Template-Type: ReDIF-Paper 1.0
Author-Name: J. Daniel Aromí
Author-Name-First: J. Daniel
Author-Name-Last: Aromí
Author-Name: Martín Llada
Author-Name-First: Martín
Author-Name-Last: Llada
Title: Forecasting inflation with twitter
Abstract: We use Twitter content to generate an indicator of attention allocated to inflation. The analysis corresponds to Argentina for the period 2012-2019. The attention index provides valuable information regarding future levels of inflation. A one standard deviation increment in the index is followed by an increment of approximately 0.4% in expected inflation in the consecutive month. Out-of-sample exercises confirm that social media content allows for gains in forecast accuracy. Beyond point forecasts, the index provides valuable information regarding inflation uncertainty. The proposed indicator compares favorably with other indicators such as media content, media tweets, google search intensity and consumer surveys.
Length:  24 pages
Creation-Date:  2020-11
File-URL: https://aaep.org.ar/works/works2020/AromiLlada.pdf
File-Format: Application/pdf
Number: 4308
Classification-JEL:  E31, C53
Handle: RePEc:aep:anales:4308