Characterizing the Anchoring Effects of Official Forecasts on Private Expectations

Barrera, Carlos (2022): Characterizing the Anchoring Effects of Official Forecasts on Private Expectations.

Abstract

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: C15, C30, E37, E47, E58, G14.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C30 - General; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E47 - Forecasting and Simulation: Models and Applications; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E58 - Central Banks and Their Policies; G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading

References

Barrera, Carlos (2018), Expectations and Central Banks’ Forecasts: The Experience of Chile, Colombia, Mexico, Peru and the United Kingdom, 2004 - 2014, Finance a úv˘er, Czech Journal of Economics and Finance, Vol. 68(6), 578-599.

Bertrand, Marianne; Esther Duflo & Sendhil Mullainathan (2004), How Much Should We Trust Differences-in-Differences Estimates?, Quarterly Journal of Economics, Vol. 119(1), 249-275.

Blinder, Alan; Michael Ehrmann; Marcel Fratzscher; Jakob De Haan & David-Jan Jansen (2008), Central Bank Communication and Monetary Policy: A Survey of Theory and Evidence, Journal of Economic Literature, Vol. 46(4), 910-945.

Bonato, Matteo (2011), Robust estimation of skewness and kurtosis in distributions with infinite higher moments, Finance Research Letters, Vol. 8(2), 77-87.

Bordo, Michael & Pierre Siklos (2015), Central Bank Credibility Before and After the Crisis, NBER Working Paper No. 21710 (November).

Brys, Guy; Mia Hubert & Anja Struyf (2006), Robust Measures of Tail Weight, Journal of Computational Statistics & Data Analysis, Vol. 50(3), 733-759 (February).

Consensus Economics Inc. (2015), Answer to a question formulated to their editors’ email address, [e-mail] (July 10, 2015).

Croux, Christophe & Peter Rousseeuw (1992), Time-efficient Algorithms for Two Highly Robust Estimators of Scale, in Yadolah Dodge & Joe Whittaker, eds., Computational Statistics, Volume I. Proceedings of the 10th Symposium on Computational Statistics, Heidelberg, Physika-Verlag, pp. 411-428.

Davidson, Russell & James MacKinnon (1993) Estimation and Inference in Econometrics, Oxford University Press.

Dräger, Lena; Michael Lamla & Damjan Pfajfar (2016), Are Survey Expectations Theory-Consistent? The Role of Central Bank Communication and News, European Economic Review, Vol. 85(June), 84-111.

Filacek, Jan & Saxa, Branislav (2012) Central Bank Forecasts as a Coordination Device: Evidence from the Czech Republic, Czech Economic Review, Vol. 6, 244-264.

Foroni, Claudia (2012) Econometric Models for Mixed-Frequency Data, Department of Economics, European University Institute.

Gürkaynak, Refet; Eric Swanson & 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, Vol. 8(6), 1208-1242.

Hattori, Masazumi; Steven Kong; Frank Packer & 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 (August).

Huang, Lei; Fabian Scheipl; Jeff Goldsmith; Jonathan Gellar; Jaroslaw Harezlak, Mathew McLean; Bruce Swihart; Luo Xiao; Ciprian Crainiceanu & Philip Reiss (2016), R Package Refund: Regression with Functional Data, Version 0.1-14.

Kozicki, Sharon & P.A. Tinsley (2005), What do you expect? Imperfect Policy Credibility and Test of the Expectations Hypothesis, Journal of Monetary Economics, Vol. 52(2), 421-447 (March).

Kumar, Saten; Hassan Afrouzi; Olivier Coibion; & Yuriy Gorodnichenko (2015), Inflation Targeting Does Not Anchor Inflation Expectations: Evidence from Firms in New Zealand, NBER Working Paper 21814 (December).

Neuenkirch, Matthias (2013), Central Bank Transparency and Financial Market Expectations: The Case of Emerging Markets, Economic Systems, Vol. 37(), 598-609.

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).

Pedersen, Michael (2015), What Affects the Predictions of Private Forecasts? The Role of Central Bank Forecasts in Chile, International Journal of Forecasting, Vol. 31(4), 1043-1055 (October-December).

Ramsay, James & R.W. Silverman (1997), Functional Data Analysis, Springer.

Ramsay, James & R.W. Silverman (2005), Functional Data Analysis, Second Edition, Springer.

Rousseeuw, Peter & Christophe Croux (1993), Alternatives to the Median Absolute Deviation, Journal of the American Statistical Association, Vol. 88(424), 1273-1283 (December).

Sigman, Karl (2010), Inverse Transform Method, Class notes, [Retrieved 2019-11-11] Available at: http://www.columbia.edu/∼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?, in Research in International Business and Finance, Vol. 38(June), 338-359.