Disagreement and Biases in Inflation Expectations

By C. Capistrán, Allan G. Timmermann. 2006

320 citations recorded by the source corpus.

Disagreement in inflation expectations observed from survey data varies systematically over time in a way that reflects the level and variance of current inflation. This paper offers a simple explanation for these facts based on asymmetries in the forecasters’ costs of over- and under-predicting inflation. Our model implies (i) biased forecasts; (ii) positive serial correlation in forecast errors; (iii) a cross-sectional dispersion that rises with the level and the variance of the inflation rate; and (iv) predictability of forecast errors at different horizons by means of the spread between the short- and long-term variance of inflation. We find empirically that these patterns are present in inflation forecasts from the Survey of Professional Forecasters. A constant bias component, not explained by asymmetric loss and rational expectations, is required to explain the shift in the sign of the bias observed for a substantial portion of forecasters around 1982.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/2950fe1e8d9e651fb0b9d60158aacc0b36229c0b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

Citations in the corpus

Individual Expectations and Aggregate Macro Behavior (2011, background): “Capistran and Timmermann (2009) show that heterogeneity of inflation expectations of professional forecasters varies over time and depends on the level and the variance of current inflation.”
The Price Is Right: Updating Inflation Expectations in a Randomized Price Information Experiment (2016, unknown): “Whereas it is common to assume the mean is being reported, implying that respondents generate forecasts under symmetric square loss, Capistran and Timmermann (2009) point out that an individual’s reported expectation is biased away from the mean if she forecasts using an asymmetric loss function.”
The Role of Media for Inflation Forecast Disagreement of Households and Professionals (2012, unknown): “…of our model is borrowed from Kandel and Zilberfarb (1999) and Lahiri and Sheng (2008a) who propose a simple approach 2See, e.g., Lahiri and Sheng (2008a, 2008b), Capistrán and Timmermann (2008), Batchelor (2007), Döpke and Fritsche (2006), Giordani and Söderlind (2003), and references therein.”
Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious? (2005, unknown): “Under this loss function, as φ approaches zero, the loss approaches a quadratic function, and we obtain the result in equation (8).”
The Role of Central Bank Transparency for Guiding Private Sector Forecasts (2012, unknown): “We do this by including the conditional volatility of the variable that needs to be forecasted.8 In concordance with the approach of Capistran and Timmermann (2009), we estimated GARCH(1,1) models (where for our purposes 2 lags proved to be sufficient to reduce remaining serial correlation), and…”
Does Inflation Targeting Affect the Dispersion of Inflation Expectations (2010, background): “Capistrán and Timmermann (2006) use di¤erences in the costs of forecast errors.”