Modeling Azerbaijan’s Inflation and Output Using a Factor-Augmented Vector Autoregressive (FAVAR) Model

Vugar Rahimov, Nijat Guliyev () and Vugar Ahmadov
Additional contact information
Nijat Guliyev: Central Bank of the Republic of Azerbaijan

No 01-2020, IHEID Working Papers from Economics Section, The Graduate Institute of International Studies

Abstract: In this study, we build and use a factor-augmented vector autoregressive (FAVAR) model to forecast inflation and output in Azerbaijan. The FAVAR model is particularly effective in data-rich environments, alleviating the curse of dimensionality of the standard VAR model and handling omitted variable bias. Using 77 variables for factor extraction and quarterly data for the period 2003 to 2018, we build several multivariate models, including a FAVAR model, and compare their performance with that of a benchmark univariate model. Our findings show that almost all of the multivariate models underperform in comparison with the univariate model. This result is in line with the literature, which finds that simple models are better forecasters of some macroeconomic variables, especially inflation. We acknowledge that the results might be affected by the relatively short length of the sample period and existence of irregularities in the data.

Pages: 37 pages
Date: 2020-01-21
New Economics Papers: this item is included in nep-ets and nep-mac
References: Add references at CitEc
Citations:

Downloads: (external link)
http://repec.graduateinstitute.ch/pdfs/Working_papers/HEIDWP01-2020.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gii:giihei:heidwp01-2020

Access Statistics for this paper

More papers in IHEID Working Papers from Economics Section, The Graduate Institute of International Studies Contact information at EDIRC.
Bibliographic data for series maintained by Dorina Dobre ().