An Empirical Analysis of Survey-Based Macroeconomic Forecasts and Uncertainty

Abstract

Paper 1 (Chapter 2): We investigate the question of whether macroeconomic variables contain information about future stock volatility beyond that contained in past volatility. We show that forecasts of GDP/IP growth from the Federal Reserve's Survey of Professional Forecasters predict volatility in a cross-section of 49 industry portfolios. The expectation of higher growth rates is associated with lower stock volatility. Our results are in line with both counter-cyclical volatility in dividend news as well as in expected returns. Inflation forecasts predict higher or lower stock volatility depending on the state of the economy and the stance of monetary policy. Forecasts of higher unemployment rates are good news for stocks during expansions and go along with lower stock volatility. Our results hold in- as well as out-of-sample and pass various robustness checks. Paper 2 (Chapter 3): We analyze the covariates of average individual inflation uncertainty and the cross-sectional variance of point forecasts (`disagreement') based on data from the European Central Bank's Survey of Professional Forecasters. We empirically confirm the implication from a theoretical variance decomposition that disagreement is an incomplete approximation to overall uncertainty. Both measures are associated with macroeconomic conditions and indicators of monetary policy, but the relations differ qualitatively. In particular, average individual inflation uncertainty is higher during periods of expansionary monetary policy, whereas disagreement rises during contractionary periods. This implies that conclusions based on disagreement as a single indicator of ex-ante uncertainty are incomplete and potentially misleading. Paper 3 (Chapter 4): We analyze the relationship between forecaster disagreement and macroeconomic uncertainty in the Euro area using data from the European Central Bank's Survey of Professional Forecasters for the period 1999Q1-2018Q2. We find that disagreement is generally a poor proxy for uncertainty. However, the strength of this link varies with the employed dispersion statistic, the choice of either the point forecasts or the histogram means to calculate disagreement, the considered outcome variable and the forecast horizon. In contrast, distributional assumptions do not appear to be very influential. The relationship is weaker during economically turbulent periods when indicators of uncertainty are needed most. Accounting for the entry and exit of forecasters to and from the survey has little impact on the results. We also show that survey-based uncertainty is associated with overall policy uncertainty, whereas forecaster disagreement is more closely related to the fluctuations on financial markets. Paper 4 (Chapter 5): Although survey-based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Forecasters who report inconspicuously low ex-ante variances often produce squared forecast errors that are much larger on average. In this paper, we document the novel stylized fact that this variance misalignment is related to the rounding behavior of survey participants. Discarding responses which are strongly rounded provides an easily implementable correction that i) can be carried out in real time, i.e., before outcomes are observed, and ii) delivers a significantly improved match between ex-ante and ex-post forecast variances. According to our estimates, uncertainty about inflation, output growth and unemployment in the U.S. and the Euro area is higher after correcting for the rounding effect. The increase in the share of non-rounded responses in recent years also helps to understand the trajectory of survey-based average uncertainty during the years after the financial and sovereign debt crisis. Our findings are in line with assertions from the previous literature regarding the connection between survey respondents' rounding behavior and their uncertainty about future macroeconomic outcomes.