Sentiment and economicactivity in Brazil
DOI:
https://doi.org/10.1108/REGE-05-2022-0081Palabras clave:
Sentiment, Economic Activity, UncertaintyResumen
Purpose: Analyze how sentiment affects economic activity in Brazil.
Design/methodology/approach: Based on a nonlinear autoregressive distributed lag (NARDL) model, we examine in detail the short-term and long-term asymmetric impacts between the variables during the period from January 2007 to December 2020.
Findings: There are three main results of this study. First, sentiment is an important factor for economic activity in Brazil, and its effect possibly occurs through the channels of consumption and investment, which are the two main components of economic growth. Second, sentiment affects economic activity in different ways in the short and the long term: in Brazil although in the short-term immediate shocks of sentiment may be confusing, the negative shocks from previous periods have a negative impact on economic activity. Third, the effect of shocks of optimism and pessimism on economic activity is asymmetric, and in the long run only shocks of optimism have a significant and positive impact.
Originality: The relationship between sentiment and economic activity is still a controversial issue in the literature and this study seeks to advance its understanding in Brazil.
Descargas
Referencias
Akerlof, G. A., & Shiller, R.J. (2009). Animal spirits: How human psychology drives the economy, and why it matters for global capitalism. Princeton University Press.
Acemoglu, D., & Scott, A. (1994). Consumer Confidence and Rational Expectations: Are Agents' Beliefs Consistent with the Theory? The Economic Journal, 104(422), p. 1-19. doi:10.2307/2234671
Angeletos, G.-M., & La'O, J. (2013). Sentiments. Econometrica, 81(2), p. 739-779. doi:10.3982/ECTA10008
Bachmann, R., Elstner, S., & Sims, E. R. (2013). Uncertainty and Economic Activity: Evidence from Business Survey Data. American Economic Journal: Macroeconomics, 5(2), p. 217-249. doi:10.2307/43189547
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), p. 1593–1636. doi:10.1093/qje/qjw024
Barboza, R. M., & Zilberman, E. (2018). Os Efeitos da Incerteza sobre a Atividade Econômica no Brasil. Revista Brasileira de Economia, 72(2), p. 144-160. doi:10.5935/0034-7140.20180007
Barsky, R. B., & Sims, E. R. (2012). Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence. American Economic Review, 102(4), p. 1343-1377. doi:10.1257/aer.102.4.1343
Basu, S., & Bundick, B. (2017). Uncertainty shocks in a model of effective demand. Econometrica, 85(3), p. 937–958. doi:10.3982/ECTA13960
Benhabib, J., & Spiegel, M. M. (2018). Sentiments and Economic Activity: Evidence from US States. The Economic Journal(in press). doi:10.1111/ecoj.12605
Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), p. 623–685. doi:10.3982/ECTA6248
Christiansen, C., Eriksen, J. N., & Møller, S. V. (2014). Forecasting US recessions: The role of sentiment. Journal of Banking & Finance, 49, p. 459–468. doi:10.1016/j.jbankfin.2014.06.017
DellaVigna, S., & Pollet, J. M. (2009). Investor Inattention and Friday Earnings Announcements. The Journal of Finance, 64(2), p. 709-749. doi:10.1111/j.1540-6261.2009.01447.x
Farmer, R. E. (2012). Confidence, Crashes and Animal Spirits. The Economic Journal, 122(559), p. 155-172. doi:10.1111/j.1468-0297.2011.02474.x
Forgas, J. P. (1995). Mood and judgment: the affect infusion model (AIM). Psychological Bulletin, 117, p. 39-66.
FGV/IBRE. (2021). Sondagem do consumidor: aspectos conceituais e metodológicos, available at: https://portalibre.fgv.br/sites/default/files/2021-08/sondagem-do-consumidor_fgv_aspectos-metodologicos_ano-2021.pdf (accessed 20 January 2021).
Greifeneder, R., & Bless, H. (2007). Relying on accessible content versus accessibility experiences: the case of processing capacity. Social Cognition, 25, p. 853–881.
Guo, Y., & He, S. (2020). Does confidence matter for economic growth? An analysis from the perspective of policy effectiveness. International Review of Economics and Finance, 69, p. 1-19. doi:10.1016/j.iref.2020.04.012
Hassan, T. A., & Mertens, T. M. (2011). Market sentiment: a tragedy of the commons. The American Economic Review, 101(3), p. 402-405. doi:10.1257/aer.101.3.402
Ilut, C., & Saijo, H. (2020). Learning, confidence, and business cycles. Journal of Monetary Economics(in press). doi:10.1016/j.jmoneco.2020.01.010
Kabiri, A. James, H. Landon-Lane, J. Tuckett, D. & Nyman, R. (2022). The role of sentiment in the US economy: 1920 to 1934, The Economic History review. Early View, doi: 10.1111/ehr.13160Keynes, J. M., (1936). The General Theory of Employment, Interest, and Money. Harcourt Brace, New York.
Knight, F. H. (2012). Risk, Uncertainty and Profit. Courier Corporation. New York: Dover Publications Inc.
Kumar, A., & Lee, C. (2006). Retail investor sentiment and return comovements. The Journal of Finance, 61(5), p. 2451–2486. doi:10.1111/j.1540-6261.2006.01063.x
Leduc, S., & Liu, Z. (2016). Uncertainty shocks are aggregate demand shocks. Journal of Monetary Economics, 82, p. 20-35. doi:10.1016/j.jmoneco.2016.07.002
Lemmon, M., & Portniaguina, E. (2006). Consumer Confidence and Asset Prices: Some Empirical Evidence. The Review of Financial Studies, 19(4), p. 1499-1529.
Lorenzoni, G. (2009). A Theory of Demand Shocks. The American Economic Review, 99(5), p. 2050-2084.
Mello, E. P. G. & Figueiredo, F. M. R. (2017). Assessing the short-term forecasting power of confidence indices. Economia Aplicada, 21(4), p. 712-727. doi: 10.11606/ea139730
Nkoro, E., & Uko, A. K. (2016). Autoregressive distributed lag (ARDL) cointegration technique: application and interpretation. Journal of Statistical and Econometric Methods, 5(4), p. 63-91.
Nowzohour, L., & Stracca, L. (2020). More than a feeling: confidence, uncertainty, and macroeconomic fluctuactions. Journal of Economics Surveys, 34(4), p. 691-726. doi:https://doi.org/10.1111/joes.12354
Ottati, V. C., & Isbell, L. M. (1996). Effects on mood during exposure to target information on subsequently reported judgments: An on-line model of misattribution and correction. Journal of Personality and Social Psychology, 71, p. 39–53.
Pesaran, M. H., & Shin, Y. (1999). An autoregressive distributed lag modeling approach to cointegration analysis. Em S. Strom, Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium. Cambridge: Cambridge University Press.
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), p. 289–326. doi:doi.org/10.1002/jae.616
Scotti, C. (2016). Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises. Journal of Monetary Economics, 82, p. 1-19. doi:10.1016/j.jmoneco.2016.06.002
Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In: Sickles R., Horrace W. (eds) Festschrift in Honor of Peter Schmidt. Springer, New York, NY., p. 281-314. doi:10.1007/978-1-4899-8008-3_9
Vuchelen, J. (2004). Consumer sentiment and macroeconomic forecasts. Journal of Economic Psychology, 25(4), p. 493-506. doi:10.1016/S0167-4870(03)00031-X
