Template-Type: ReDIF-Article 1.0
Author-Name: Samvel S. Lazaryan
Author-Email: lazaryan@nifi.ru
Author-Workplace-Name: Financial Research Institute, Moscow 127006, Russia
Author-Name: Nikita E. German
Author-Email: nichita.gherman@gmail.com
Author-Workplace-Name: National Research University Higher School of Economics, Moscow 101000, Russia
Title: Forecasting Current GDP Dynamics With Google Search Data
Abstract: In order to conduct a conscious economic policy, timely assessment of the main economic indicators, viz GDP, is needed. In this paper the authors justify the reason why an inclusion of query search data may help to produce better nowcasts compared to the currently used Russian GDP models, which are built on the data from official statistical services. The authors also check, whether this hypothesis holds true in the real-time forecasting experiment. For this purpose the authors suggest two competing dynamic factor models: the one, which includes data on the query search frequency, and the other one, which excludes it. The models show that the inclusion of query search data does not change the forecast performance of the model built only upon official economic indicators. At the same time, both models have produced more accurate nowcasts of Russian GDP then AR(1) model did. Finally, the authors try to explain the resulting irrelevance of query search data in nowcasting GDP. The article discusses both fundamental reasons and the pitfalls of the methodology used in this paper, which could have led to such result.
Classification-JEL: C32, C53
Keywords: forecasting, factor models, GDP, nowcasting, search queries, data frequency
Journal: Finansovyj žhurnal — Financial Journal
Pages:83-94
Issue: 6
Year: 2018
Month: December
DOI: 10.31107/2075-1990-2018-6-83-94
File-URL: http://www.finjournal-nifi.ru/images/FILES/Journal/Archive/2018/6/statii/fm_2018_6_07.pdf
File-Format: Application/pdf 
Handle: RePEc:fru:finjrn:180607:p:83-94