Example L2 Continuous response model

 

We analyses a version of the NLSY data as used in various Stata Manuals (to illustrate the xt commands). The data is for young women who were aged 14-26 in 1968. The women were surveyed each year from 1970 to 1988, except for 1974, 1976, 1979, 1981, 1984 and 1986. We have removed records with missing values on one or more of the response and explanatory variables we want use in our analysis of the joint determinants of wages and trade union membership. In this example we model the wage response. There are 4132 women (idcode) with between 1 and 12 years of observation on wages being in employment (i.e. not in full time education) and earning more than $1/hour but less than $700/hour.

 

Reference

 

Stata Longitudinal/Panel Data, Reference Manual, Release 9, (2005), Stata Press, StataCorp LP, College Station, Texas.

 

Data description

 

Number of observations (rows): 18995

Number of variables (columns): 20

 

The subset of variables we use are:

 

ln_wage=ln(wage/GNP deflator) in a particular year are:

black=1 if woman is black, 0 otherwise;

msp=1 if woman is married and spouse is present, 0 otherwise;

grade= years of schooling completed; (0-18);

not_smsa=1 if woman was living outside a standard metropolitan statistical area (smsa), 0 otherwise;

south=1 if the woman was living in the South, 0 otherwise;

union=1 if a member of a trade union, 0 otherwise;

tenure= job tenure in years (0-26).

 

 

The first few lines of nls.dat look like

 

 

 

Sabre commands

 

out wage.log

trace wage.trace

data idcode year birth_yr age race msp nev_mar grade collgrad not_smsa &

     c_city south union ttl_exp tenure ln_wage black age2 ttl_exp2 tenure2

read nls.dat

case idcode

yvar ln_wage

family g

constant cons

lfit black msp grade not_smsa south union tenure cons

dis m

dis e

mass 64

fit black msp grade not_smsa south union tenure cons

dis m

dis e

stop

 

 

 

Sabre log file

 

<S> trace wage.trace

<S> data idcode year birth_yr age race msp nev_mar grade collgrad not_smsa &

<S>      c_city south union ttl_exp tenure ln_wage black age2 ttl_exp2 tenure2

<S> read nls.dat

 

      18995 observations in dataset

 

<S> case idcode

<S> yvar ln_wage

<S> family g

<S> constant cons

<S> lfit black msp grade not_smsa south union tenure cons

 

    Iteration       Log. lik.       Difference

    __________________________________________

        1          -8329.7229

 

<S> dis m

 

    X-vars            Y-var

    ______________________________

    cons              ln_wage

    black

    msp

    grade

    not_smsa

    south

    union

    tenure

 

    Univariate model

    Standard linear

 

    Number of observations             =   18995

 

    X-var df           =     8

 

    Log likelihood =     -8329.7229     on   18987 residual degrees of freedom

 

<S> dis e

 

    Parameter              Estimate         Std. Err.

    ___________________________________________________

    cons                   0.82027          0.16614E-01

    black                 -0.10093          0.66150E-02

    msp                    0.50526E-03      0.57363E-02

    grade                  0.69701E-01      0.11861E-02

    not_smsa              -0.18494          0.62495E-02

    south                 -0.80056E-01      0.59837E-02

    union                  0.13725          0.66379E-02

    tenure                 0.32222E-01      0.67368E-03

    sigma                  0.37523

 

<S> mass 64

<S> fit black msp grade not_smsa south union tenure cons

 

    Initial Homogeneous Fit:

 

    Iteration       Log. lik.       Difference

    __________________________________________

        1          -8329.7229

 

 

    Iteration       Log. lik.         Step      End-points     Orthogonality

                                     length    0          1      criterion

    ________________________________________________________________________

        1          -9467.6528        1.0000    fixed  fixed       2046.9

        2          -8984.0889        1.0000    fixed  fixed       913.78

        3          -7780.0804        1.0000    fixed  fixed       2743.7

        4          -5809.8772        0.5000    fixed  fixed       17164.

        5          -5329.1065        0.5000    fixed  fixed      0.16462E+06

        6          -5128.8993        1.0000    fixed  fixed      0.12681E+06

        7          -4938.3977        1.0000    fixed  fixed      0.41150E+06

        8          -4871.7215        1.0000    fixed  fixed      0.34215E+06

        9          -4870.4148        1.0000    fixed  fixed      0.34370E+06

       10          -4870.4141        1.0000    fixed  fixed      0.35343E+06

       11          -4870.4141        1.0000    fixed  fixed

 

<S> dis m

 

    X-vars            Y-var             Case-var

    ________________________________________________

    cons              ln_wage           idcode

    black

    msp

    grade

    not_smsa

    south

    union

    tenure

 

    Univariate model

    Standard linear

    Gaussian random effects

 

    Number of observations             =   18995

    Number of cases                    =    4132

 

    X-var df           =     8

    Sigma df           =     1

    Scale df           =     1

 

    Log likelihood =     -4870.4141     on   18985 residual degrees of freedom

 

<S> dis e

 

    Parameter              Estimate         Std. Err.

    ___________________________________________________

    cons                   0.76119          0.27736E-01

    black                 -0.77839E-01      0.11567E-01

    msp                   -0.38486E-02      0.58172E-02

    grade                  0.72769E-01      0.20097E-02

    not_smsa              -0.14126          0.87666E-02

    south                 -0.74745E-01      0.85956E-02

    union                  0.11106          0.64703E-02

    tenure                 0.28474E-01      0.63994E-03

    sigma                  0.26019          0.15142E-02

    scale                  0.27966          0.40104E-02

 

<S> stop