Interpreting categorical variable when fixed effect model in 'plm' has no intercept

Question

I run my panel regression using 'plm' command with fixed effect 'within' option. The dependent variable is numerical while all the independent variables are either categorical or binary except cgi, eui, sjump and rv.

The three binary variables cc, ce, cw are supposed to represent the same categorical variable with 4 categories so I excluded one.

However, when I try to analyze the effect of this fourth category from these three binary variables representing 4 categories, I have difficulty since this fixed effect model does not give out intercept that I can use to get the effect of this fourth categorical variable where I have to set everything zeros.

May I have help on this?

 Oneway (individual) effect Within Model

 Call:
 plm(formula = liq ~ gov + indus + fin + cc * us + ce * us + cw * 
     us + cgi + eui + sjump + rv, data = DATA, model = "within", 
     index = c("year", "id"))

 Unbalanced Panel: n=20, T=1-5, N=56

 Residuals :
        Min.     1st Qu.      Median     3rd Qu.        Max. 
 -0.00261615 -0.00032955  0.00000000  0.00054460  0.00355775 

 Coefficients :
          Estimate  Std. Error t-value  Pr(>|t|)    
 gov    2.6297e-03  9.3741e-04  2.8053 0.0100474 *  
 indus  1.2222e-03  5.6817e-04  2.1511 0.0422088 *  
 fin    1.8561e-04  1.1653e-03  0.1593 0.8748340    
 cc    -4.9511e-03  2.2878e-03 -2.1641 0.0410807 *  
 us    -4.1023e-03  5.4593e-04 -7.5143 1.235e-07 ***
 ce    -5.6131e-03  2.2753e-03 -2.4669 0.0215034 *  
 cw    -5.1635e-03  2.5178e-03 -2.0508 0.0518462 .  
 cgi   -1.5682e-03  3.4829e-04 -4.5026 0.0001608 ***
 eui    1.1203e-06  4.1869e-05  0.0268 0.9788832    
 sjump -1.8977e-03  7.2104e-03 -0.2632 0.7947520    
 rv    -2.7708e+00  5.6234e+00 -0.4927 0.6268798    
 cc:us  1.2738e-03  2.4844e-03  0.5127 0.6130542    
 us:ce  6.6711e-04  2.5962e-03  0.2570 0.7994964    
 ---
 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

 Total Sum of Squares:    0.00052049
 Residual Sum of Squares: 5.7059e-05
 R-Squared:      0.89037
 Adj. R-Squared: 0.73785
 F-statistic: 14.3696 on 13 and 23 DF, p-value: 4.5827e-08

Answer 1

First of all: sorry, this should be a comment, but I can't comment since I have few points and badges.

Have you tried to use within_intercept() function? "This function gives an overall intercept for within models and its accompanying standard error"

if you go to the plm manual there are some examples (page 117) (https://cran.r-project.org/web/packages/plm/plm.pdf):

gi <- plm(inv ~ value + capital, data = Grunfeld, model = "within")
fx_level <- fixef(gi, type = "level")
fx_dmean <- fixef(gi, type = "dmean")
overallint <- within_intercept(gi)
all.equal(overallint + fx_dmean, fx_level, check.attributes = FALSE) # TRUE
# overall intercept with robust standard error
within_intercept(gi, vcov = function(x) vcovHC(x, method="arellano", type="HC0"))