The rapid development of digital technologies has changed the way data is collected, stored, and analyzed. Big Data has created new opportunities and challenges for econometric research. This article discusses the integration of econometric methods with big data analysis, the methodological innovations needed, and the results of empirical economic research. The study also highlights modern tools and ideas that help econometricians manage the complexity and scale of large data sets while maintaining model accuracy and interpretability. It also highlights how the combination of traditional econometric thinking and computational methods can improve the quality and scope of economic analysis in the modern digital economy
| Mualliflar | Khudoykulov, Said, Худойкулов, Саид, Xudoykulov, Said |
|---|---|
| Jurnal | Илғор иқтисодиёт ва педагогик технологиялар |
| Nashr sanasi | 2025-11-04 |
| Jild | 2 |
| Son | 5 |
| Betlar | 840-843 |
| Til | O‘zbek |
| DOI | 10.60078/3060-4842-2025-vol2-iss5-pp840-843 |
DOI: 10.60078/3060-4842-2025-vol2-iss5-pp840-843 · Maqolaning asl sahifasi
big data, bias, size, diversity, speed, regularization, reduction methods, LASSO regression, Ridge regression, Panel data, Time Series, Python, R, Julia, Statsmodels, scikit-learn, data. table, PySpark, causal forests, большие данные, смещение, размер, разнообразие, скорость, регуляризация, методы редукции, регрессия LASSO, гребневая регрессия, панельные данные, временные ряды, Python, R, Julia, Statsmodels, scikit-learn, data.table, PySpark, причинно-следственные леса, katta hajmdagi ma’lumotlar, bias, hajm, xilma-xillik, tezlik, regularizatsiya, qisqarish usullari, LASSO regressiyasi, Ridge regressiyasi, Panel ma’lumotlari, Time Series, Python, R, Julia, Statsmodels, scikit-learn, data.table, PySpark, sabab o‘rmonlari
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