Characterizing the Anchoring Effects of Official Forecasts on Private Expectations

  • Carlos R. BARRERA CHAUPIS Research Division, Banco Central de Reserva del Perú, Perú


The paper proposes a method for simultaneously estimating the treatment effects of a change in a policy variable on a numerable set of interrelated outcome variables (different moments from the same probability density function). Firstly, it defines a non-Gaussian probability density function as the outcome variable. Secondly, it uses a functional regression to explain the density in terms of a set of scalar variables. From both the observed and the fitted probability density functions, two sets of interrelated moments are then obtained by simulation. Finally, a set of difference-in-difference estimators can be defined from the available pairs of moments in the sample. A stylized application provides a 29-moment characterization of the direct treatment effects of the Peruvian Central Bank’s forecasts on two sequences of Peruvian firms’ probability densities of expectations (for inflation -π- and real growth -g-) during 2004-2015.


Barrera, Carlos. 2018. Expectations and Central Banks’ Forecasts: The Experience of Chile, Colombia, Mexico, Peru and the United Kingdom, 2004 – 2014. Finance a úve ̆r, Czech Journal of Economics and Finance 68(6): 578-599.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004. How Much Should We Trust Differences-in-Differences Estimates? Quarterly Journal of Economics 119(1): 249-275. DOI:
Blinder, Alan, Michael Ehrmann, Marcel Fratzscher, Jakob De Haan, and David-Jan Jansen. 2008. Central Bank Communication and Monetary Policy: A Survey of Theory and Evidence. Journal of Economic Literature 46(4): 910-945. DOI: 10.1257/jel.46.4.910
Bonato, Matteo. 2011. Robust estimation of skewness and kurtosis in distributions with infinite higher moments. Finance Research Letters 8(2): 77-87. DOI:
Bordo, Michael, and Pierre Siklos. 2015. Central Bank Credibility Before and After the Crisis. NBER Working Paper No. 21710. Available at:
Brys, Guy, Mia Hubert, and Anja Struyf. 2006. Robust Measures of Tail Weight. Journal of Computational Statistics & Data Analysis 50(3): 733-759. DOI:
Croux, Christophe, and Peter Rousseeuw. 1992. Time-efficient Algorithms for Two Highly Robust Estimators of Scale. In Yadolah Dodge and Joe Whittaker, eds., Computational Statistics, Volume I. Proceedings of the 10th Symposium on Computational Statistics, Heidelberg, Physika-Verlag, pp. 411-428.
Davidson, Russell and James MacKinnon. 1993. Estimation and Inference in Econometrics. Oxford University Press.
Dräger, Lena, Michael Lamla, and Damjan Pfajfar. 2016. Are Survey Expectations Theory-Consistent? The Role of Central Bank Communication and News. European Economic Review 85: 84-111. DOI:
Filacek, Jan, and Branislav Saxa. 2012. Central Bank Forecasts as a Coordination Device: Evidence from the Czech Republic. Czech Economic Review 6: 244-264. Available at:
Foroni, Claudia. 2012. Econometric Models for Mixed-Frequency Data, Department of Economics, European University Institute.
Gürkaynak, Refet, Eric Swanson, and Andrew Levin. 2010. Does inflation targeting anchor long-run inflation expectations? Evidence from the U.S., UK and Sweden. Journal of the European Economic Association 8(6): 1208-1242. DOI:
Hattori, Masazumi, Steven Kong, Frank Packer, and Toshitaka Sekine. 2016. The Effects of a Central Bank’s Inflation Forecasts on Private Sector Forecasts: Recent Evidence from Japan. Bank of Japan Working Paper No. 16-E-11. Available at:
Huang, Lei, Fabian Scheipl, Jeff Goldsmith, Jonathan Gellar, Jaroslaw Harezlak, Mathew McLean, Bruce Swihart, Luo Xiao, Ciprian Crainiceanu, and Philip Reiss. 2016. R Package Refund: Regression with Functional Data, Version 0.1-14.
Kozicki, Sharon, and P.A. Tinsley. 2005. What do you expect? Imperfect Policy Credibility and Test of the Expectations Hypothesis. Journal of Monetary Economics 52(2): 421-447. DOI:
Kumar, Saten, Hassan Afrouzi, Olivier Coibion, and Yuriy Gorodnichenko. 2015. Inflation Targeting Does Not Anchor Inflation Expectations: Evidence from Firms in New Zealand. NBER Working Paper No. 21814. DOI:10.3386/w21814
Neuenkirch, Matthias. 2013. Central Bank Transparency and Financial Market Expectations: The Case of Emerging Markets. Economic Systems 37: 598-609. DOI:
Pedersen, Michael. 2015. What Affects the Predictions of Private Forecasts? The Role of Central Bank Forecasts in Chile. International Journal of Forecasting 31(4): 1043-1055. DOI:
Pereira da Silva, Luis Awazu. 2016. Old and New Challenges for 2016 and Beyond: Strengthening Confidence by Re-anchoring Long-term Expectations. Paper presented as the Lamfalussy Lecture Series, at the Professor Lamfalussy Commemorative Conference ‘His Contribution to Economic Policy and the Birth of the Euro’, Budapest (February).
Ramsay, James and R.W. Silverman. 1997. Functional Data Analysis, Springer.
Ramsay, James and R.W. Silverman. 2005. Functional Data Analysis, Second Edition, Springer.
Rousseeuw, Peter, and Christophe Croux. 1993. Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association 88(424): 1273-1283. DOI: 10.1080/01621459.1993.10476408
Sigman, Karl. 2010. Inverse Transform Method - Class Notes. Avaible at:∼ks20/ 4404-Sigman/4404-Notes-ITM.pdf
Trabelsi, Emna. 2016. Central Bank Transparency and the Consensus Forecast: What Does the Economist Poll of Forecasters Tell Us? Research in International Business and Finance 38: 338-359. DOI:
Consensus Economics Inc. 2015. Answer to a question formulated to their editors’ email address,
How to Cite
BARRERA CHAUPIS, Carlos R.. Characterizing the Anchoring Effects of Official Forecasts on Private Expectations. Theoretical and Practical Research in Economic Fields, [S.l.], v. 14, n. 1, p. 126 - 145, june 2023. ISSN 2068-7710. Available at: <>. Date accessed: 19 apr. 2024. doi: