The Effectiveness of Central Bank Forward Guidance Under Inflation and Price-level Targeting

This paper examines the effectiveness of central bank forward guidance under inflation and price-level targeting monetary policies. The results show that the beneficial effects of forward guidance increase if a central bank pursues price-level targeting instead of inflation targeting. Output and inflation respond more favorably to forward guidance with price-level targeting than inflation targeting. A monetary policy rule that aggressively reacts to inflation and includes interest rate inertia narrows the performance gap between the two policy regimes. However, forward guidance with price-level targeting is still preferred to forward guidance with inflation targeting after performing multiple robustness checks

(WP 2021-05) Heterogeneity in Individual Expectations, Sentiment, and Constant-Gain Learning

This paper uses adaptive learning to understand the heterogeneity of individual-level expectations. We exploit individual Survey of Professional Forecasters data on output and inflation forecasts. We endow all forecasters with the same information set that they would have as economic agents in a benchmark New Keynesian model. Forecasters are, however, allowed to differ in the constant gain values that they use to update their beliefs and in their sentiments. The latter are defined as the degrees of excess optimism or pessimism about the economy that cannot be justified by the learning model. Our results highlight the heterogeneity in the gain coefficients adopted by forecasters. The median values of the gain coefficients occasionally jump to higher values in the 1970-80s, and stabilize in the 1990s and 2000s. Individual sentiment is also persistent and heterogeneous. Differences in sentiment, however, do not simply cancel out in the aggregate: the majority of forecasters exhibit excess optimism, or excess pessimism, at the same time

(WP 2020-04) Heterogeneity in Individual Expectations, Sentiment, and Constant-Gain Learning

The adaptive learning approach has been fruitfully employed to model the formation of aggregate expectations at the macroeconomic level, as an alternative to rational expectations. This paper uses adaptive learning to understand, instead, the formation of expectations at the micro-level, by focusing on individual expectations and, in particular, trying to account for their heterogeneity. We exploit survey data on output and inflation expectations by individual professional forecasters. We link micro and macro by endowing forecasters with the same information set that they would have as economic agents in a benchmark New Keynesian model. Forecasters are, however, allowed to differ in the constant gain values that they use to update their beliefs. We estimate the best-fitting constant gain for each forecaster. We also extract individual measures of sentiment, defined as the degrees of excess optimism and pessimism that cannot be justified by the near-rational learning model, given the state of the economy and the updated beliefs. Our results highlight the heterogeneity in the gain coefficients adopted by forecasters, which is particularly pronounced at the beginning of the sample. The median values are consistent with those typically estimated using aggregate data, and display some moderate time variation: they occasionally jump to higher values in the 1970-80s, and stabilize in the 1990s and 2000s. Individual sentiment is persistent and heterogeneous. Differences in sentiment, however, don\u27t simply cancel out in the aggregate: the majority of forecasters exhibit excess optimism, or excess pessimism, at the same time

(WP 2020-03) The Effect of Central Bank Credibility on Forward Guidance in an Estimated New Keynesian Model

This paper examines the effectiveness of forward guidance in an estimated New Keynesian model with imperfect central bank credibility. We estimate credibility for the U.S. Federal Reserve with Bayesian methods exploiting survey data on interest rate expectations from the Survey of Professional Forecasters (SPF). The results provide important takeaways: (1) The estimate of Federal Reserve credibility in terms of for- ward guidance announcements is relatively high, which indicates a degree of forward guidance effectiveness, but still one that is below the fully credible case. Hence, anticipation effects are attenuated and, accordingly, output and inflation do not respond as favorably to forward guidance announcements. (2) Imperfect central bank credibility is an important feature to resolve the so-called forward guidance puzzle, which the literature shows arises from the unrealistically large responses of macroeconomic variables to forward guidance statements in structural models with perfect credibility. (3) Imperfect monetary authority credibility can also explain the evidence of forecasting error predictability based on forecasting disagreement found in the SPF data. Thus, accounting for imperfect credibility is important to model the formation of expectations in the economy and to understand the transmission mechanism of forward guidance announcements

Workset Creation for Scholarly Analysis: Recommendations and Prototyping Project Reports

This document assembles and describes the outcomes of the four prototyping projects undertaken as part of the Workset Creation for Scholarly Analysis (WCSA) research project (2013 – 2015). Each prototyping project team provided its own final report. These reports are assembled together and included in this document. Based on the totality of results reported, the WCSA project team also provide a set of overarching recommendations for HTRC implementation and adoption of research conducted by the Prototyping Project teams. The work described here was made possible through the generous support of The Andrew W. Mellon Foundation (Grant Ref # 21300666).The Andrew W. Mellon Foundation (Grant Ref # 21300666)Ope