you need file . * MOM.dat . * in your directory . . ********** SETUP ********** . . set more off . version 8.0 . set scheme s1mono /* Graphics scheme */ . . ********** DATA DESCRIPTION ********** . . * The original data is from . * Jim Ziliak (1997) . * "Efficient Estimation With Panel Data when Instruments are Predetermined: . * An Emprirical Comparison of Moment-Condition Estimators" . * Journal of Business and Economic Statistics, 15, 419-431 . . * File MOM.dat has data on 532 men over 10 years (1979-1988) . * Data are space-delimited ordered by person with separate line for each year . * So id 1 1979, id 1 1980, ..., id 1 1988, id 2 1979, 1d 2 1980, ... . * 8 variables: . * lnhr lnwg kids ageh agesq disab id year . . * File MOM.dat is the version of the data posted at the JBES website . * Note that in chapter 22 we instead use MOMprecise.dat . * which is the same data set but with more significant digits . . ********** READ DATA ********** . * . * The data are in ascii file MOM.dat . * There are 532 individuals with 10 lines (years) per individual . * Read in using Infile: FREE FORMAT WITHOUT DICTIONARY . infile lnhr lnwg kids ageh agesq disab id year using MOM.dat (5320 observations read) . summarize Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- lnhr | 5320 7.65743 .2855914 2.77 8.56 lnwg | 5320 2.609436 .4258924 -.26 4.69 kids | 5320 1.555827 1.195924 0 6 ageh | 5320 38.91823 8.450351 22 60 agesq | 5320 1586.024 689.7759 484 3600 -------------+-------------------------------------------------------- disab | 5320 .0609023 .2391734 0 1 id | 5320 266.5 153.5893 1 532 year | 5320 1983.5 2.872551 1979 1988 . . ************ (1) ANALYSIS: OBTAIN KEY AUTOCORRELATIONS Tables 21.3, 21.4 ********** . . ** RUN REGRESSIONS AND GET RESIDUALS OF INTEREST . . * pooled ols . regress lnhr lnwg Source | SS df MS Number of obs = 5320 -------------+------------------------------ F( 1, 5318) = 82.22 Model | 6.60538417 1 6.60538417 Prob > F = 0.0000 Residual | 427.225206 5318 .080335691 R-squared = 0.0152 -------------+------------------------------ Adj R-squared = 0.0150 Total | 433.830591 5319 .081562435 Root MSE = .28344 ------------------------------------------------------------------------------ lnhr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnwg | .0827436 .0091251 9.07 0.000 .0648545 .1006326 _cons | 7.441516 .0241265 308.44 0.000 7.394219 7.488814 ------------------------------------------------------------------------------ . predict upols, residuals . . * fixed effects (within) . xtreg lnhr lnwg, fe i(id) Fixed-effects (within) regression Number of obs = 5320 Group variable (i): id Number of groups = 532 R-sq: within = 0.0162 Obs per group: min = 10 between = 0.0213 avg = 10.0 overall = 0.0152 max = 10 F(1,4787) = 78.96 corr(u_i, Xb) = -0.1995 Prob > F = 0.0000 ------------------------------------------------------------------------------ lnhr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnwg | .1676755 .01887 8.89 0.000 .1306816 .2046694 _cons | 7.219892 .0493434 146.32 0.000 7.123156 7.316628 -------------+---------------------------------------------------------------- sigma_u | .18142881 sigma_e | .23278339 rho | .37789558 (fraction of variance due to u_i) ------------------------------------------------------------------------------ F test that all u_i=0: F(531, 4787) = 5.83 Prob > F = 0.0000 . predict ufe, e . . * random effects . xtreg lnhr lnwg, re i(id) Random-effects GLS regression Number of obs = 5320 Group variable (i): id Number of groups = 532 R-sq: within = 0.0162 Obs per group: min = 10 between = 0.0213 avg = 10.0 overall = 0.0152 max = 10 Random effects u_i ~ Gaussian Wald chi2(1) = 76.64 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ lnhr | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnwg | .1193322 .0136312 8.75 0.000 .0926155 .146049 _cons | 7.346041 .0363925 201.86 0.000 7.274713 7.417368 -------------+---------------------------------------------------------------- sigma_u | .16124733 sigma_e | .23278339 rho | .32424354 (fraction of variance due to u_i) ------------------------------------------------------------------------------ . predict ure, e . . summarize upols ufe ure Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- upols | 5320 -1.27e-10 .2834089 -4.826247 .964581 ufe | 5320 -5.52e-11 .2208354 -4.003929 1.2719 ure | 5320 -9.00e-11 .2231118 -4.131111 1.085362 . save mom3, replace file mom3.dta saved . . ** TRANSFORM DATA FROM LONG FORM TO WIDE FORM . . * Here just do this for lnhr and lnwg and the residuals . keep lnhr lnwg id year upols ufe ure . reshape wide lnhr lnwg upols ufe ure, i(id) j(year) (note: j = 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988) Data long -> wide ----------------------------------------------------------------------------- Number of obs. 5320 -> 532 Number of variables 7 -> 51 j variable (10 values) year -> (dropped) xij variables: lnhr -> lnhr1979 lnhr1980 ... lnhr1988 lnwg -> lnwg1979 lnwg1980 ... lnwg1988 upols -> upols1979 upols1980 ... upols1988 ufe -> ufe1979 ufe1980 ... ufe1988 ure -> ure1979 ure1980 ... ure1988 ----------------------------------------------------------------------------- . . * Since year is 1979 to 1988 this will create . * lnhr1979 to lnhr1988 and lnwg1979 to lnwg1988 . . summarize Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- id | 532 266.5 153.7194 1 532 lnhr1979 | 532 7.669342 .249361 5.89 8.54 lnwg1979 | 532 2.597763 .4188951 .52 4.62 upols1979 | 532 .0128775 .2517228 -1.764168 .8312218 ufe1979 | 532 .0138689 .2249175 -1.578105 1.2719 -------------+-------------------------------------------------------- ure1979 | 532 .0133046 .2200196 -1.618987 1.085362 lnhr1980 | 532 7.660094 .2691995 5.22 8.34 lnwg1980 | 532 2.602368 .3945963 .8 4.61 upols1980 | 532 .0032483 .2679463 -2.354734 .6659743 ufe1980 | 532 .0038486 .2253673 -2.085636 1.128546 -------------+-------------------------------------------------------- ure1980 | 532 .0035069 .2238723 -2.089847 .9429754 lnhr1981 | 532 7.66765 .2105797 6.36 8.4 lnwg1981 | 532 2.610959 .3870011 1.53 4.53 upols1981 | 532 .0100939 .2133106 -1.342159 .7582438 ufe1981 | 532 .0099646 .163407 -1.001722 1.03687 -------------+-------------------------------------------------------- ure1981 | 532 .0100382 .1596593 -1.02491 .8517824 lnhr1982 | 532 7.64609 .2427195 5.38 8.31 lnwg1982 | 532 2.61468 .4014363 1.21 4.61 upols1982 | 532 -.0117742 .2422735 -2.264238 .6897579 ufe1982 | 532 -.0122196 .1890237 -1.623214 .7918997 -------------+-------------------------------------------------------- ure1982 | 532 -.0119661 .1875585 -1.737484 .6666697 lnhr1983 | 532 7.613064 .382703 2.77 8.37 lnwg1983 | 532 2.610526 .4111869 1.08 4.62 upols1983 | 532 -.0444568 .3778255 -4.826247 .7307264 ufe1983 | 532 -.0445494 .2836351 -3.577253 .5196197 -------------+-------------------------------------------------------- ure1983 | 532 -.0444967 .294545 -3.804399 .5078294 lnhr1984 | 532 7.636523 .3316735 3.18 8.44 lnwg1984 | 532 2.600188 .4621549 -.26 4.65 upols1984 | 532 -.0201427 .3208512 -4.240003 .8263766 ufe1984 | 532 -.0193572 .225836 -2.810104 .8327778 -------------+-------------------------------------------------------- ure1984 | 532 -.0198043 .2378605 -3.140221 .7036628 lnhr1985 | 532 7.668365 .2597423 5.08 8.54 lnwg1985 | 532 2.614944 .4347554 1.33 4.69 upols1985 | 532 .0104785 .259051 -2.503835 .8624523 ufe1985 | 532 .0100107 .1856724 -1.581894 .7944546 -------------+-------------------------------------------------------- ure1985 | 532 .010277 .1886509 -1.752727 .7370209 lnhr1986 | 532 7.659286 .3330862 2.77 8.38 lnwg1986 | 532 2.602632 .4432807 .07 4.59 upols1986 | 532 .0024183 .3312105 -4.801424 .7439653 ufe1986 | 532 .0029962 .2595405 -4.003929 .6384854 -------------+-------------------------------------------------------- ure1986 | 532 .0026673 .264328 -4.131111 .5111209 lnhr1987 | 532 7.67406 .2745015 4.38 8.56 lnwg1987 | 532 2.614699 .4300122 1.28 4.03 upols1987 | 532 .0161942 .2749153 -3.283269 .964581 ufe1987 | 532 .0157472 .2141618 -2.817174 1.009662 -------------+-------------------------------------------------------- ure1987 | 532 .0160016 .2148092 -2.897725 .8441463 lnhr1988 | 532 7.679831 .2552894 4.79 8.53 lnwg1988 | 532 2.625602 .4701759 -.22 4.6 upols1988 | 532 .0210628 .2519891 -2.633313 .9072749 ufe1988 | 532 .0196898 .2048927 -1.68379 1.123516 -------------+-------------------------------------------------------- ure1988 | 532 .0204713 .2022375 -1.897506 .9393954 . . ** OBTAIN THE VARIOUS CORRELATIONS . . corr lnhr1979 lnhr1980 lnhr1981 lnhr1982 lnhr1983 lnhr1984 lnhr1985 lnhr1986 lnhr1987 lnhr1988 (obs=532) | lnhr1979 lnhr1980 lnhr1981 lnhr1982 lnhr1983 lnhr1984 lnhr1985 lnhr1986 lnhr1987 -------------+--------------------------------------------------------------------------------- lnhr1979 | 1.0000 lnhr1980 | 0.3220 1.0000 lnhr1981 | 0.4321 0.4022 1.0000 lnhr1982 | 0.2947 0.3142 0.5670 1.0000 lnhr1983 | 0.2070 0.2324 0.3788 0.4781 1.0000 lnhr1984 | 0.1908 0.2235 0.3141 0.3318 0.6476 1.0000 lnhr1985 | 0.2284 0.3184 0.3999 0.3453 0.3930 0.5839 1.0000 lnhr1986 | 0.1934 0.1931 0.2813 0.2524 0.3162 0.3595 0.4128 1.0000 lnhr1987 | 0.1986 0.3160 0.3322 0.2951 0.3261 0.3464 0.3987 0.3603 1.0000 lnhr1988 | 0.1640 0.2551 0.3081 0.2674 0.2267 0.2537 0.3509 0.5741 0.5248 | lnhr1988 -------------+--------- lnhr1988 | 1.0000 . corr lnwg1979 lnwg1980 lnwg1981 lnwg1982 lnwg1983 lnwg1984 lnwg1985 lnwg1986 lnwg1987 lnwg1988 (obs=532) | lnwg1979 lnwg1980 lnwg1981 lnwg1982 lnwg1983 lnwg1984 lnwg1985 lnwg1986 lnwg1987 -------------+--------------------------------------------------------------------------------- lnwg1979 | 1.0000 lnwg1980 | 0.8415 1.0000 lnwg1981 | 0.8283 0.8920 1.0000 lnwg1982 | 0.7984 0.8559 0.9015 1.0000 lnwg1983 | 0.7795 0.8408 0.8787 0.9155 1.0000 lnwg1984 | 0.7208 0.7737 0.8102 0.8267 0.8625 1.0000 lnwg1985 | 0.7424 0.7929 0.8290 0.8511 0.8636 0.8620 1.0000 lnwg1986 | 0.7250 0.7714 0.8122 0.8286 0.8530 0.8399 0.9157 1.0000 lnwg1987 | 0.7188 0.7639 0.8029 0.8282 0.8525 0.8681 0.9117 0.9111 1.0000 lnwg1988 | 0.7220 0.7604 0.7900 0.8139 0.8326 0.8373 0.8787 0.8743 0.9101 | lnwg1988 -------------+--------- lnwg1988 | 1.0000 . * The following gives Table 21.3 p.714 . corr upols1979 upols1980 upols1981 upols1982 upols1983 upols1984 upols1985 upols1986 upols1987 upo > ls1988 (obs=532) | upo~1979 upo~1980 upo~1981 upo~1982 upo~1983 upo~1984 upo~1985 upo~1986 upo~1987 -------------+--------------------------------------------------------------------------------- upols1979 | 1.0000 upols1980 | 0.3283 1.0000 upols1981 | 0.4442 0.4035 1.0000 upols1982 | 0.3008 0.3140 0.5678 1.0000 upols1983 | 0.2089 0.2298 0.3739 0.4684 1.0000 upols1984 | 0.2025 0.2289 0.3194 0.3360 0.6398 1.0000 upols1985 | 0.2395 0.3246 0.4087 0.3484 0.3898 0.5800 1.0000 upols1986 | 0.1987 0.1903 0.2797 0.2470 0.3109 0.3535 0.3991 1.0000 upols1987 | 0.2091 0.3167 0.3340 0.2877 0.3097 0.3361 0.3941 0.3496 1.0000 upols1988 | 0.1619 0.2456 0.3016 0.2582 0.2083 0.2470 0.3436 0.5545 0.5242 | upo~1988 -------------+--------- upols1988 | 1.0000 . corr ure1979 ure1980 ure1981 ure1982 ure1983 ure1984 ure1985 ure1986 ure1987 ure1988 (obs=532) | ure1979 ure1980 ure1981 ure1982 ure1983 ure1984 ure1985 ure1986 ure1987 -------------+--------------------------------------------------------------------------------- ure1979 | 1.0000 ure1980 | 0.0778 1.0000 ure1981 | 0.1777 0.0604 1.0000 ure1982 | -0.0250 -0.0519 0.2492 1.0000 ure1983 | -0.2339 -0.2277 -0.1609 0.0587 1.0000 ure1984 | -0.2482 -0.2431 -0.2691 -0.1709 0.3795 1.0000 ure1985 | -0.1842 -0.0919 -0.1054 -0.1581 -0.0939 0.2197 1.0000 ure1986 | -0.1860 -0.2333 -0.2434 -0.2405 -0.1110 -0.0763 -0.0361 1.0000 ure1987 | -0.1665 -0.0481 -0.1580 -0.1904 -0.1710 -0.1506 -0.0646 -0.0553 1.0000 ure1988 | -0.1960 -0.1251 -0.1646 -0.1949 -0.3265 -0.2786 -0.1221 0.2708 0.2379 | ure1988 -------------+--------- ure1988 | 1.0000 . * The following gives Table 21.4 p.715 . corr ufe1979 ufe1980 ufe1981 ufe1982 ufe1983 ufe1984 ufe1985 ufe1986 ufe1987 ufe1988 (obs=532) | ufe1979 ufe1980 ufe1981 ufe1982 ufe1983 ufe1984 ufe1985 ufe1986 ufe1987 -------------+--------------------------------------------------------------------------------- ufe1979 | 1.0000 ufe1980 | 0.1017 1.0000 ufe1981 | 0.2082 0.0802 1.0000 ufe1982 | 0.0003 -0.0380 0.2631 1.0000 ufe1983 | -0.2632 -0.2691 -0.2113 0.0089 1.0000 ufe1984 | -0.2594 -0.2698 -0.3004 -0.2037 0.3249 1.0000 ufe1985 | -0.1757 -0.0958 -0.1069 -0.1685 -0.1617 0.1713 1.0000 ufe1986 | -0.1915 -0.2534 -0.2644 -0.2676 -0.1723 -0.1364 -0.0865 1.0000 ufe1987 | -0.1519 -0.0497 -0.1561 -0.2008 -0.2399 -0.2066 -0.0918 -0.0908 1.0000 ufe1988 | -0.1650 -0.1109 -0.1385 -0.1772 -0.3816 -0.3096 -0.1268 0.2420 0.2439 | ufe1988 -------------+--------- ufe1988 | 1.0000 . . * The following does estimation for just one year . regress lnhr1979 lnwg1979 Source | SS df MS Number of obs = 532 -------------+------------------------------ F( 1, 530) = 0.00 Model | .000035507 1 .000035507 Prob > F = 0.9810 Residual | 33.0180361 530 .062298181 R-squared = 0.0000 -------------+------------------------------ Adj R-squared = -0.0019 Total | 33.0180716 531 .062180926 Root MSE = .2496 ------------------------------------------------------------------------------ lnhr1979 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnwg1979 | .0006173 .0258574 0.02 0.981 -.0501783 .0514129 _cons | 7.667738 .0680375 112.70 0.000 7.534082 7.801395 ------------------------------------------------------------------------------ . . ************ (2) ANALYSIS: OBTAIN AUTOCORRELATIONS FOR FIRST DIFFERNCES . . ** SET UP THE DATA . use mom, clear . gen dlnhr = lnhr - lnhr[_n-1] (1 missing value generated) . gen dlnwg = lnwg - lnwg[_n-1] (1 missing value generated) . * The following drops the first year which here is 1979 . drop if year == 1979 (532 observations deleted) . regress dlnhr dlnwg Source | SS df MS Number of obs = 4788 -------------+------------------------------ F( 1, 4786) = 26.09 Model | 2.27870825 1 2.27870825 Prob > F = 0.0000 Residual | 417.943979 4786 .087326364 R-squared = 0.0054 -------------+------------------------------ Adj R-squared = 0.0052 Total | 420.222687 4787 .087784142 Root MSE = .29551 ------------------------------------------------------------------------------ dlnhr | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- dlnwg | .1089851 .0213351 5.11 0.000 .0671584 .1508118 _cons | .0008283 .0042712 0.19 0.846 -.0075452 .0092018 ------------------------------------------------------------------------------ . predict ufdiff, residuals . * Here just do this for lnhr and lnwg and the residuals . keep dlnhr dlnwg ufdiff id year . reshape wide dlnhr dlnwg ufdiff, i(id) j(year) (note: j = 1980 1981 1982 1983 1984 1985 1986 1987 1988) Data long -> wide ----------------------------------------------------------------------------- Number of obs. 4788 -> 532 Number of variables 5 -> 28 j variable (9 values) year -> (dropped) xij variables: dlnhr -> dlnhr1980 dlnhr1981 ... dlnhr1988 dlnwg -> dlnwg1980 dlnwg1981 ... dlnwg1988 ufdiff -> ufdiff1980 ufdiff1981 ... ufdiff1988 ----------------------------------------------------------------------------- . summarize Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- id | 532 266.5 153.7194 1 532 dlnhr1980 | 532 -.0092481 .3023508 -2.5 1.71 dlnwg1980 | 532 .0046053 .2301879 -2.12 1.05 ufdiff1980 | 532 -.0105783 .3014161 -2.499738 1.690644 dlnhr1981 | 532 .0075564 .2668644 -1.2 2.32 -------------+-------------------------------------------------------- dlnwg1981 | 532 .0085902 .1818033 -.79 1.62 ufdiff1981 | 532 .0057919 .2669213 -1.145188 2.343149 dlnhr1982 | 532 -.0215602 .212834 -2.06 1.14 dlnwg1982 | 532 .0037218 .1755574 -1.17 .74 ufdiff1982 | 532 -.0227941 .213709 -2.036851 1.135902 -------------+-------------------------------------------------------- dlnhr1983 | 532 -.0330263 .3413969 -4.51 .9899998 dlnwg1983 | 532 -.0041541 .1673057 -.88 .6399999 ufdiff1983 | 532 -.0334019 .3398726 -4.419281 .9780819 dlnhr1984 | 532 .0234586 .3034213 -2.31 2.57 dlnwg1984 | 532 -.0103383 .2342514 -2.13 .77 -------------+-------------------------------------------------------- ufdiff1984 | 532 .0237571 .3004287 -2.168058 2.502691 dlnhr1985 | 532 .0318421 .2772558 -1.46 3.52 dlnwg1985 | 532 .0147556 .2371054 -1.33 3.06 ufdiff1985 | 532 .0294057 .2697542 -1.315878 3.185677 dlnhr1986 | 532 -.0090789 .3270724 -4.79 1.8 -------------+-------------------------------------------------------- dlnwg1986 | 532 -.012312 .1804162 -1.83 1.04 ufdiff1986 | 532 -.0085654 .3299129 -4.796278 1.789363 dlnhr1987 | 532 .0147744 .3470122 -3.24 4.52 dlnwg1987 | 532 .0120677 .1845692 -.9400001 1.95 ufdiff1987 | 532 .0126309 .3494111 -3.243008 4.550777 -------------+-------------------------------------------------------- dlnhr1988 | 532 .0057707 .2587991 -2.5 2.74 dlnwg1988 | 532 .0109023 .194813 -1.5 1.22 ufdiff1988 | 532 .0037542 .2576554 -2.337351 2.739172 . . ** GET THE CORRELATIONS . corr dlnhr1980 dlnhr1981 dlnhr1982 dlnhr1983 dlnhr1984 dlnhr1985 dlnhr1986 dlnhr1987 dlnhr1988 (obs=532) | dlnhr1~0 dlnhr1~1 dlnhr1~2 dlnhr1~3 dlnhr1~4 dlnhr1~5 dlnhr1~6 dlnhr1~7 dlnhr1~8 -------------+--------------------------------------------------------------------------------- dlnhr1980 | 1.0000 dlnhr1981 | -0.6289 1.0000 dlnhr1982 | 0.0402 -0.2306 1.0000 dlnhr1983 | 0.0144 -0.0204 -0.2209 1.0000 dlnhr1984 | -0.0001 -0.0570 -0.1410 -0.4495 1.0000 dlnhr1985 | 0.0393 -0.0320 -0.0827 -0.4035 -0.1969 1.0000 dlnhr1986 | -0.0629 0.0322 0.0112 0.0233 -0.1192 -0.2334 1.0000 dlnhr1987 | 0.0811 -0.0709 -0.0029 -0.0448 -0.0202 0.0093 -0.6231 1.0000 dlnhr1988 | -0.0341 0.0461 -0.0082 -0.1020 0.0261 0.0682 0.2486 -0.6064 1.0000 . corr dlnwg1980 dlnwg1981 dlnwg1982 dlnwg1983 dlnwg1984 dlnwg1985 dlnwg1986 dlnwg1987 dlnwg1988 (obs=532) | dlnwg1~0 dlnwg1~1 dlnwg1~2 dlnwg1~3 dlnwg1~4 dlnwg1~5 dlnwg1~6 dlnwg1~7 dlnwg1~8 -------------+--------------------------------------------------------------------------------- dlnwg1980 | 1.0000 dlnwg1981 | -0.3507 1.0000 dlnwg1982 | -0.0149 -0.2849 1.0000 dlnwg1983 | 0.0215 -0.0351 -0.3338 1.0000 dlnwg1984 | -0.0112 0.0098 -0.0686 -0.1899 1.0000 dlnwg1985 | -0.0135 -0.0085 0.0141 -0.1179 -0.5560 1.0000 dlnwg1986 | -0.0121 0.0289 -0.0303 0.0725 -0.0526 -0.2665 1.0000 dlnwg1987 | -0.0042 -0.0119 0.0382 -0.0083 0.1200 -0.1482 -0.5043 1.0000 dlnwg1988 | -0.0281 -0.0377 0.0157 -0.0133 -0.0174 -0.0058 -0.0174 -0.2627 1.0000 . corr ufdiff1980 ufdiff1981 ufdiff1982 ufdiff1983 ufdiff1984 ufdiff1985 ufdiff1986 ufdiff1987 ufdif > f1988 (obs=532) | ufd~1980 ufd~1981 ufd~1982 ufd~1983 ufd~1984 ufd~1985 ufd~1986 ufd~1987 ufd~1988 -------------+--------------------------------------------------------------------------------- ufdiff1980 | 1.0000 ufdiff1981 | -0.6263 1.0000 ufdiff1982 | 0.0451 -0.2389 1.0000 ufdiff1983 | 0.0128 -0.0239 -0.2316 1.0000 ufdiff1984 | -0.0010 -0.0588 -0.1291 -0.4804 1.0000 ufdiff1985 | 0.0453 -0.0285 -0.0868 -0.3731 -0.1853 1.0000 ufdiff1986 | -0.0674 0.0321 0.0110 0.0256 -0.1138 -0.2538 1.0000 ufdiff1987 | 0.0811 -0.0711 -0.0077 -0.0533 -0.0081 0.0211 -0.6250 1.0000 ufdiff1988 | -0.0323 0.0499 0.0022 -0.1019 0.0368 0.0543 0.2326 -0.5943 1.0000 . . ************ (3) ANALYSIS: CORRELATIONS FOR AN INDIVIDUAL OBSERVATION . . * Look at correlations for each individual . . ** TRANSFORM DATA FROM LONG FORM TO WIDE FORM FOR INDIVIDUALS . . use mom3, replace . * Here just do this for lnhr and lnwg and the residuals . keep lnhr lnwg id year . reshape wide lnhr lnwg, i(year) j(id) (note: j = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 > 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 6 > 6 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 > 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 > 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 1 > 48 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 > 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 1 > 97 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 > 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 2 > 46 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 > 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 2 > 95 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 > 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 3 > 44 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 > 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 3 > 93 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 > 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 4 > 42 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 > 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 4 > 91 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 > 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532) Data long -> wide ----------------------------------------------------------------------------- Number of obs. 5320 -> 10 Number of variables 4 -> 1065 j variable (532 values) id -> (dropped) xij variables: lnhr -> lnhr1 lnhr2 ... lnhr532 lnwg -> lnwg1 lnwg2 ... lnwg532 ----------------------------------------------------------------------------- . * Note that i and j are reversed . . * Since year is 1979 to 1988 this will create . * lnhr1979 to lnhr1988 and lnwg1979 to lnwg1988 . . tsset year time variable: year, 1979 to 1988 . . * First-order Correlation over T years for the first observation . corr lnhr1 L.lnhr1 (obs=9) | L. | lnhr1 lnhr1 -------------+------------------ lnhr1 | -- | 1.0000 L1 | 0.6378 1.0000 . * First-order Correlation over T years for the second observation . corr lnhr2 L.lnhr2 (obs=9) | L. | lnhr2 lnhr2 -------------+------------------ lnhr2 | -- | 1.0000 L1 | 0.5553 1.0000 . * And so on . . ********** CLOSE OUTPUT . log close log: c:\Imbook\bwebpage\Section5\mma21p3panresiduals.txt log type: text closed on: 23 May 2005, 13:01:15 ----------------------------------------------------------------------------------------------------