Showing results 1 to 10 of approximately 11.(refine search)
Changing Risk-Return Profiles
Are stock returns predictable? This question is a perennially popular subject of debate. In this post, we highlight some results from our recent working paper, where we investigate the matter. Rather than focusing on a single object like the forecasted mean or median, we look at the entire distribution of stock returns and find that the realized volatility of stock returns, especially financial sector stock returns, has strong predictive content for the future distribution of stock returns. This is a robust feature of the data since all of our results are obtained with real-time analyses ...
Online Estimation of DSGE Models
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for ?online? estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions and document the benefits of conditioning DSGE model forecasts on nowcasts ...
A review of the experience of fielding the Survey of Consumer Expectations
Remarks at the Barclays Global Inflation Conference, New York City.
The relationship between expected inflation, disagreement, and uncertainty: evidence from matched point and density forecasts
This paper examines matched point and density forecasts of inflation from the Survey of Professional Forecasters to analyze the relationship between expected inflation, disagreement, and uncertainty. We extend previous studies through our data construction and estimation methodology. Specifically, we derive measures of disagreement and uncertainty by using a decomposition proposed in earlier research by Wallis and by applying the concept of entropy from information theory. We also undertake the empirical analysis within a seemingly unrelated regression framework. Our results offer mixed ...
Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy
This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of ...
All Forecasters Are Not the Same: Time-Varying Predictive Ability across Forecast Environments
This paper examines data from the European Central Bank’s Survey of Professional Forecasters to investigate whether participants display equal predictive performance. We use panel data models to evaluate point- and density-based forecasts of real GDP growth, inflation, and unemployment. The results document systematic differences in participants’ forecast accuracy that are not time invariant, but instead vary with the difficulty of the forecasting environment. Specifically, we find that some participants display higher relative accuracy in tranquil environments, while others display ...
Changing risk-return profiles
We show that realized volatility, especially the realized volatility of financial sector stock returns, has strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time. Current realized volatility has the most information content on the uncertainty of future returns, whereas it has only limited content about the location of the future return distribution. When volatility is low, the predicted distribution of returns is less dispersed and probabilistic forecasts are ...
The behavior of uncertainty and disagreement and their roles in economic prediction: a panel analysis
This paper examines point and density forecasts from the European Central Bank?s Survey of Professional Forecasters. We derive individual uncertainty measures along with individual point- and density-based measures of disagreement. We also explore the relationship between uncertainty and disagreement, as well as their roles in respondents? forecast performance and forecast revisions. We observe substantial heterogeneity in respondents? uncertainty and disagreement. In addition, there is little co-movement between uncertainty and disagreement, and forecast performance shows a more robust ...
The advantages of probabilistic survey questions: remarks at the IT Forum and RCEA Bayesian Workshop, keynote address, Rimini, Italy, May 2016
Remarks at the IT Forum and RCEA Bayesian Workshop Keynote Address, Rimini, Italy.
Forecasting China's Economic Growth and Inflation
Although macroeconomic forecasting forms an integral part of the policymaking process, there has been a serious lack of rigorous and systematic research in the evaluation of out-of-sample model-based forecasts of China's real gross domestic product (GDP) growth and consumer price index inflation. This paper fills this research gap by providing a replicable forecasting model that beats a host of other competing models when measured by root mean square errors, especially over long-run forecast horizons. The model is shown to be capable of predicting turning points and usable for policy analysis ...