I am estimating a logistic regression on a subset of my data and predicting the outcome for the whole sample.

1. In the regression only some data are statistically significant at the chosen level. **Should I include only those regressors in the prediction or all of them?**
2. I cannot chose my preferred model specification as it includes years Fixed Effects and there is no perfect overlapping year-wise between the samples. **Should I just go for my second-best specification without the Fixed Effects?**
3. How do I go about the first point in `R`?

For reference, my code in `R` looks like this

```
db_tr <- data[data$group=="1",]
db\_pr <- data[data$group=="2",]

f <- formula(y ~ x1 + x2 + x2 + factor(year)
logit.training <- glm(f,family=binomial(link="logit"), data= db_tr)
logit.predict <- predict(logit.training, newdata= db_pr, type="response")

```