In fixed effect model we usually add time dummies, is it necessary to add time dummies in random effect model also?
I would generally in a repeated measures random effects model include time in the fixed part to get the general trend over time; I usually do this as a polynomial of time so that in a reasonably in a long series it is in a a parsimonious form. if it was a short series I would consider discrete time as dummies.
You may be interested in these two papers
https://www.researchgate.net/publication/233756428_Explaining_Fixed_Effects_Random_Effects_Modeling_of_Time-Series_Cross-Sectional_and_Panel_Data
https://www.researchgate.net/publication/299604336_Fixed_and_Random_effects_making_an_informed_choice
Article Explaining Fixed Effects: Random Effects Modeling of Time-Se...
Article Fixed and Random effects models: making an informed choice
I would generally in a repeated measures random effects model include time in the fixed part to get the general trend over time; I usually do this as a polynomial of time so that in a reasonably in a long series it is in a a parsimonious form. if it was a short series I would consider discrete time as dummies.
You may be interested in these two papers
https://www.researchgate.net/publication/233756428_Explaining_Fixed_Effects_Random_Effects_Modeling_of_Time-Series_Cross-Sectional_and_Panel_Data
https://www.researchgate.net/publication/299604336_Fixed_and_Random_effects_making_an_informed_choice
Article Explaining Fixed Effects: Random Effects Modeling of Time-Se...
Article Fixed and Random effects models: making an informed choice
Dear Madan,
As Professor Jones mentioned, it is useful to consider time dummies to show the trend for your model.
However, it is not essential. In case, if including time in your model makes it complicated, it is possible to avoid it in your random effect model and consider it as a part of your estimation error.
A parsimonious and efficient way of including discrete time dummies in a longitudinal model is as orthogonal polynomials - these fit an underlying trend
For how to use OPs in random effects growth models see
https://www.researchgate.net/publication/234015221_Manual_supplement_for_MLwiN_Version_214
Don Hedeker's book uses the approach - it is very useful for fitting random slopes for time as the discreet time dummies will be orthogonal and parsimonious
http://hedeker.people.uic.edu/ml.html
Hedeker, D., and R. D. Gibbons. 2006. Longitudinal Data Analysis. Wiley-Interscience.
Data Manual supplement for MLwiN Version 2.14
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