4

I am trying to create a table of regressions using the Stargazer package in R. I have several regressions that differ only in the dummy variables. I want it to report the coefficient of the independent variable, the constant, etc., and to say "yes" or "no" if certain fixed effects (i.e., dummy variables) were included in the regression. These are my regressions:

iv1 <- ivreg(data=merge1,log(total_units)~log(priceIndex)|log(taxIndex))
iv2 <- ivreg(data=merge1,log(total_units)~log(priceIndex)+factor(fips_state_code)|log(taxIndex)+factor(fips_state_code))
iv4 <- ivreg(data=merge1,log(total_units)~log(priceIndex)+factor(fips_state_code) +factor(year)|log(taxIndex)+factor(fips_state_code) +factor(year))
iv5 <- ivreg(data=merge1,log(total_units)~log(priceIndex)+factor(fips_state_code) +time*factor(fips_state_code)|log(taxIndex)+factor(fips_state_code) +time*factor(fips_state_code))

(The data frame code is at the bottom, by the way.)

As you can see, iv1 has no dummies. iv2 has state dummies. iv4 has state and year dummies. iv5 has state dummies and time trend dummies.

Instead of reporting the betas of all these dummies, I would like for the regression to simply report whether each dummy was included. For some reason I can get this to work for each individual regression using Stargazer, as such:

> stargazer(iv1,type="text",
+           omit = c("fips_state_code","year","time"),
+           omit.labels = c("State FE?","Year FE?","State time trend?"))

===============================================
                        Dependent variable:    
                    ---------------------------
                         log(total_units)      
-----------------------------------------------
log(priceIndex)                1.146           
                              (1.481)          

Constant                      -0.283           
                              (3.576)          

-----------------------------------------------
State FE?                       No             
Year FE?                        No             
State time trend?               No             
-----------------------------------------------
Observations                    189            
R2                            -1.347           
Adjusted R2                   -1.359           
Residual Std. Error      1.297 (df = 187)      
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01
> 
> stargazer(iv2,type="text",
+           omit = c("fips_state_code","year","time"),
+           omit.labels = c("State FE?","Year FE?","State time trend?"))

===============================================
                        Dependent variable:    
                    ---------------------------
                         log(total_units)      
-----------------------------------------------
log(priceIndex)                1.184           
                              (1.561)          

Constant                      -0.495           
                              (3.767)          

-----------------------------------------------
State FE?                       Yes            
Year FE?                        No             
State time trend?               No             
-----------------------------------------------
Observations                    189            
R2                            -1.130           
Adjusted R2                   -1.487           
Residual Std. Error      1.332 (df = 161)      
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01
> 
> stargazer(iv4,type="text",
+           omit = c("fips_state_code","year","time"),
+           omit.labels = c("State FE?","Year FE?","State time trend?"))

===============================================
                        Dependent variable:    
                    ---------------------------
                         log(total_units)      
-----------------------------------------------
log(priceIndex)                0.845           
                              (1.049)          

Constant                       0.342           
                              (2.619)          

-----------------------------------------------
State FE?                       Yes            
Year FE?                        Yes            
State time trend?               No             
-----------------------------------------------
Observations                    189            
R2                            -0.393           
Adjusted R2                   -0.690           
Residual Std. Error      1.098 (df = 155)      
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01
> 
> stargazer(iv5,type="text",
+           omit = c("fips_state_code","year","time"),
+           omit.labels = c("State FE?","Year FE?","State time trend?"))

===============================================
                        Dependent variable:    
                    ---------------------------
                         log(total_units)      
-----------------------------------------------
log(priceIndex)                0.554           
                              (1.064)          

Constant                       0.041           
                              (2.393)          

-----------------------------------------------
State FE?                       Yes            
Year FE?                        No             
State time trend?               Yes            
-----------------------------------------------
Observations                    189            
R2                            -0.001           
Adjusted R2                   -0.405           
Residual Std. Error      1.001 (df = 134)      
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01

However, things get weird when I try to do multiple regressions at once:

> stargazer(iv1,iv2,iv4,iv5,type="text",
+           omit = c("fips_state_code","year","time"),
+           omit.labels = c("State FE?","Year FE?","State time trend?"))

=======================================================================================
                                            Dependent variable:                        
                    -------------------------------------------------------------------
                                             log(total_units)                          
                          (1)              (2)              (3)              (4)       
---------------------------------------------------------------------------------------
log(priceIndex)          1.146            1.184            0.845            0.554      
                        (1.481)          (1.561)          (1.049)          (1.064)     

Constant                 -0.283           -0.495           0.342            0.041      
                        (3.576)          (3.767)          (2.619)          (2.393)     

---------------------------------------------------------------------------------------
State FE?                  No               No               No               No       
Year FE?                   No               No               No               No       
State time trend?          No               No               No               No       
---------------------------------------------------------------------------------------
Observations              189              189              189              189       
R2                       -1.347           -1.130           -0.393           -0.001     
Adjusted R2              -1.359           -1.487           -0.690           -0.405     
Residual Std. Error 1.297 (df = 187) 1.332 (df = 161) 1.098 (df = 155) 1.001 (df = 134)
=======================================================================================
Note:                                                       *p<0.1; **p<0.05; ***p<0.01

Notice how all of the dummies are reported as "no" now. It seems like the usage of iv1, with no dummies, throws off Stargazer. I'm not sure why this is the case!

So, my question is: How do I get the combined Stargazer output to look like this?

=======================================================================================
                                                Dependent variable:                        
                        -------------------------------------------------------------------
                                                 log(total_units)                          
                              (1)              (2)              (3)              (4)       
    ---------------------------------------------------------------------------------------
    log(priceIndex)          1.146            1.184            0.845            0.554      
                            (1.481)          (1.561)          (1.049)          (1.064)     

    Constant                 -0.283           -0.495           0.342            0.041      
                            (3.576)          (3.767)          (2.619)          (2.393)     

    ---------------------------------------------------------------------------------------
    State FE?                  No               Yes              Yes              Yes      
    Year FE?                   No               No               Yes              No       
    State time trend?          No               No               No               Yes      
    ---------------------------------------------------------------------------------------
    Observations              189              189              189              189       
    R2                       -1.347           -1.130           -0.393           -0.001     
    Adjusted R2              -1.359           -1.487           -0.690           -0.405     
    Residual Std. Error 1.297 (df = 187) 1.332 (df = 161) 1.098 (df = 155) 1.001 (df = 134)
    =======================================================================================
    Note:                                                       *p<0.1; **p<0.05; ***p<0.01

I know this seems like a silly problem. But I am trying to do this for a lot more regressions, and manually formatting it each time is a HUGE pain in the neck. Any and all advice would be helpful! Thanks.

And here's my data:

structure(list(year = c(2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 
2006L, 2006L, 2006L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 
2007L, 2007L, 2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 
2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 
2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 
2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 
2009L, 2009L, 2009L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 
2010L, 2010L, 2010L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L), fips_state_code = c(4, 5, 6, 8, 9, 10, 
11, 12, 13, 17, 18, 21, 22, 24, 25, 27, 29, 31, 32, 34, 35, 36, 
38, 45, 46, 48, 55, 4, 5, 6, 8, 9, 10, 11, 12, 13, 17, 18, 21, 
22, 24, 25, 27, 29, 31, 32, 34, 35, 36, 38, 45, 46, 48, 55, 4, 
5, 6, 8, 9, 10, 11, 12, 13, 17, 18, 21, 22, 24, 25, 27, 29, 31, 
32, 34, 35, 36, 38, 45, 46, 48, 55, 4, 5, 6, 8, 9, 10, 11, 12, 
13, 17, 18, 21, 22, 24, 25, 27, 29, 31, 32, 34, 35, 36, 38, 45, 
46, 48, 55, 4, 5, 6, 8, 9, 10, 11, 12, 13, 17, 18, 21, 22, 24, 
25, 27, 29, 31, 32, 34, 35, 36, 38, 45, 46, 48, 55, 4, 5, 6, 
8, 9, 10, 11, 12, 13, 17, 18, 21, 22, 24, 25, 27, 29, 31, 32, 
34, 35, 36, 38, 45, 46, 48, 55, 4, 5, 6, 8, 9, 10, 11, 12, 13, 
17, 18, 21, 22, 24, 25, 27, 29, 31, 32, 34, 35, 36, 38, 45, 46, 
48, 55), priceIndex = c(8L, 16L, 25L, 27L, 2L, 24L, 18L, 26L, 
26L, 26L, 20L, 15L, 1L, 10L, 30L, 11L, 12L, 18L, 17L, 23L, 23L, 
6L, 1L, 5L, 24L, 7L, 10L, 22L, 7L, 20L, 8L, 10L, 2L, 30L, 16L, 
27L, 21L, 14L, 21L, 13L, 16L, 11L, 11L, 7L, 22L, 21L, 30L, 2L, 
19L, 2L, 10L, 17L, 6L, 12L, 5L, 30L, 12L, 15L, 29L, 19L, 16L, 
16L, 22L, 9L, 10L, 9L, 10L, 19L, 22L, 6L, 16L, 24L, 25L, 24L, 
12L, 10L, 26L, 12L, 30L, 16L, 9L, 5L, 8L, 7L, 2L, 4L, 9L, 11L, 
16L, 10L, 13L, 23L, 1L, 10L, 9L, 10L, 2L, 17L, 6L, 15L, 5L, 18L, 
2L, 2L, 13L, 9L, 18L, 10L, 25L, 8L, 26L, 29L, 14L, 3L, 12L, 22L, 
15L, 22L, 14L, 13L, 27L, 4L, 16L, 20L, 12L, 19L, 12L, 20L, 12L, 
17L, 9L, 1L, 28L, 23L, 24L, 13L, 16L, 10L, 21L, 1L, 18L, 15L, 
1L, 15L, 23L, 5L, 16L, 27L, 8L, 7L, 5L, 20L, 3L, 3L, 7L, 3L, 
23L, 1L, 26L, 4L, 5L, 18L, 13L, 17L, 30L, 22L, 14L, 29L, 1L, 
1L, 23L, 12L, 14L, 21L, 29L, 2L, 2L, 16L, 21L, 15L, 11L, 29L, 
26L, 26L, 17L, 20L, 23L, 27L, 7L), totalWeight = c(0.964679717852504, 
0.910153114749701, 0.937533258307128, 0.908932907218257, 0.897870703904312, 
0.570664114467063, 0.793595725333603, 0.960149778439218, 0.702012263867207, 
0.959840103392019, 0.942220302688495, 0.964136166436202, 0.945368646478464, 
0.899686521142446, 0.874686707751765, 0.914447566897194, 0.952932668846809, 
0.960061052199137, 0.926259918197789, 0.885837510813906, 0.901475780845684, 
0.779591446248175, 0.604818428169235, 0.941410295398351, 0.908944873195851, 
0.940822410107144, 0.820433580971128, 0.955543163510268, 0.914685040312209, 
0.948635424851211, 0.946104114649245, 0.932230610899134, 0.558057546499175, 
0.750564479296488, 0.971764930983387, 0.68817373783927, 0.975097771312425, 
0.962368976746048, 0.970230629172812, 0.953507602894619, 0.892296298593537, 
0.930726885101312, 0.908546595974175, 0.962179609608759, 0.96839162884849, 
0.935106841280912, 0.897095564773418, 0.920053661608378, 0.820365371424697, 
0.646532974396383, 0.944743562870499, 0.911857926468439, 0.963635866793497, 
0.944584511990913, 0.973319999879543, 0.912794288563832, 0.950505538487169, 
0.947587097715066, 0.932230610899134, 0.585877063357753, 0.741854702451495, 
0.974829401211451, 0.691439730628336, 0.975813815364686, 0.960835846736876, 
0.961274083799183, 0.959334487143946, 0.89688427237274, 0.937723734431402, 
0.912751255497468, 0.971245010442592, 0.971456099076554, 0.941243932527261, 
0.898677051935661, 0.909199996904926, 0.904176820031607, 0.660962686468937, 
0.926016809434945, 0.927065572055749, 0.969462751042824, 0.887911658008384, 
0.974754164229651, 0.885875391195578, 0.958515313970186, 0.948823953012966, 
0.936466604521389, 0.613240721391053, 0.777793767761539, 0.981209274133896, 
0.706831562657967, 0.982459601639192, 0.969382100794866, 0.970450010303705, 
0.960978075054578, 0.902842393873445, 0.942890887235305, 0.905145032941613, 
0.985616404521002, 0.974335897510718, 0.94236227101429, 0.92257155375435, 
0.903566344156375, 0.905142965998554, 0.661175613077282, 0.948470597079574, 
0.937249077110803, 0.972342549476988, 0.966932959536049, 0.969719582376951, 
0.892634342170433, 0.964670562454497, 0.951929452222193, 0.93649537248916, 
0.612101928212217, 0.724332887315945, 0.980582527341166, 0.712928614791972, 
0.987189573702774, 0.974718254899991, 0.975852766090469, 0.96236303821044, 
0.899854848145425, 0.946343691677045, 0.911796075815032, 0.981805900102976, 
0.97572086066658, 0.940776475282425, 0.920956214063409, 0.918314213645145, 
0.909966039838214, 0.688692601749395, 0.939834970965504, 0.938634040266665, 
0.97372751263285, 0.96841594260187, 0.965125603615924, 0.872094653176646, 
0.974957711538891, 0.972050595493474, 0.933488903015909, 0.664724768281132, 
0.725532855017458, 0.982136493351554, 0.731583789519918, 0.986998917423862, 
0.985672785517343, 0.985359985268326, 0.96327016977471, 0.907456559706999, 
0.947841526350148, 0.924724066870382, 0.984805872685194, 0.974845207727776, 
0.956650623685199, 0.927323325078334, 0.928141500916387, 0.912472003821784, 
0.718170802590407, 0.935947208560755, 0.946217508856548, 0.975281478643238, 
0.969969908612259, 0.97439813803871, 0.849645214769615, 0.971427658757611, 
0.972050595493474, 0.927830874535962, 0.655478629719111, 0.734298949581601, 
0.984919482876493, 0.737396852851197, 0.988375665649713, 0.978252656267413, 
0.978204861100427, 0.961122141972513, 0.941660644201143, 0.953036993924037, 
0.925681643545421, 0.990001340259083, 0.969788001954067, 0.94817860131528, 
0.928318571162957, 0.927885380703944, 0.913542321320878, 0.825157348433747, 
0.948727363244703, 0.948225380163735, 0.975281478643238, 0.971354871768121
), taxIndex = c(14L, 4L, 4L, 19L, 15L, 18L, 12L, 12L, 14L, 7L, 
10L, 28L, 29L, 30L, 14L, 3L, 23L, 10L, 26L, 15L, 26L, 21L, 29L, 
4L, 22L, 23L, 16L, 5L, 4L, 25L, 7L, 6L, 10L, 16L, 25L, 6L, 13L, 
25L, 18L, 7L, 14L, 27L, 27L, 17L, 6L, 4L, 18L, 10L, 19L, 18L, 
14L, 12L, 19L, 21L, 23L, 5L, 6L, 28L, 28L, 21L, 10L, 30L, 18L, 
23L, 24L, 25L, 19L, 13L, 22L, 14L, 11L, 2L, 13L, 24L, 8L, 30L, 
12L, 13L, 4L, 3L, 1L, 21L, 7L, 8L, 30L, 3L, 7L, 14L, 10L, 23L, 
24L, 17L, 11L, 27L, 18L, 4L, 9L, 14L, 29L, 25L, 4L, 8L, 16L, 
3L, 28L, 2L, 2L, 28L, 28L, 5L, 7L, 30L, 30L, 6L, 24L, 1L, 28L, 
19L, 3L, 2L, 5L, 14L, 23L, 13L, 14L, 23L, 21L, 23L, 14L, 20L, 
21L, 25L, 27L, 30L, 5L, 15L, 27L, 3L, 4L, 15L, 1L, 12L, 9L, 17L, 
24L, 26L, 1L, 25L, 6L, 13L, 11L, 18L, 28L, 30L, 3L, 28L, 8L, 
11L, 11L, 8L, 25L, 11L, 4L, 20L, 1L, 14L, 3L, 15L, 2L, 11L, 1L, 
17L, 30L, 15L, 21L, 14L, 29L, 26L, 1L, 27L, 18L, 12L, 7L, 17L, 
4L, 30L, 23L, 1L, 27L), total_units = c(30L, 12L, 16L, 10L, 30L, 
6L, 8L, 24L, 15L, 6L, 6L, 16L, 15L, 19L, 28L, 16L, 7L, 13L, 12L, 
21L, 9L, 9L, 10L, 4L, 12L, 21L, 30L, 1L, 26L, 7L, 2L, 7L, 1L, 
2L, 15L, 14L, 11L, 28L, 29L, 2L, 22L, 26L, 9L, 21L, 8L, 26L, 
4L, 14L, 18L, 15L, 18L, 11L, 9L, 20L, 3L, 20L, 20L, 24L, 1L, 
9L, 16L, 27L, 29L, 2L, 25L, 16L, 24L, 13L, 11L, 13L, 1L, 19L, 
5L, 5L, 11L, 22L, 16L, 20L, 21L, 2L, 9L, 13L, 15L, 6L, 12L, 28L, 
7L, 24L, 22L, 24L, 21L, 14L, 1L, 6L, 10L, 10L, 26L, 26L, 3L, 
9L, 16L, 30L, 16L, 23L, 20L, 11L, 17L, 16L, 15L, 8L, 20L, 21L, 
1L, 19L, 4L, 4L, 26L, 21L, 18L, 18L, 24L, 8L, 17L, 15L, 20L, 
19L, 10L, 19L, 23L, 4L, 17L, 1L, 20L, 29L, 28L, 26L, 2L, 17L, 
22L, 17L, 17L, 14L, 17L, 13L, 1L, 3L, 15L, 5L, 30L, 27L, 20L, 
10L, 3L, 24L, 28L, 22L, 28L, 20L, 15L, 16L, 10L, 11L, 28L, 27L, 
12L, 5L, 19L, 11L, 15L, 26L, 15L, 27L, 6L, 25L, 7L, 8L, 29L, 
26L, 16L, 25L, 28L, 22L, 20L, 13L, 3L, 8L, 4L, 29L, 10L), time = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7)), .Names = c("year", 
"fips_state_code", "priceIndex", "totalWeight", "taxIndex", "total_units", 
"time"), row.names = c(NA, -189L), vars = list(year), drop = TRUE, indices = list(
    0:26, 27:53, 54:80, 81:107, 108:134, 135:161, 162:188), group_sizes = c(27L, 
27L, 27L, 27L, 27L, 27L, 27L), biggest_group_size = 27L, labels = structure(list(
    year = 2006:2012), class = "data.frame", row.names = c(NA, 
-7L), vars = list(year), drop = TRUE, .Names = "year"), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))
ejn
  • 415
  • 6
  • 16

2 Answers2

5

I had a similar problem with other model types and the thing is that the order the fixed effects appear matters.

If you simply flip the order the models:

stargazer(iv5,iv4,iv2,iv1,type="text",
       omit = c("fips_state_code","year","time"),
       omit.labels = c("State FE?","Year FE?","State time trend?"))

You get the correct output:

=======================================================================================
                                            Dependent variable:                        
                    -------------------------------------------------------------------
                                             log(total_units)                          
                          (1)              (2)              (3)              (4)       
---------------------------------------------------------------------------------------
log(priceIndex)          0.554            0.845            1.184            1.146      
                        (1.064)          (1.049)          (1.561)          (1.481)     

Constant                 0.041            0.342            -0.495           -0.283     
                        (2.393)          (2.619)          (3.767)          (3.576)     

---------------------------------------------------------------------------------------
State FE?                 Yes              Yes              Yes               No       
Year FE?                   No              Yes               No               No       
State time trend?         Yes               No               No               No       
---------------------------------------------------------------------------------------
Observations              189              189              189              189       
R2                       -0.001           -0.393           -1.130           -1.347     
Adjusted R2              -0.405           -0.690           -1.487           -1.359     
Residual Std. Error 1.001 (df = 134) 1.098 (df = 155) 1.332 (df = 161) 1.297 (df = 187)
=======================================================================================
Note:                                                       *p<0.1; **p<0.05; ***p<0.01
Florian
  • 61
  • 5
  • I didn't realize that works.. But unfortunately that's not very useful because I'd still have to manually change each table one by one to the correct set up. – ejn Apr 10 '16 at 02:28
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I simply don't want to print out all the dummies used in my regression and this question bothers for more than 3hs, and it's surprising to find it here.

I tried what Florian suggested and that works, actually, order of fixed effect appear in the regressions don't matter in my case, I'm running plm here, and below is my stargazer code:

stargazer(cluster.matched.fixed.7,cluster.matched.fixed.2,cluster.matched.fixed.3,cluster.matched.fixed.4,
          cluster.matched.fixed.5,cluster.matched.fixed.6,cluster.matched.fixed.1,title="Matched sample regression DID results",
            omit.stat = c("f"),covariate.labels=c("D","D1","D2","log(ROA)","log(totalasset)","log(sales)",
                             "log(GM)","log(Export)","log(Leverage)"),omit = c("year"),omit.labels = c("Year FE?"))

where regression 7 has no fixed effect and the result is correct. what's more interesting to me is that where you find the omit = 

c("fips_state_code","year","time"),
       omit.labels = c("State FE?","Year FE?","State time trend?")

arguments in "stargazer", I printed out the document from R-cran but there is nothing like that.

Jia Gao
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