KATTA HAJMLI IQTISODIY MA’LUMOTLAR (BIG DATA) SHAROITIDA EKONOMETRIK BAHOLASH USULLARINING NAZARIY TRANSFORMATSIYASI
DOI:
https://doi.org/10.5281/zenodo.22742417Abstract
Mazkur maqolada katta hajmli iqtisodiy ma’lumotlar — Big Data sharoitida ekonometrik
baholash metodologiyasida yuz berayotgan nazariy transformatsiyalar tizimli tahlil qilinadi. An’anaviy ekonometrik
modellarning past o‘lchamli regressorlarga, oldindan belgilangan parametrik spetsifikatsiyaga va klassik
asimptotik shartlarga tayangan xususiyatlari yuqori o‘lchamli, murakkab va geterogen ma’lumotlar muhitidagi
yangi talablar bilan qiyoslanadi. OLSdan Ridge, LASSO, Elastic Net, Random Forest, Gradient Boosting va
Double/Debiased Machine Learning (DML) yondashuvlariga o‘tishning nazariy asoslari yoritiladi. Tahlil Big
Data ekonometrikani almashtirmasligini, aksincha, identifikatsiya va statistik inferensiyani regularizatsiya,
out-of-sample validatsiya hamda moslashuvchan mashinaviy o‘qitish usullari bilan integratsiyalash zaruratini
kuchaytirishini ko‘rsatadi
Keywords
Big Data, ekonometrik baholash, yuqori o‘lchamli ma’lumotlar, Ridge, LASSO, Elastic Net, Random Forest, Gradient Boosting, Double Machine Learning, causal inference.References
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