The article is dedicated to the systematic description of approaches to working with macroeconomic data used in econometric analysis. It focuses on key indicators reflecting the state of the economy: GDP, inflation, unemployment, government debt, stock indices, exports, and imports. The author examines their characteristics, differences in calculation methodologies, and issues of comparability between countries. Special attention is given to how data quality affects the accuracy of econometric models and the interpretation of results. The paper analyzes typical difficulties arising in the search, collection, and preparation of data: varying publication frequencies, the presence of gaps, revisions of statistics, structural breaks, and inconsistencies in methodologies. The research aims to develop a comprehensive approach that enhances the reliability of time series and ensures the correctness of subsequent analysis. Using data from Russia and the USA as an example, differences in the depth of historical series, methodological stability, and volatility of indicators are demonstrated, highlighting the need for formalized data preparation procedures. The paper employs methods for searching, verifying the quality, and preprocessing macroeconomic data, including unifying frequency, interpolation, logarithmization, differencing, seasonal adjustment, and outlier detection. Visual analytical tools used include time series, histograms, correlation matrices, and seasonal profiles. The scientific novelty of the work lies in the formation of a universal and consistent methodology for preparing macroeconomic data for econometric analysis. The author proposes criteria for selecting indicators among their functional counterparts, considering the statistical properties of time series, methodological robustness, and sensitivity to revisions. It is shown that the use of derived indicators, unification of frequency, and proper treatment of gaps significantly improve data quality. The differences in economic structure and statistical tradition between Russia and the USA illustrate how they influence the interpretation of indicators and the choice of analysis methods. Visualization of time series and assessment of their properties allow for the identification of hidden patterns, structural breaks, and seasonal effects. The outcome of the research is a holistic approach that ensures reproducibility of results, reduces statistical distortions, and enhances the reliability of econometric models.
Denis Konstantinovich Bezdomov (Sun,) studied this question.