@article { author = {Mohammadi, Teimur and Bahrami, Javid and Fahimifar, Fatemeh}, title = {Comparing the Forecasting of Iranian Inflation with many predictors}, journal = {Quarterly Journal of Quantitative Economics}, volume = {18}, number = {4}, pages = {159-190}, year = {2022}, publisher = {Shahid Chamran University of Ahvaz}, issn = {2008-5850}, eissn = {2717-4271}, doi = {10.22055/jqe.2020.31882.2190}, abstract = {EXTENDED ABSTRACT INTRODUCTION One of the most important economic problems in Iran during the last few decades is the phenomenon of inflation, to an extent that improving the conditions caused by inflation has always been one of the important goals of developmental plans in Iran. Achieving this goal requires the creation of a rigorous and purposeful mechanism for the economic policymaking process, which in its standard form involves forecasting, targeting and policy analysis. Given that inflation affects many macroeconomic indicators, therefore, explaining and forecasting inflation rates based on econometrics models can help improve policy making.   METHODOLOGY In this study, 108 quarterly variables from 1990-2017 were used. The variables included the consumer price index as a dependent variable and 107 independent variables (predictors) which were included in nine blocks in order to extract the factors. Thus, 1) the price block included different values ​​of the producer price index, GDP deflator and wage indexes (including 13 variables); 2) the demand block included components of GDP on the demand side and some other indexes related to capital stock (including 7 variables); 3) the fiscal block included all types of government revenues and expenditures (including 13 variables); 4) the international block included exports, imports, exchange rates, as well as the inflation and exchange rates of China and Germany (as Iran's largest trading partners) (including 17 variables); 5) the output block, where different types of production variables were used (including 14 variables); 6) the money block included liquidity and monetary base components (including 21 variables); 7) the financial block, including stock market and insurance variable (including 6 variables); 8) the energy block included various variables related to crude oil, electricity and energy (including 7 variables); and 9) the labor force block which included various variables of unemployment, employment and productivity (including 9 Variables). All the variables of this paper were seasonally adjusted using X-13, TRAMO or STL. Also, the statics of the variables were investigated by the use of unit root tests without seasonal integration (Dickey Fuller and KPSS tests) and a unit root test with seasonal and semi-annual integration (HEGY test). In addition, all variables were standardized by differentiating from the mean and dividing by the standard deviation. Principal Component Analysis (PCA) was used to extract the factors using all the variables in each block. The main purpose of this study was to compare DMA and DMS models (9 block factor methodes) with BMA, BVAR, TVP and AR. In addition, the interruptions of each model were determined using BIC. Similar to the study of Koop and Korobilis (2012), the predictions were considered with a short-run horizon (h = 1), a medium-run horizon (h = 4) and a long-run horizon (h = 8). In order to assess the prediction performance, Mean Squared Forecast Error (MSFE), Mean Absolute Forecast Error (MAFE), Mean Absolute Percentage Error (MAPE), the bias of the prediction error, the variance of the prediction error and the sum of log predictive likelihoods (PL) were used. In addition, the Diebold-Mariano (1995) test was used to compare the prediction accuracy.   FINDINGS The results show that the prediction of DMS and DMA is more efficient than other traditional predictive methods to predict the inflation rate in Iran. Findings indicated that in all forecast horizons, money and price blocks were the highest in using the optimal model over time and the fiscal block was the lowest. In general, it can be said that the DMA model is the average of many models and does not only use a single factor block (there is also no irrelevant factor block) and this is the most important feature of DMA / DMS use.   CONCLUSION Due to the fact that variables can be changed in DMA / DMS in any time period, so their use for economic policy is recommended.  }, keywords = {Forecasting,inflation,State-space,Factor model,Dynamic model averaging}, title_fa = {مقایسه پیش ‏بینی نرخ تورم مصرف‌کننده ایران با استفاده از تعداد بسیاری متغیر پیش ‏بینی کننده}, abstract_fa = {یکی از مهم‌ترین مشکلات اقتصادی در ایران طی چند دهه اخیر پدیده­ی تورم  بالا و دو رقمی است، به‌طوری که بهبود شرایط ناشی از وجود تورم بالا همواره یکی از اهداف مهم برنامه‌های توسعه کشور بوده است. دستیابی به این هدف مستلزم ایجاد ساز و کاری دقیق و هدفمند از فرآیند سیاستگذاری اقتصادی است که در شکل استاندارد خود، پیش­بینی، هدف‏گذاری و تحلیل سیاستی را شامل می­گردد. در این مطالعه از 108 متغیر فصلی در دوره زمانی96-1369 استفاده شده‏است. متغیرهای مورد استفاده شامل شاخص قیمت مصرف­کننده به عنوان متغیر وابسته و 107 متغیر مستقل (پیش‏بینی‏کننده) بوده که در نه بلوک (بلوک قیمتی، بلوک تقاضا، بلوک دولت، بلوک خارجی، بلوک ستاده، بلوک پولی، بلوک مالی، بلوک انرژی و بلوک نیروی‏کار) به منظور استخراج عوامل گنجانده شده­اند.از تحلیل مؤلفه‏ها‏ی اساسی برای استخراج عوامل با استفاده از تمامی متغیرها در هر بلوک استفاده شده است. علاوه‏بر این، وقفه‏های هر مدل با استفاده از BIC تعیین شده‏اند. همانند مطالعه کوپ و کوروبیلیس (2012) پیش‏بینی‏ها با سه افق کوتاه‏مدت(h=1)، افق میان‌مدت(h=4) و افق بلندمدت(h=8) در نظرگرفته شده است. هدف اصلی این مطالعه، مقایسه عملکرد پیش‏بینی مدل‏های DMA و DMS با BMA، BVAR، TVP و AR می­باشد. به منظور ارزیابی عملکرد پیش‏بینی از مربع میانگین خطای پیش‏بینی‏، قدرمطلق میانگین خطای پیش‏بینی، میانگین درصد قدرمطلق خطای پیش‏بینی، تورش خطای پیش‏بینی و واریانس خطای پیش‏بینی  و مجموع لگاریتم احتمالات پیش‏بینی استفاده‏شده‏است. علاوه بر این، به منظور مقایسه صحت پیش‏بینی از آزمون دیبولد-ماریانو (1995) استفاده‏شد. نتایج مطالعه نشان می­دهد که پیش‏بینی مدل‏های گزینشی‏نمودن (DMS) و متوسط‏گیری الگوی پویا (DMA) نسبت به سایر روش‏های پیش‏بینی سنتی دارای عملکرد کاراتری برای نرخ تورم ایران هستند. یافته‏ها حاکی از آن‏است که در تمامی افق‏های پیش‏بینی، بلوک‏های پولی و قیمتی دارای بیشترین تعداد در استفاده از مدل بهینه در طول زمان بوده و کمترین تعداد نیز به بلوک دولت اختصاص داشته است.}, keywords_fa = {پیش‏ بینی,نرخ تورم مصرف‌کننده,مدل فضا-حالت,مدل عاملی,متوسط‏گیری الگوی پویا}, url = {https://jqe.scu.ac.ir/article_15619.html}, eprint = {https://jqe.scu.ac.ir/article_15619_0f6835035902ac77413cf60f1f15b226.pdf} }