Faculty & Research -Why Are Inflation Forecasts Sticky? Theory and Application to France and Germany

Why Are Inflation Forecasts Sticky? Theory and Application to France and Germany

We study the frequency of central banks’ inflation forecasts announcement building on a novel theory where both a fixed cost of announcing a revised forecast and a fixed cost of updating the information set and adapting the forecast accordingly are considered. Basically, the former fixed communication costs determine state dependence, which implies that the forecaster changes its forecast only when it is far enough from the optimal forecast, and the latter fixed information costs determine time dependence, which implies that the forecaster updates its information set only every other T periods, where T is optimally chosen. Using in particular survey data on inflation forecasts updates, we estimate the optimal frequency of forecast updates announcements. French and German data suggest that the maximum optimal time to next observation is six months, while the observation (or information) cost is at most twice as large as the communication cost.

Recent empirical evidence from forecast surveys data point to a “forecast stickiness”, inconsistent with the full information rational expectation hypothesis in a frictionless setup. To explain these findings, literature has put forward information rigidities models, such as sticky information à la Mankiw and Reis [2002] or noisy information à la Woodford [2002] as an explanation. Hence forecast stickiness is observed because access to information is imperfect and/or costly. However, these models usually fail to reproduce the degree of stickiness observed in the data. Moreover, they imply decision rules that are time-dependent only, hence contradicting empirical findings that they are also state-dependent.

In this paper, we put forward an additional ingredient to explain the forecast stickiness observed in the data: communication costs. Hence, a forecaster can update her information set (and pay an observation cost), process an update of her forecast, and decide not to communicate it (publicly or to the survey) because this communication is costly for her. This “communication cost” do cover all the costs associated with the official release of the revised forecast, such as public communication, writing reports, interview with media etc. This cost also includes the loss of the forecaster’s credibility from its institution’s customer that too frequent forecast revisions would cause.


The theoretical framework developed borrows from the mathematical structure developed by Alvarez et al. (2011). These authors tackle a completely different economic issue (namely, optimal price setting with observation and menu costs). We show that the dynamic forecasting structure can be adapted to our central banking communication problem, with a few key modifications. The main technical properties  enhanced by Alvarez et al. are preserved within our extension.

The model’s structure

Note: T is the time between two observations;  is the forecast gap,  i.e. the difference between the actual forecast and the one which would prevails using updated information set. p uppe- bar is a threshold that depends on information and communication costs.

Our theoretical model of forecasts formation which incorporates separate observation and communication costs. As a result, forecast update decision rule is found to be both time- and state-dependent. The main model’s implications for forecasts update process are the following. First, the time between two observations is a non-linear function of the gap between the current forecast and the one which would prevail using updated information set (hereafter called forecast gap): for small such gaps, the closer to a threshold value, the sooner the next observation. By contrast, small enough gaps define an inaction band from the forecasters where it is not worth it either reviewing observation or revising the forecast. Second, the time between two observations reaches a maximum when the gap is closed. This maximum time increases in both observation and communication costs. Third, the forecast update is triggered immediately after observation if the forecast gap upon observation is large enough in absolute value. If so, the gap is closed by the update.


The time- and state dependence of the observations and forecasts revisions implied by this model are ultimately  tested using inflation forecast updates of professional forecasters from recent Consensus Economics panel data for France and Germany. To this end, conditional probabilities as estimated from binary choice models are used. Our findings clearly support time-dependent updates, a result which is compatible with the observation cost assumption. Indeed, they point to a strong positive effect when the last update has occurred three and/or six months ago, even after controlling for the institutions periodic forecast framework (quarterly or bi-annual). This gives an upper bound estimate of the optimal time between two observations of six months. Evidence of updates state-dependence is also provided. Actually, a strong positive and significant effect is found on updates when the forecast gap is larger than the estimated threshold, as proxied by the last known monthly inflation rate, weighted by its mechanical contribution to the yearly inflation rate forecast. Finally, our results confirm the co-existence of both types of costs with a forecast communication cost smaller than the observation cost.


The paper is twofold. It starts with a theoretical part, with the communication and information costs explicitly modelled, leading to the optimal frequency of forecasts updated and announcements in a couple of mathematical characterizations. The second part describes an empirical strategy using the latter characterizations to estimate proxies to optimal frequencies and communication timings. The strategy is applied to two inflation forecasts survey data in France and Germany with subsequent estimates of optimal frequencies (more precisely, the those of optimal times between successive observations) and other relevant findings on state and time dependencies.

Applications and beneficiaries

This work is primarily motivated by an empirical problem, inflation forecasts stickiness, and as such, the conceptual framework developed is thought to deeply analyze this problem to come out with a characterization of what should be the optimal frequency of inflation observations and announcements to the public. Therefore, the preliminary applications provided for Germany and France should be replicated on other inflation forecasts surveys quite straightforwardly.

Reference to the research

Frédérique Bec, Raouf Boucekkine, and Caroline Jardet (2023) “Why Are Inflation Forecasts Sticky? Theory and Application to France and Germany,” International Journal of Central Banking, 19(4), 215-250.

Related useful references (cited above)

Alvarez, F., F. Lippi, and Paciello, L. (2011), “Optimal Price Setting With Observation and Menu Costs,” Quarterly Journal of Economics, 126 (4), 1909-6190.

Mankiw, N. and Reis, N. (2002), “Sticky information versus sticky prices: A proposal to replace the new Keynesian Phillips curve,” Quarterly Journal of Economics, 117, 1295–1328.

Woodford, M. (2002), ”Imperfect Common Knowledge and the Effects of Monetary Policy,” in P. Aghion, R. Frydman, J. Stiglitz, and M. Woodford, editors, Knowledge, Information and Expectations in Modern Macroeconomics. Princeton University Press,  25–58.