Search Results
Working Paper
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, document the accuracy and runtime benefits o fgeneralized data tempering for “online” estimation (that is, re-estimating a model asnew data become available), 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 and study the sensitivity ofthe predictive performance to ...
Discussion Paper
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 ...
Speech
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.
Working Paper
Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics
Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the 'data speak.' Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile ...
Working Paper
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 ...
Working Paper
A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area
This paper examines point and density forecasts of real GDP growth, inflation and unemployment from the European Central Bank?s Survey of Professional Forecasters. We present individual uncertainty measures and introduce individual point- and density-based measures of disagreement. The data indicate substantial heterogeneity and persistence in respondents? uncertainty and disagreement, with uncertainty associated with prominent respondent effects and disagreement associated with prominent time effects. We also examine the co-movement between uncertainty and disagreement and find an ...
Working Paper
Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective
This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, ...
Working Paper
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 ...
Report
Changing Risk-Return Profiles
We show that realized volatility in market returns and financial sector stock returns have 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 sharper.
Report
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 ...