پژوهش ها و چشم اندازهای اقتصادی

پژوهش ها و چشم اندازهای اقتصادی

مقایسه روش‌های مختلف پیش‌ بینی رشد اقتصادی ایران با تأکید بر مدل های گزینشی نمودن و متوسط‌ گیری الگوی پویا

نوع مقاله : پژوهشی اصیل

نویسندگان
1 دانشیار و عضو هیأت علمی دانشکده اقتصاد، دانشگاه علامه طباطبائی
2 دانشیار اقتصاد، عضو هیأت علمی دانشکده اقتصاد، دانشگاه علامه طباطبائی
3 دانشجوی دکتری اقتصاد، دانشگاه علامه طباطبائی
چکیده
در دهه های اخیر، به دلیل اهمیت مقادیر آتی متغیرهای کلان اقتصادی، طیف وسیعی از روش‏ها و مدل‏های پیش‏ بینی، مورد بررسی و ارزیابی قرار گرفته است. هدف اصلی این مقاله، مقایسه روش‏های مختلف پیش‏ بینی رشد اقتصادی ایران با استفاده از داده‏ های سری زمانی فصلی در دوره زمانی 96-1369 است. به منظور دستیابی به این هدف، به پیش‏ بینی این متغیر با استفاده از مدل‏های DMA، DMS،BMA، BVAR، TVP و AR در سه افق پیش ‏بینی (یک، چهار و هشت فصل) پرداخته شده است. مدل‏ های مورد استفاده در این مطالعه، به سه طیف، بزرگ مقیاس (شامل 112 متغیر در نه بلوک عاملی)، متوسط مقیاس (شامل 10 متغیر) و مدل‏های تک متغیره، دسته بندی شده اند. نتایج مطالعه، نشان می دهد که پیش ‏بینی مدل‏های گزینشی‏ نمودن (DMS) و متوسط‏ گیری الگوی پویا (DMA) نسبت به سایر روش ‏های پیش‏ بینی سنتی، دارای عملکرد پیش ‏بینی بسیار کارآیی برای رشد اقتصادی ایران هستند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Comparison of Different Methods of Predicting Iran's Economic Growth with an Emphasis on Dynamic Model Selection and Dynamic Model Averaging

نویسندگان English

Teymour Mohammadi 1
naser khiabani 2
Javid Bahrami 1
fatemeh fahimifar 3
1 Associate Professor of Economics, Allameh Tabataba’i University
2 Associate Professor of Economics, Allameh Tabataba’i University
3 Ph.D Candidate of Economics, Allameh Tabataba’i University
چکیده English

In recent decades, due to the importance of future values of macroeconomic variables, a range of predicting methods and models has been studied and evaluated. The main purpose of this paper is to compare different methods of predicting Iran's economic growth using seasonal time series data during 1990-2017. To this end, economic growth is predicted using dynamic model averaging (DMA), dynamic model selection (DMS), BMA, BVAR, TVP and AR models in three prediction horizons (one, four and eight seasons). The models used in this study are categorized into three spectra, large-scale (including 112 variables in nine factor blocks), average-scale (including 10 variables) and univariate models. The results show that the predictions of DMS and DMA are more efficient than other traditional prediction.

کلیدواژه‌ها English

forecasting
economic growth
State-space model
Factor model
Dynamic model averaging
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