elmanito
Reputation: 35
Panel data fixed effect issue with R, I don't see my all dummy variables in the summary
I'm running a data panel regression with R. I use those data(sheet 2).
I want to use a fixed effect for my panel data. Please, find below the code for my fixed effect
FE_1 <- plm(GDP_per_capita_growth ~
log(GDP_per_capita) + GF_GDP + MA_GDP +
start_business + Invest_GDP +
second_schooling + Pop_growth +
log(Inflation_CPI) + Trade +
GF_GDP * start_business +
factor(as.character(time_fixed_effect)) +
factor(as.character(regional)) +
factor(as.character(oil_exporting_countries)),
data = Temp_1,
index = c("country",
"year"),
na.action = na.omit,
model = "within")
When I run the summary for my panel data fixed effect, some variables are missing, such as time_fixed_effect, regional and oil_exporting_countries. Below, the result I got.
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
log(GDP_per_capita) -1.9676e+01 5.0218e+00 -3.9181 0.0001386 ***
GF_GDP 1.2637e+00 1.9705e+00 0.6413 0.5223695
MA_GDP 1.9337e+01 8.3736e+00 2.3093 0.0223807 *
start_business -1.3378e-07 1.0077e-07 -1.3276 0.1864403
Invest_GDP 1.5166e-08 1.1173e-08 1.3574 0.1768341
second_schooling -4.9808e-01 5.4186e+00 -0.0919 0.9268910
Pop_growth -1.6174e+00 5.9779e-01 -2.7055 0.0076606 **
log(Inflation_CPI) 1.3861e-01 3.2403e-01 0.4278 0.6694806
Trade 1.8669e-02 1.8617e-02 1.0028 0.3176852
factor(as.character(time_fixed_effect))1 3.8295e-01 4.4318e-01 0.8641 0.3890017
GF_GDP:start_business 5.0494e-07 3.4416e-07 1.4672 0.1445566
---
I would like to know, what should I need to do to display all my dummy variables. Is there an error somewhere in my code?
Instead of having index = c("country", "year"),, I wrote index = c("year", "country"),, and I got this following result:
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
log(GDP_per_capita) -3.3627e+00 2.0642e+00 -1.6291 0.105013
GF_GDP 1.2334e+00 1.8129e+00 0.6804 0.497127
MA_GDP 5.2312e-01 6.4419e+00 0.0812 0.935366
start_business 1.4314e-08 3.0105e-08 0.4755 0.635022
Invest_GDP -9.8744e-10 1.5174e-09 -0.6507 0.516025
second_schooling 1.0860e+00 1.0806e+00 1.0050 0.316205
Pop_growth -6.3753e-01 2.2690e-01 -2.8097 0.005494 **
log(Inflation_CPI) 1.4547e-01 2.5011e-01 0.5816 0.561550
Trade 9.2730e-04 3.8306e-03 0.2421 0.808991
factor(as.character(regional))2 -1.0668e+00 8.3584e-01 -1.2763 0.203441
factor(as.character(regional))3 -2.6186e-01 6.6220e-01 -0.3954 0.692972
factor(as.character(regional))4 1.4760e-01 7.9347e-01 0.1860 0.852639
factor(as.character(regional))5 1.3558e+00 7.2400e-01 1.8727 0.062696 .
factor(as.character(oil_exporting_countries))1 -1.8890e-01 4.4884e-01 -0.4209 0.674344
GF_GDP:start_business -2.9751e-08 1.3526e-07 -0.2199 0.826157
---
I don't know why I got this result! Can you help me to find out why, please? And also help me to display my "time_fixed-effect" variable, please.
Thank you in advance for your precious help.
PS: This is complete code
# Rename column names
colnames(my_data)[4] <- "Invest_GDP" # percentage
colnames(my_data)[9] <- "Pr_sector_GDP" # percentage
colnames(my_data)[12] <- "start_business"
colnames(my_data)[16] <- "second_schooling"
colnames(my_data)[18] <- "GDP_per_capita_growth"
# Transform column "inflation" as numeric
my_data$Inflation_CPI <- as.numeric(my_data$Inflation_CPI)
my_data$second_schooling <- as.numeric(my_data$second_schooling)
Temp_1 <- my_data %>%
select(
-region,
-Pr_sector_GDP,
-Prop_rights,
-T_freedom,
-current_invest
)
Upvotes: 2
Views: 4225
Answers (1)
paqmo
Reputation: 3729
The variables are not shown because of multi-collinearity--they are dropped just as @AntoniosK suggested. plm is not verbose and does not tell you this, but you can see it more clearly if you estimate the same model with lm (I have abridged the output a bit):
> summary(lm(GDP_per_capita_growth ~
+ log(GDP_per_capita) + GF_GDP + MA_GDP +
+ start_business + Invest_GDP +
+ second_schooling + Pop_growth +
+ log(Inflation_CPI) + Trade +
+ GF_GDP * start_business + factor(country) +
+ factor(time_fixed_effect) +
+ factor(regional) +
+ factor(oil_exporting_countries),
+ data = Temp_1))
Call:
lm(formula = GDP_per_capita_growth ~ log(GDP_per_capita) + GF_GDP +
MA_GDP + start_business + Invest_GDP + second_schooling +
Pop_growth + log(Inflation_CPI) + Trade + GF_GDP * start_business +
factor(country) + factor(time_fixed_effect) + factor(regional) +
factor(oil_exporting_countries), data = Temp_1)
Residuals:
Min 1Q Median 3Q Max
-6.263 -1.070 0.000 1.029 7.001
Coefficients: (5 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.609e+01 5.791e+00 4.506 1.38e-05 ***
log(GDP_per_capita) -1.968e+01 5.022e+00 -3.918 0.000139 ***
GF_GDP 1.264e+00 1.970e+00 0.641 0.522370
MA_GDP 1.934e+01 8.374e+00 2.309 0.022381 *
start_business -1.338e-07 1.008e-07 -1.328 0.186440
Invest_GDP 1.517e-08 1.117e-08 1.357 0.176834
second_schooling -4.981e-01 5.419e+00 -0.092 0.926891
Pop_growth -1.617e+00 5.978e-01 -2.706 0.007661 **
log(Inflation_CPI) 1.386e-01 3.240e-01 0.428 0.669481
Trade 1.867e-02 1.862e-02 1.003 0.317685
factor(country)Algeria 2.489e+00 2.448e+00 1.017 0.310886
...
factor(country)Vietnam -4.958e-01 2.338e+00 -0.212 0.832356
factor(time_fixed_effect)1 3.830e-01 4.432e-01 0.864 0.389002
factor(regional)2 NA NA NA NA
factor(regional)3 NA NA NA NA
factor(regional)4 NA NA NA NA
factor(regional)5 NA NA NA NA
factor(oil_exporting_countries)1 NA NA NA NA
GF_GDP:start_business 5.049e-07 3.442e-07 1.467 0.144557
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.383 on 141 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.5391, Adjusted R-squared: 0.3397
F-statistic: 2.703 on 61 and 141 DF, p-value: 6.858e-07
You get different results with your second plm model because it is a different model -- you are telling it that year is your entity fixed effect and so it is estimating a model with year dummies and not country dummies!
Again, using `lm`, here's what you are estimating:
> summary(lm(GDP_per_capita_growth ~
+ log(GDP_per_capita) + GF_GDP + MA_GDP +
+ start_business + Invest_GDP +
+ second_schooling + Pop_growth +
+ log(Inflation_CPI) + Trade +
+ GF_GDP * start_business + factor(year) +
+ factor(time_fixed_effect) +
+ factor(regional) +
+ factor(oil_exporting_countries),
+ data = Temp_1))
Call:
lm(formula = GDP_per_capita_growth ~ log(GDP_per_capita) + GF_GDP +
MA_GDP + start_business + Invest_GDP + second_schooling +
Pop_growth + log(Inflation_CPI) + Trade + GF_GDP * start_business +
factor(year) + factor(time_fixed_effect) + factor(regional) +
factor(oil_exporting_countries), data = Temp_1)
Residuals:
Min 1Q Median 3Q Max
-6.7279 -1.2924 -0.1109 1.2808 10.6906
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.032e+00 2.442e+00 3.699 0.000286 ***
log(GDP_per_capita) -3.363e+00 2.064e+00 -1.629 0.105013
GF_GDP 1.233e+00 1.813e+00 0.680 0.497127
MA_GDP 5.231e-01 6.442e+00 0.081 0.935366
start_business 1.431e-08 3.011e-08 0.475 0.635022
Invest_GDP -9.874e-10 1.517e-09 -0.651 0.516025
second_schooling 1.086e+00 1.081e+00 1.005 0.316205
Pop_growth -6.375e-01 2.269e-01 -2.810 0.005494 **
log(Inflation_CPI) 1.455e-01 2.501e-01 0.582 0.561550
Trade 9.273e-04 3.831e-03 0.242 0.808991
factor(year)2008 -2010 -9.536e-01 5.308e-01 -1.796 0.074056 .
factor(year)2011-2013 -1.449e+00 5.336e-01 -2.714 0.007269 **
factor(year)2014-2016 -2.261e+00 5.554e-01 -4.071 6.93e-05 ***
factor(time_fixed_effect)1 NA NA NA NA
factor(regional)2 -1.067e+00 8.358e-01 -1.276 0.203441
factor(regional)3 -2.619e-01 6.622e-01 -0.395 0.692972
factor(regional)4 1.476e-01 7.935e-01 0.186 0.852639
factor(regional)5 1.356e+00 7.240e-01 1.873 0.062696 .
factor(oil_exporting_countries)1 -1.889e-01 4.488e-01 -0.421 0.674344
GF_GDP:start_business -2.975e-08 1.353e-07 -0.220 0.826157
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.56 on 184 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.3063, Adjusted R-squared: 0.2384
F-statistic: 4.513 on 18 and 184 DF, p-value: 4.363e-08
Now, it is not clear what you are trying to do. It might help if you explain what you are trying to achieve with your analysis.
Do you want country and year fixed effects?
What exactly is the time_fixed_effect variable doing?
If you want country and year fixed effects, you need the argument effect = "twoway". By default plm specifies effect = "individual", which just estimates a entity fixed effects model.
FE_1 <- plm(GDP_per_capita_growth ~
log(GDP_per_capita) + GF_GDP + MA_GDP +
start_business + Invest_GDP +
second_schooling + Pop_growth +
log(Inflation_CPI) + Trade +
GF_GDP * start_business +
factor(as.character(regional)) +
factor(as.character(oil_exporting_countries)),
data = Temp_1,
index = c("country", "year"),
model = "within", effect = "twoway")
Upvotes: 1
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