Does time fixed effect always have to be considered in panel regression analysis? This question might sound very silly, but I haven't found a clear answer so far.
Suppose you are working with a panel including several time series data for different countries/cities/firms: when analyzing the panel in a regression is it somehow mandatory to consider time and countries/cities/firms fixed effects?
If this is not the case, what makes you decide when these have to be considered or not? 
Thanks
 A: Please, consider that this is not THE way to go, it is just a quick roadmap to start analyzing panel data. A more rigorous procedure should be tested for the specific case of the user and supported by the literature.
After studying a bit more in detail the topic, although there is not a standardized procedure to apply to these analyses, a sequential approach that could be adopted is the following (ref and more info in this website and this video):
(example code in R using the plm package)


*

*clean the panel data and set up the panel analysis (not explained here);

*Estimate a simple OLS model
OLS<-plm(Y ~ X, data = my_panel, model = "pooling")


*Estimate a random effect model
random<-plm(Y ~ X, data = my_panel, model = "random")


*Estimate a fixed effect model
fixed<-plm(Y ~ X, data = my_panel, model = "within")


*Test the difference between the models
# LM test for random effects versus OLS
plmtest(OLS)

if the p-values is small enough, it will indicate alternative hypothesis: significant effects, then opt for a random effect model.
    # LM test for fixed effects versus OLS
    pFtest(fixed, OLS)

if the p-values is small enough, it will indicate alternative hypothesis: significant effects, then opt for a fixed effect model. 
    # Hausman test for fixed versus random effects model
     phtest(random, fixed)

if the test suggests alternative hypothesis: one model is inconsistent, then it would be more appropriate to go for a fixed effect model.
