EKONOMETRIK BAHOLASHNING EPISTEMOLOGIK CHEGARALARI: GIBRID METODOLOGIYA
DOI:
https://doi.org/10.5281/zenodo.20132428Keywords:
epistemologiya, ekonometrika, gibrid metodologiya, kauzallik, identifikatsiya muammosi, model turgʻunligi, sifatli tadqiqot, qiyosiy metodologiya.Abstract
Ushbu maqolada ekonometrik baholashning epistemologik asoslari va uning metodologik chegaralari nazariy
jihatdan tahlil etildi. Kauzallik taxmini, identifikatsiya muammosi va model turgʻunligi masalalari izchil tarzda koʻrib chiqildi.
Pozitivizm, post-pozitivizm va pragmatizm doirasidagi epistemologik pozitsiyalar qiyosiy tahlil qilindi hamda ularning
iqtisodiy tadqiqot amaliyotiga taʼsiri baholandi. Gibrid metodologiya — miqdoriy va sifatli yondashuvlarning epistemologik
jihatdan asoslangan integratsiyasi — kauzal xulosaning ishonchliligini oshirishda muhim omil sifatida koʻrsatildi. Aniq
identifikatsiya strategiyalari (instrumental oʻzgaruvchilar, keskin regressiya dizayni, farqlar-farqlari usuli) epistemologik
quvvat va cheklovlar nuqtai nazaridan taqqoslandi. Maqolada gibrid arxitekturaning uch qavatli tuzilmasi taklif etilib, uning
amaliy konfiguratsiyasi va metodologik qoʻllanilish shartlari belgilandi. Natijalar qiyosiy iqtisodiyot va tranzit iqtisodiyotlar
kontekstida muhim metodologik ahamiyat kasb etadi.
References
1. Leamer, E. E. (1983). Let’s take the con out of econometrics. The American Economic Review, 73(1), 31–43. URL:
https://www.jstor.org/stable/1803924
2. Heckman, J. J. (2005). The scientific model of causality. Sociological Methodology, 35(1), 1–97. https://doi.org/10.1111/
j.0081-1750.2006.00164.x
3. Popper, K. R. (2002). The Logic of Scientific Discovery. Routledge. https://doi.org/10.4324/9780203994627
4. Kuhn, T. S. (1996). The Structure of Scientific Revolutions (3rd ed.). University of Chicago Press. https://doi.org/10.7208/
chicago/9780226458106.001.0001
5. Blaug, M. (1992). The Methodology of Economics: Or, How Economists Explain (2nd ed.). Cambridge University
Press. https://doi.org/10.1017/CBO9780511528224
6. Lakatos, I. (1978). The Methodology of Scientific Research Programmes: Philosophical Papers (Vol. 1). Cambridge
University Press. https://doi.org/10.1017/CBO9780511621123
7. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017
8. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods.
Econometrica, 37(3), 424–438. https://doi.org/10.2307/1912791
9. Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. https://doi.
org/10.1017/CBO9780511803161
10. Manski, C. F. (1995). Identification Problems in the Social Sciences. Harvard University Press. https://doi.org/10.2307/j.
ctv1pnc1k7
11. Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University
Press. https://press.princeton.edu/books/paperback/9780691120355/mostly-harmless-econometrics
12. Morgan, S. L., & Winship, C. (2015). Counterfactuals and Causal Inference: Methods and Principles for Social
Research (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781107587991
13. Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches
(5th ed.). SAGE Publications. https://us.sagepub.com/en-us/nam/research-design/book255675
14. Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research. SAGE Publications. https://us.sagepub.
com/en-us/nam/foundations-of-mixed-methods-research/book230038
15. Ziliak, S. T., & McCloskey, D. N. (2008). The Cult of Statistical Significance: How the Standard Error Costs Us Jobs,
Justice, and Lives. University of Michigan Press. https://press.umich.edu/Books/T/The-Cult-of-Statistical-Significance2
16. Freedman, D. A. (2009). Statistical Models: Theory and Practice (2nd ed.). Cambridge University Press. https://doi.
org/10.1017/CBO9780511815867
17. Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. https://mitpress.
mit.edu/9780262232586/econometric-analysis-of-cross-section-and-panel-data/
18. Greene, W. H. (2018). Econometric Analysis (8th ed.). Pearson. https://www.pearson.com/en-us/subject-catalog/p/
econometric-analysis/P200000006084
19. Hendry, D. F. (1995). Dynamic Econometrics. Oxford University Press. https://doi.org/10.1093/0198283164.001.0001
20. Stock, J. H., & Watson, M. W. (2015). Introduction to Econometrics (3rd ed.). Pearson. https://www.pearson.com/
en-us/subject-catalog/p/introduction-to-econometrics/P200000006070
21. Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences. Cambridge
University Press. https://doi.org/10.1017/CBO9781139025751
22. Lucas, R. E. (1976). Econometric policy evaluation: A critique. Carnegie-Rochester Conference Series on Public
Policy, 1, 19–46. https://doi.org/10.1016/S0167-2231(76)80003-6
23. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251–1271. https://doi.
org/10.2307/1913827
24. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity.
Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934
25. Andrews, D. W. K. (1993). Tests for parameter instability and structural change with unknown change point.
Econometrica, 61(4), 821–856. https://doi.org/10.2307/2951764
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 GREEN ECONOMY AND DEVELOPMENT

This work is licensed under a Creative Commons Attribution 4.0 International License.