Panel Data Disadvantages

Panel data in general is obtained through a longitudinal study, when the same entities (e.g. individuals, companies, countries) are observed in time. The values of the variables of interest are registered for several time periods or at several time points for each individual. Thus, the panel dataset consists of both time series and cross-sectional data. Practice shows that panel data has an extensive use in biological and social sciences. There are considerable advantages of using panel data as opposed to using only time series or only cross-sectional data. They are extensively addressed by Frees (2004). The additional information that the panel data provides allows for more accurate estimations. The panel data estimation methods require less assumptions and is often less problematic than simpler methods. They combine the values of using both cross sectional data and time series data and add further benefit in terms of problem-solving.
One advantage of using panel data is the use of individual-specific components in the models. For example a linear regression model of k factors can be expressed in the following way: y_it=β_0+α_i+β_1 x_(1,it)+β_2 x_(2,it)+⋯+β_k x_(k,it)+ε_it ( 1 ) where α_i is

In the classical time series analysis some methods require series of at least 30 observations and that can be a drawback for two reasons: one is the availability of data for so many consecutive time periods and the second is that sometimes it is unreasonable to use the same model for describing data in a very long period of time. In panel data the model can be more easily inferred by making observations on the series for all the individuals. By finding the common motif among the individuals, one can construct a model accurately without having to rely on very long series. The available data across individuals compensates for the shorter