Template-type: ReDIF-Article 1.0
Author-Name: Vladimir Boyko
Author-Email: boyko838@mail.ru
Author-Workplace-Name: Bank of Russia
Author-Name: Nadezhda Kislyak
Author-Email: nadya.kislyak@gmail.com
Author-Workplace-Name: Bank of Russia
Author-Name: Mikhail Nikitin
Author-Email: mihailnikitin1993@yandex.ru
Author-Workplace-Name: Bank of Russia
Author-Name: Oleg Oborin
Author-Email: oborin.oleg@gmail.com
Author-Workplace-Name: Bank of Russia
Title: Methods for Estimating the Gross Regional Product Leading Indicator
Abstract: This paper discusses two methods for estimating the quarterly values of the gross regional product (GRP) leading indicator. The first method is based on Rosstat methodology using the growth rates of indicators that reflect the output for main economic activities in the region. The second method uses temporal disaggregation (disaggregation in time). A distinctive feature of the second method is the possibility of obtaining high-frequency series using not only the indicators specified in Rosstat methodology but also other variables reflecting the dynamics of business activity in regions. The research suggests that temporal disaggregation methods provide more accurate estimates of quarterly values of the physical GRP volume index as compared to methods based on Rosstat methodology. The particular temporal disaggregation model used to forecast GRP for seven federal districts (i.e., all except the North Caucasian District) is chosen based on the performance in forecasting the gross domestic product (GDP) volume, which is close in economic terms to the overall GRP for Russia.
Classification-JEL: C15, C43, C53, C65, E23, E37
Keywords: leading indicator, gross regional product, temporal disaggregation, Chow-Lin, Litterman, Fernandez
Journal: Russian Journal of Money and Finance
Pages: 3-29
Volume: 79
Issue: 3
Year: 2020
Month: September
DOI: 10.31477/rjmf.202003.03
File-URL: https://rjmf.econs.online/upload/iblock/cd5/Estimating_GRP_Leading_Indicator.pdf
Handle: RePEc:bkr:journl:v:79:y:2020:i:3:p:3-29