Introduction Panel data is a two-dimensional array in which one dimension is “spatial” (1 < i <N) and the other is temporal (1 < t <T). Thus, panel data has two indices (i, t). Similar arrays occur when collecting data on a given set of objects over a period (Mummolo & Peterson, 2018). For example, when conducting a survey, there may be “exhaustion” of the sample: someone moved, someone died, someone refused to participate in the survey. In general, the panel data regression follows: x it = Z it α it + ε it , i = 1,…, N; t = 1,…, T, where i is the index, t is time, αit is the vector coefficient of the unifying variables Zit in period t for the sample unit i (Xu et al., 2018). Importance of Panel Data Regression The main feature of static-dynamic information is the dependence of observations. Dependency is not only the levels of the dynamic series but also the rows (both spatial and temporal) since the membership of the standards to a row is fixed (Zyphur et al., 2019). Yet, the dependence between the series of dynamics is the result of spatial variation, which, because of the inertia of the processes, is stored for a specific time (Das, 2019). Spatial row dependency reflects the synchronous dynamics of the metrics across individual objects, driven by common development conditions. Ignoring these features of the database model leads to erroneous conclusions. In panel data over a long period, you can identify trends in the development of individual objects and the totality. When the objects are differentiating, but the individual features are stable over the observation period, a fixed-effect model is applied to such an information base (Shi & Lee, 2016). The specific set of such a model is supplemented by the dummy variables of the individual objects u and j, the regression coefficients for these variables characterize the special effects (Zyphur et al., 2019). The pattern of the function y inherent in all objects is considered in the model by the variable t (Okui & Yanagi, 2019). However, due to the uneven development of individual aggregates along with a common trend, different trends may appear significant. Therefore, object periods only extend the model’s information base. Types of Panel Data Regression POLS (pooled ordinary least square) POLS, because of its broad scope, holds an exceptional place among mathematical statistics methods. The POLS task is to evaluate the patterns observed in the background of random fluctuations and to use them for further calculations for forecasts (Ott & Longnecker, 2016). A unique role is played by POLS in geophysics, determining the concept and methodology of solving the inverse problem of geophysics (Balazsi et al., 2015). The POLS problem is solved by parametric estimation of the regression function, which describes the dependence of one quantity Y, whose values (y i ) are observed with random errors (θ i ), on the group of non-random variables X 1 , X 2 ,…, X k The regression function is a function of k variables x 1 , x 2 ,…, x k , which is the mathematical expectation of Y at X 1 = x 1 , X 2 = x 2 ,…, X k = x k : f (x 1 , x 2 ,…,x k ) = M (Y/X 1 =x 1 , X 2 =x 2 ,…,X k = x k ). Fixed-Effect Model In the fixed-effects model, the unobservable individual effects were correlated with the variables included in the model (Galvao et al., 2020). However, sometimes there are reasons to believe that special effects may not