Measuring and evaluating the effects of an economic policy uncertainty shock on the Brazilian economy
DOI:
https://doi.org/10.11606/1980-5330/ea182617Keywords:
economic uncertainty, Copom, sentiment analysis, VARAbstract
This article aims to create an index capable of measuring the degree of uncertainty of economic policy in Brazil. This index will be built from the estimation of the textual sentiment contained in the minutes of Copom meetings, considering the period from January 2000 to December 2018. Subsequently, we analyze how an uncertainty shock affects the dynamics of a set of macroeconomic variables through an Autoregressive Vector Model with signal restriction. It was possible to verify that the increase in uncertainty has contractionary effects, promoting a reduction in consumption and negatively affecting economic activity.
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AGUIAR, E. J. et al. Análise de sentimento em redes sociais para a língua portuguesa utilizando algoritmos de classificação. In: SBC. Anais do XXXVI Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. [S. l.: s. n.], 2018.
ALCHIAN, A. A. Uncertainty, evolution, and economic theory. Journal of Political Economy, v. 58, n. 3, p. 211–221, 1950.
ALUÍSIO, S. M.; ALMEIDA, G. M. B. O que é e como se constrói um corpus? Lições aprendidas na compilação de vários corpora para pesquisa linguística. Calidoscópio, v. 4, n. 3, p. 156–178, 2006.
APEL, M.; GRIMALDI, M. The information content of central bank minutes, 2012.
BAKER, S. R.; BLOOM, N.; DAVIS, S. J. Measuring economic policy uncertainty. Quarterly Journal of Economics, v. 131, n. 4, p. 1593–1636, 2016.
BARBOZA, R. M.; ZILBERMAN, E. Os efeitos da incerteza sobre a atividade econômica no Brasil. Revista Brasileira de Economia, v. 72, n. 2, p. 144–160, 2018.
BHOLAT, D. et al. Text mining for central banks. Available at SSRN 2624811, 2015.
BONCIANI, D.; VAN ROYE, B. Uncertainty shocks, banking frictions and economic activity. Journal of Economic Dynamics and Control, v. 73, p. 200–219, 2016.
BRUNO, G. Central Bank Communications: Information extraction and Semantic Analysis. In: the R User Conference, useR! 2017 July 4-7 2017 Brussels, Belgium. [S. l.: s. n.], 2017. p. 253.
BRUNO, G. Text mining and sentiment extraction in central bank documents. In: IEEE. 2016 IEEE International Conference on BigData (BigData). [S. l.: s. n.], 2016. p. 1700–1708.
CHISHOLM, E.; KOLDA, T. G. New term weighting formulas for the vector space method in information retrieval. [S. l.], 1999.
CORREA, R. et al. Sentiment in Central Banks’ Financial Stability Reports. Available at SSRN 3091943, 2017.
COSTA FILHO, A. E. Incerteza e atividade econômica no Brasil. Economia Aplicada, v. 18, n. 3, p. 421–453, 2014.
COSTA FILHO, A. E.; ROCHA, F. Como o mercado de juros futuros reage à comunicação do Banco Central? Economia Aplicada, v. 14, n. 3, p. 265–292, 2010.
COSTA FILHO, A. E.; ROCHA, F. Comunicação e política monetária no Brasil. Revista Brasileira de Economia, v. 63, n. 4, p. 405–422, 2009.
FERREIRA, P. C. et al. Medindo a Incerteza Econômica no Brasil. Economia Aplicada, 2017.
GODEIRO, L. L.; OLIVEIRA LIMA, L. R. R. Medindo incerteza macroeconômica para o Brasil. Economia Aplicada, v. 21, n. 2, p. 311, 2017.
GRAMINHO, F. M. et al. Sentimento e Macroeconomia: uma análise dos índices de confiança no Brasil. Brazilian Central Bank, trabalhos para discussão, v. 408, 2015.
GRIGNANI, F.; FONTANA, R. Using machine learning and Bayesian networks to objectively analyze central bank statements and market sentiment. 2018. Tese (Doutorado) – Politecnico di Torino.
GRIMME, C. Uncertainty and the cost of bank vs. bond finance. CESifoWorking Paper, 2019.
HANSEN, S.; MCMAHON, M. Shocking language: Understanding the macroeconomic effects of central bank communication. Journal of International Economics, v. 99, s114–s133, 2016.
HASELMAYER, M.; JENNY, M. Sentiment analysis of political communication: combining a dictionary approach with crowdcoding. Quality & quantity, v. 51, n. 6, p. 2623–2646, 2017.
HOLT, C. C. Forecasting seasonals and trends by exponentially weighted Moving averages. International Journal of Forecasting, v. 20, n. 1, p. 5–10, 1957.
HUTTO, C. J.; GILBERT, E. Vader: A parsimonious rule-based model for sentimento analysis of social media text. In: eighth international AAAI conference on weblogs and social media. [S. l.: s. n.], 2014.
JURADO, K.; LUDVIGSON, S. C.; NG, S. Measuring uncertainty. American Economic Review, v. 105, n. 3, p. 1177–1216, 2015.
KEARNEY, C.; LIU, S. Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, v. 33, p. 171–185, 2014.
KNIGHT, F. H. Risk, uncertainty and profit. [S. l.]: Houghton Mifflin Company, New York, 1921.
LI, J. et al. Topic Popularity Prediction with Sentiment Time Series on Short Text based Social Media, 2019.
LOUGHRAN, T.; MCDONALD, B. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, v. 66, n. 1, p. 35–65, 2011.
MACHADO, M. A. V. et al. Análise do Sentimento Textual dos Relatórios de Desempenho Trimestral das Indústrias Brasileiras. Sociedade, Contabilidade e Gestão, v. 12, n. 1, 2017.
MEINEN, P.; ROEHE, O. To sign or not to sign? On the response of prices to financial and uncertainty shocks. Economics Letters, v. 171, p. 189–192, 2018.
MONTES, G. C.; NICOLAY, R. T. F. Central bank’s perception on inflation and inflation expectations of experts: empirical evidence from Brazil. Journal of Economic Studies, v. 42, n. 6, p. 1142–1158, 2015.
NOPP, C.; HANBURY, A. Detecting Risks in the Banking System by Sentiment Analysis. In: proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal: Association for Computational Linguistics, set. 2015. p. 591–600. Disponível em: https://aclweb.org/anthology/D/D15/D15-1071.
ORMEROD, P.; NYMAN, R.; TUCKETT, D. Measuring financial sentiment to predict financial instability: A new approach based on text analysis. arXiv preprint arXiv:1508.05357, 2015.
PAO, M. L. Automatic text analysis based on transition phenomena of word occurrences. Journal of the American Society for Information Science, v. 29, n. 3, p. 121–124, 1978.
POPP, A.; ZHANG, F. The macroeconomic effects of uncertainty shocks: The role of the financial channel. Journal of Economic Dynamics and Control, v. 69, p. 319–349, 2016.
REDL, C. The impact of uncertainty shocks in the United Kingdom. Bank of England Working Paper, 2017.
RUBIO-RAMIREZ, J. F.; WAGGONER, D. F.; ZHA, T. Structural vector autoregressions: Theory of identification and algorithms for inference. The Review of Economic Studies, v. 77, n. 2, p. 665–696, 2010.
RYBINSKI, K. I. A machine learning framework for automated analysis of formal and informal central bank communication. The case of the National Bank of Poland, 2018.
SALTON, G.; BUCKLEY, C. Term-weighting approaches in automatic text retrieval. Information Processing & Management, v. 24, n. 5, p. 513–523, 1988.
SCHYMURA, L. G. A crescente importância de medir a incerteza e seus impactos no Brasil de hoje. Revista Conjuntura Econômica, v. 71, n. 5, p. 6–9, 2017.
SILVA, M. D. O. P. et al. O efeito do sentimento das notícias sobre o comportamento dos preços no mercado acionário brasileiro, 2017.
UHLIG, H. What are the effects of monetary policy on output? Results from an agnostic identification procedure. Journal of Monetary Economics, v. 52, n. 2, p. 381–419, 2005.
WILKINSON, L.; FRIENDLY, M. The history of the cluster heat map. The American Statistician, v. 63, n. 2, p. 179–184, 2009.
YOU, S.; DESARMO, J.; JOO, S. Measuring happiness of US cities by mining usergenerated text in Flickr. com: A pilot analysis. In: american society for information science. proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries. [S. l.: s. n.], 2013. p. 167.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Grant numbers 001
