Nowcasting inflation expectations using twitter

Bibliographic Details
Main Author: Gorham, Nicholas
Publication Date: 2023
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/173357
Summary: This study explores the potential of Twitter to forecast inflation expectations through various machine learning models. Twitter-based measures of inflation were shown to be correlated with survey-based inflation expectations. No single regression model was consistently superior, although PCA-Ridge appears to be appropriate. The study also unveiled potential issues with data cleaning, model overfitting, and limitations in available data. These findings advance the understanding of economic forecasting using unconventional data sources, opening pathways for future research.
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spelling Nowcasting inflation expectations using twitterNowcastingTwitterInflation expectationsDynamic topic modellingCEMS MIMDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis study explores the potential of Twitter to forecast inflation expectations through various machine learning models. Twitter-based measures of inflation were shown to be correlated with survey-based inflation expectations. No single regression model was consistently superior, although PCA-Ridge appears to be appropriate. The study also unveiled potential issues with data cleaning, model overfitting, and limitations in available data. These findings advance the understanding of economic forecasting using unconventional data sources, opening pathways for future research.Han, QiweiRUNGorham, Nicholas2023-07-052023-06-192029-06-19T00:00:00Z2023-07-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/173357TID:203366670enginfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-10-14T01:41:29Zoai:run.unl.pt:10362/173357Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:59:06.422058Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Nowcasting inflation expectations using twitter
title Nowcasting inflation expectations using twitter
spellingShingle Nowcasting inflation expectations using twitter
Gorham, Nicholas
Nowcasting
Twitter
Inflation expectations
Dynamic topic modelling
CEMS MIM
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Nowcasting inflation expectations using twitter
title_full Nowcasting inflation expectations using twitter
title_fullStr Nowcasting inflation expectations using twitter
title_full_unstemmed Nowcasting inflation expectations using twitter
title_sort Nowcasting inflation expectations using twitter
author Gorham, Nicholas
author_facet Gorham, Nicholas
author_role author
dc.contributor.none.fl_str_mv Han, Qiwei
RUN
dc.contributor.author.fl_str_mv Gorham, Nicholas
dc.subject.por.fl_str_mv Nowcasting
Twitter
Inflation expectations
Dynamic topic modelling
CEMS MIM
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Nowcasting
Twitter
Inflation expectations
Dynamic topic modelling
CEMS MIM
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description This study explores the potential of Twitter to forecast inflation expectations through various machine learning models. Twitter-based measures of inflation were shown to be correlated with survey-based inflation expectations. No single regression model was consistently superior, although PCA-Ridge appears to be appropriate. The study also unveiled potential issues with data cleaning, model overfitting, and limitations in available data. These findings advance the understanding of economic forecasting using unconventional data sources, opening pathways for future research.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-05
2023-06-19
2023-07-05T00:00:00Z
2029-06-19T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/173357
TID:203366670
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identifier_str_mv TID:203366670
dc.language.iso.fl_str_mv eng
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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repository.mail.fl_str_mv info@rcaap.pt
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