مقایسه عملکرد روش‌های مختلف پیش‌بینی شاخص قیمت تولیدکننده در ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری اقتصاد دانشکده اقتصاد دانشگاه علامه طباطبائی، تهران، ایران

2 دانشیار گروه اقتصاد دانشکده اقتصاد دانشگاه علامه طباطبائی، تهران، ایران

10.29252/ecoj.10.4.151

چکیده

شاخص‌های اقتصاد کلان یکی از ابزارهای ضروری به منظور تعیین آثار سیاست‌های اقتصادی، برنامه‌ریزی‌ها و سیاست‏گذاری‏های کوتاه‌مدت، میان‌مدت و بلندمدت برای یک اقتصاد است. یکی از شاخص‌های مهم در این زمینه شاخص قیمت تولیدکننده است. از این رو این مقاله به بررسی پیش‏بینی شاخص قیمت تولیدکننده در ایران با استفاده از داده‏های سری زمانی فصلی در دوره زمانی 96-1369 با استفاده از روش‏های گزینشی‏نمودن و متوسط‏گیری الگوی پویا در سه افق پیش‏بینی (یک، چهار و هشت فصل) می‏پردازد. در این گونه روش‏ها نه تنها ضرایب بلکه الگو‏های پیش‏بینی نیز در طول زمان تغییر می‏کنند. الگو‏های مورد استفاده در این مطالعه به سه طیف، بزرگ مقیاس (شامل 101 متغیر در نه بلوک عاملی)، متوسط مقیاس (شامل 6 متغیر) و الگو‏های تک متغیره دسته‌بندی شده‌اند. نتایج مطالعه نشان می‌دهد که پیش‏بینی الگو‏های گزینشی‏نمودن و متوسط‏گیری الگوی پویا نسبت به سایر رویکردهای در نظرگرفته‏شده در این مقاله، دارای عملکرد پیش‏بینی بهتری برای شاخص قیمت تولیدکننده در ایران هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Comparing the Performance of Different Methods of Forecasting Producer Price Index in Iran

نویسندگان [English]

  • Fatemeh Fahimifar 1
  • Teymour Mohammadi 2
1 Ph.D Candidate in Economics, Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran,
2 Associate Professor of Economics, Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran
چکیده [English]

Macroeconomic indices are the essential tools to determine the effects of economic policies, in the short-, medium- and long-run planning and policy makings for an economy. One of the important indices in this area is the producer price index. Therefore, this paper investigates the forecasting of producer price index (PPI) in Iran using seasonal time series data over the period 1990-2017 (1369-1396) through Dynamic Model Selection(DMS) andDynamic Model Averaging (DMA) in three forecast horizons (one, four, and eight season). In such methods, not only the coefficients but also the forecasting models change over time. The models used in this study (DMA, DMS, BMA, BVAR, TVP and AR) were divided into three categories, large-scale (including 101 variables in nine factor blocks), middle-scale (including 6 variables), and univariate models. The results of the study indicated that forecasting DMS and DMA compared to other approaches has an efficient forecasting performance for the producer price index in Iran.

کلیدواژه‌ها [English]

  • Factor model
  • State-space Model
  • Forecasting
  • Producer Price Index
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