A Statistical Analysis of the Migration Process: A Case Study-Romania

The research aims at studying and predicting the migration process in Romania over the last 20 years and at identifying the impact of the COVID-19 pandemic. The study analyzes several models for estimating migration through linear regression, but also a VAR (Vector autoregression) analysis, as the variables can influence each other. Vector autoregression (VAR) is also used to model multivariate time series, and it can analyze the dynamics of a migration process. Therefore, the best model for forecasting the migration process in Romania is Model 1 of linear regression. This phenomenon generates many positive and negative economic, demographic and political effects. The migration process has become particularly important for Romania in the last 20 years, and its socio-economic, political and cultural effects affect the Romanian state. That is why flexible policies are needed in order to be coherent, to have as main purpose keeping specialists in the country in certain basic economic fields, as well to implement measures to determine the return of specialists and students who have left to study abroad.

Automated summary

This research aims to study and predict the migration process in Romania over the past 20 years, as well as identify the impact of the COVID-19 pandemic. The study analyzes various models, including linear regression and VAR analysis, to estimate migration and determine the best model for forecasting in Romania. The migration process has had significant economic, demographic, and political effects on Romania, making flexible policies necessary to retain specialists and encourage their return.