Panel Data. Assembling the Data insheet using marriage-data.csv, c d u "background-data", clear d u "experience-data", clear u "wage-data", clear d reshape.

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Presentation transcript:

Panel Data

Assembling the Data insheet using marriage-data.csv, c d u "background-data", clear d u "experience-data", clear u "wage-data", clear d reshape long lwage, i(nr) j(year) sort nr year merge 1:1 nr year using "marriage-data" drop _merge merge 1:1 nr year using "experience-data" drop _merge merge n:1 nr using "background-data" drop _merge d sum save "data-exercise-11-nls", replace

(2) Is the data balanced? xtset nr year panel variable: nr (strongly balanced) time variable: year, 1980 to 1987 delta: 1 unit What does being balanced mean?

(3) First Step. reg lwage married Source | SS df MS Number of obs = F( 1, 4358) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] married | _cons |

(4) Controls. reg lwage married exper union educ black hisp Source | SS df MS Number of obs = F( 6, 4353) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] married | exper | union | educ | black | hisp | _cons |

(5) Panel Data. xtreg lwage married exper union educ black hisp, fe note: educ omitted because of collinearity note: black omitted because of collinearity note: hisp omitted because of collinearity Fixed-effects (within) regression Number of obs = 4360 Group variable: nr Number of groups = 545 R-sq: within = Obs per group: min = 8 between = avg = 8.0 overall = max = 8 F(3,3812) = corr(u_i, Xb) = Prob > F = lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] married | exper | union | educ | 0 (omitted) black | 0 (omitted) hisp | 0 (omitted) _cons | sigma_u | sigma_e | rho | (fraction of variance due to u_i) F test that all u_i=0: F(544, 3812) = Prob > F = Why have ‘black’, ‘educ’ and ‘hisp’ been dropped from the regression? What variation are we working off when we include fixed effects?

Collinearity IDYearID1_FEID2_FEID3_FEIncomeMarriedBlackNational GDP 1995_FE etc 

(6) Clustering xtreg lwage married exper union, fe cluster(nr) Fixed-effects (within) regression Number of obs = 4360 Group variable: nr Number of groups = 545 R-sq: within = Obs per group: min = 8 between = avg = 8.0 overall = max = 8 F(3,544) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 545 clusters in nr) | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] married | exper | union | _cons | sigma_u | sigma_e | rho | (fraction of variance due to u_i)

(7) Are dummies equivalent to FE?. reg lwage married exper union i.nr, cluster(nr) Linear regression Number of obs = 4360 F( 2, 544) =. Prob > F =. R-squared = Root MSE = (Std. Err. adjusted for 545 clusters in nr) | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] married | exper | union |

(7) Time FE? Why not include Experience?. xtreg lwage married union i.year, fe cluster(nr) Fixed-effects (within) regression Number of obs = 4360 Group variable: nr Number of groups = 545 R-sq: within = Obs per group: min = 8 between = avg = 8.0 overall = max = 8 F(9,544) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 545 clusters in nr) | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] married | union | | year | 1981 | | | | | | | | _cons | sigma_u | sigma_e | rho | (fraction of variance due to u_i)

(8) Driven By Divorce?. xtreg lwage married union i.year if everdivorce == 0, fe cluster(nr) Fixed-effects (within) regression Number of obs = 3792 Group variable: nr Number of groups = 474 R-sq: within = Obs per group: min = 8 between = avg = 8.0 overall = max = 8 F(9,473) = corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 474 clusters in nr) | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] married | union | | year | 1981 | | | | | | | | _cons | sigma_u | sigma_e | rho | (fraction of variance due to u_i)

(8) Driven by Divorce 2?. xtreg lwage married union i.year if everdivorce == 1, fe cluster(nr) Fixed-effects (within) regression Number of obs = 568 Group variable: nr Number of groups = 71 R-sq: within = Obs per group: min = 8 between = avg = 8.0 overall = max = 8 F(9,70) = 6.21 corr(u_i, Xb) = Prob > F = (Std. Err. adjusted for 71 clusters in nr) | Robust lwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] married | union | | year | 1981 | | | | | | | | _cons | sigma_u | sigma_e | rho | (fraction of variance due to u_i)