Hi Everyone!
I'm using stata 15.0 on Mac
I'm trying to do a mediation analysis in STATA with a panel data set. SEM or GSEM does not take into consideration that it is panel data, it uses a pooled regression, hence I'm looking for an analysis that does take the panel structure into consideration.
It includes 41 countries and the time range is 1995 until 2018, the IV is 'LME' which is the only variable which does not change over time, it can only have the values 0 or 1. The MV is 'CVC' which is panel data and the DV is 'market concentration' which is panel data as well. I've tried SEM and GSEM but both do not seem right for panel data as it does not consider the panel structure.
Small part of the data:
.gsem (cvc <- lme)(marketcon <- cvc lme)
Iteration 0: log likelihood = -8753.5826
Iteration 1: log likelihood = -8753.5826
Generalized structural equation model Number of obs = 984
Response : cvc Number of obs = 984
Family : Gaussian
Link : identity
Response : marketcon Number of obs = 748
Family : Gaussian
Link : identity
Log likelihood = -8753.5826
----------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
cvc |
lme | 2248.843 231.6727 9.71 0.000 1794.773 2702.913
_cons | 59.60339 88.62546 0.67 0.501 -114.0993 233.3061
-----------------+----------------------------------------------------------------
marketcon |
cvc | 3.34e-06 2.54e-06 1.31 0.189 -1.65e-06 8.32e-06
lme | -.0554207 .0163841 -3.38 0.001 -.087533 -.0233085
_cons | .2567047 .005838 43.97 0.000 .2452624 .268147
-----------------+----------------------------------------------------------------
var(e.cvc)| 6597756 297449.8 6039780 7207280
var(e.marketcon)| .0218785 .0011313 .0197699 .0242121
----------------------------------------------------------------------------------
. sem (cvc <- lme)(marketcon <- cvc lme)
(236 observations with missing values excluded)
Endogenous variables
Observed: cvc marketcon
Exogenous variables
Observed: lme
Fitting target model:
Iteration 0: log likelihood = -6698.2382
Iteration 1: log likelihood = -6698.2382
Structural equation model Number of obs = 748
Estimation method = ml
Log likelihood = -6698.2382
---------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
Structural |
cvc |
lme | 2079.756 223.0174 9.33 0.000 1642.65 2516.862
_cons | 23.62279 83.95391 0.28 0.778 -140.9238 188.1694
--------------+----------------------------------------------------------------
marketcon |
cvc | 3.34e-06 2.54e-06 1.31 0.189 -1.65e-06 8.32e-06
lme | -.0554207 .0163841 -3.38 0.001 -.087533 -.0233085
_cons | .2567047 .005838 43.97 0.000 .2452624 .268147
----------------+----------------------------------------------------------------
var(e.cvc)| 4524982 233981.2 4088860 5007621
var(e.marketcon)| .0218785 .0011313 .0197699 .0242121
---------------------------------------------------------------------------------
Note: The LR test of model vs. saturated is not reported because the fitted
model is not full rank.
I expected it to use the panel structure, which is 41 countries, but it does not. It seems to interpret every observation separately.
Hence my question is: What analysis should I use for a mediation analysis with panel data? I only want to do the mediation analysis for all the countries which have a value of 1 for LME.
Thank you in advance!
Kind regards,
Tim Winters
I'm using stata 15.0 on Mac
I'm trying to do a mediation analysis in STATA with a panel data set. SEM or GSEM does not take into consideration that it is panel data, it uses a pooled regression, hence I'm looking for an analysis that does take the panel structure into consideration.
It includes 41 countries and the time range is 1995 until 2018, the IV is 'LME' which is the only variable which does not change over time, it can only have the values 0 or 1. The MV is 'CVC' which is panel data and the DV is 'market concentration' which is panel data as well. I've tried SEM and GSEM but both do not seem right for panel data as it does not consider the panel structure.
Small part of the data:
| nr | id | year | cvc | marketcon | lme |
| 3 | Belgium | 1995 | 0 | . | 0 |
| 3 | Belgium | 1996 | 0 | 0,1 | 0 |
| 3 | Belgium | 1997 | 0 | 0,1 | 0 |
| 3 | Belgium | 1998 | 0 | 0,18 | 0 |
| 3 | Belgium | 1999 | 0 | 0,13 | 0 |
| 3 | Belgium | 2000 | 0 | 0,12 | 0 |
| 3 | Belgium | 2001 | 0 | 0,11 | 0 |
| 3 | Belgium | 2002 | 0 | 0,12 | 0 |
| 3 | Belgium | 2003 | 0 | 0,14 | 0 |
| 3 | Belgium | 2004 | 20,7 | 0,14 | 0 |
| 3 | Belgium | 2005 | 0 | 0,09 | 0 |
| 3 | Belgium | 2006 | 91,1 | 0,14 | 0 |
| 3 | Belgium | 2007 | 38,1 | 0,09 | 0 |
| 3 | Belgium | 2008 | 12,8 | 0,07 | 0 |
| 3 | Belgium | 2009 | 15,8 | 0,11 | 0 |
| 3 | Belgium | 2010 | 27,7 | 0,18 | 0 |
| 3 | Belgium | 2011 | 14,2 | 0,17 | 0 |
| 3 | Belgium | 2012 | 58,6 | 0,15 | 0 |
| 3 | Belgium | 2013 | 33 | 0,2 | 0 |
| 3 | Belgium | 2014 | 52,6 | 0,11 | 0 |
| 3 | Belgium | 2015 | 10,8 | 0,17 | 0 |
| 3 | Belgium | 2016 | 82 | . | 0 |
| 3 | Belgium | 2017 | 58,3 | . | 0 |
| 3 | Belgium | 2018 | 195,3 | . | 0 |
| 4 | Canada | 1995 | 0 | . | 1 |
| 4 | Canada | 1996 | 0 | 0,13 | 1 |
| 4 | Canada | 1997 | 0 | 0,15 | 1 |
| 4 | Canada | 1998 | 0 | 0,15 | 1 |
| 4 | Canada | 1999 | 487,1 | 0,15 | 1 |
| 4 | Canada | 2000 | 17 | 0,17 | 1 |
| 4 | Canada | 2001 | 72,8 | 0,21 | 1 |
| 4 | Canada | 2002 | 3,5 | 0,14 | 1 |
| 4 | Canada | 2003 | 28,2 | 0,18 | 1 |
| 4 | Canada | 2004 | 0 | 0,19 | 1 |
| 4 | Canada | 2005 | 11,9 | 0,17 | 1 |
| 4 | Canada | 2006 | 49,3 | 0,15 | 1 |
| 4 | Canada | 2007 | 91,5 | 0,19 | 1 |
| 4 | Canada | 2008 | 55 | 0,22 | 1 |
| 4 | Canada | 2009 | 102,2 | 0,17 | 1 |
| 4 | Canada | 2010 | 103,3 | 0,38 | 1 |
| 4 | Canada | 2011 | 288,4 | 0,43 | 1 |
| 4 | Canada | 2012 | 253,9 | 0,39 | 1 |
| 4 | Canada | 2013 | 136,1 | 0,39 | 1 |
| 4 | Canada | 2014 | 321,4 | 0,35 | 1 |
| 4 | Canada | 2015 | 531 | 0,39 | 1 |
| 4 | Canada | 2016 | 848,9 | . | 1 |
| 4 | Canada | 2017 | 917 | . | 1 |
| 4 | Canada | 2018 | 1270 | . | 1 |
.gsem (cvc <- lme)(marketcon <- cvc lme)
Iteration 0: log likelihood = -8753.5826
Iteration 1: log likelihood = -8753.5826
Generalized structural equation model Number of obs = 984
Response : cvc Number of obs = 984
Family : Gaussian
Link : identity
Response : marketcon Number of obs = 748
Family : Gaussian
Link : identity
Log likelihood = -8753.5826
----------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
cvc |
lme | 2248.843 231.6727 9.71 0.000 1794.773 2702.913
_cons | 59.60339 88.62546 0.67 0.501 -114.0993 233.3061
-----------------+----------------------------------------------------------------
marketcon |
cvc | 3.34e-06 2.54e-06 1.31 0.189 -1.65e-06 8.32e-06
lme | -.0554207 .0163841 -3.38 0.001 -.087533 -.0233085
_cons | .2567047 .005838 43.97 0.000 .2452624 .268147
-----------------+----------------------------------------------------------------
var(e.cvc)| 6597756 297449.8 6039780 7207280
var(e.marketcon)| .0218785 .0011313 .0197699 .0242121
----------------------------------------------------------------------------------
. sem (cvc <- lme)(marketcon <- cvc lme)
(236 observations with missing values excluded)
Endogenous variables
Observed: cvc marketcon
Exogenous variables
Observed: lme
Fitting target model:
Iteration 0: log likelihood = -6698.2382
Iteration 1: log likelihood = -6698.2382
Structural equation model Number of obs = 748
Estimation method = ml
Log likelihood = -6698.2382
---------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
Structural |
cvc |
lme | 2079.756 223.0174 9.33 0.000 1642.65 2516.862
_cons | 23.62279 83.95391 0.28 0.778 -140.9238 188.1694
--------------+----------------------------------------------------------------
marketcon |
cvc | 3.34e-06 2.54e-06 1.31 0.189 -1.65e-06 8.32e-06
lme | -.0554207 .0163841 -3.38 0.001 -.087533 -.0233085
_cons | .2567047 .005838 43.97 0.000 .2452624 .268147
----------------+----------------------------------------------------------------
var(e.cvc)| 4524982 233981.2 4088860 5007621
var(e.marketcon)| .0218785 .0011313 .0197699 .0242121
---------------------------------------------------------------------------------
Note: The LR test of model vs. saturated is not reported because the fitted
model is not full rank.
I expected it to use the panel structure, which is 41 countries, but it does not. It seems to interpret every observation separately.
Hence my question is: What analysis should I use for a mediation analysis with panel data? I only want to do the mediation analysis for all the countries which have a value of 1 for LME.
Thank you in advance!
Kind regards,
Tim Winters
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