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SCIENTIFIC RESEARCH - 2026
Statistical Analysis as a Tool for Assessing and Forecasting Economic Processes
Nanuli Khizanishvili 
Abstract. This article examines how statistical analysis supports the assessment and forecasting of economic processes and demonstrates why data definitions, revisions, and uncertainty must accompany numerical results. The study combines conceptual analysis with an illustrative case study of Georgia. Official annual unemployment data for 2021-2025, population releases, demographic indicators, and the Economic Analysis Portal of the National Statistics Office of Georgia are examined alongside methodological literature on descriptive statistics, econometrics, forecasting, and official data quality. The results show that Georgia's unemployment rate fell from 20.6 percent in 2021 to 13.9 percent in 2025, but the decline was uneven and stopped in the final year. A linear time-trend fitted to only five observations yields a 2026 point estimate of 11.38 percent and a wide 95 percent prediction interval of 6.99-15.77 percent; it is therefore presented as a diagnostic benchmark, not a policy forecast. The population case reveals an additional risk: revisions following the 2024 census materially changed the historical series, making unrevised figures unsuitable for simple extrapolation. The article concludes that credible economic assessment requires reproducible calculations, metadata, revision awareness, alternative scenarios, and explicit communication of uncertainty.
Keywords: statistical analysis, economic forecasting, unemployment, data revision, time series, official statistics, Georgia
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