Xuguang Simon Sheng

Learning and heterogeneity in GDP and inflation forecasts

We estimate a Bayesian learning model with heterogeneity aimed at explaining the evolution of expert disagreement in forecasting real GDP growth and inflation over 24 monthly horizons for G7 countries during 1990-2007. Professional forecasters are found to begin and have relatively more success in predicting inflation than real GDP at significantly longer horizons; forecasts for real GDP contain little information beyond 6 quarters, but forecasts for inflation have predictive value beyond 24 months and even 36 months for some countries. Forecast disagreement arises from two primary sources in our model: differences in the initial prior beliefs of experts, and differences in the interpretation of new public information. Estimated model parameters, together with two separate case studies on (i) the dynamics of forecast disagreement in the aftermath of the 9/11 terrorist attack in the U.S. and (ii) the successful inflation targeting experience in Italy after 1997, firmly establish the importance of these two pathways to expert disagreement.

Truncated Product Methods for Panel Unit Root Tests

This paper proposes two new panel unit root tests based on Zaykin et al. (2002)s truncated product method. The first one assumes constant correlation between p-values and the second one uses sieve bootstrap to allow for general forms of cross-section dependence in the panel units. Monte Carlo simulation shows that both tests have reasonably good size and are powerful in cases of some very large p-values. The proposed tests are applied to a panel of real GDP and inflation density forecasts, resulting in evidence that professional forecasters may not update their forecast precision in an optimal Bayesian way.

Evaluating the Economic Forecasts of FOMC Members

This paper provides a detailed analysis of the forecasts of real GDP, inflation and unemployment made by individual members of the Federal Open Market Committee (FOMC) for the period 1992–2003. Despite a general tendency for the committee members to underpredict real GDP over the sample period, we find evidence suggesting that the FOMC has a considerable amount of information about output growth, beyond what is known by commercial forecasters. We also document a substantial level of variation in the members’ forecasts, which can be explained in part by the differences in economic conditions between Federal Reserve districts. The members’ heterogeneous forecasts for output growth and inflation contain useful information for explaining their preferred policy settings, beyond that in the Greenbook forecasts.

The Information Environment and Cost of Capital

In empirical tests guided by recent theory (e.g., Hughes, Liu and Liu 2007; and Lambert, Leuz and Verrecchia 2011), we examine the joint effects of information precision, information asymmetry and the level of market competition on firms’ cost of equity capital. Consistent with theory, we find that average information precision and the level of market competition reduce the positive effect of information asymmetry but do not eliminate it. Besides examining various aspects of the environment jointly, our study is also unique in that we follow the suggestions of Sheng and Thevenot (2012) for modifying the Barron, Kim, Lim and Stevens (1998) measures of information asymmetry and precision. We find that cost of equity capital varies greatly with the modified measures of information asymmetry and average information precision. For example, our regression estimates suggest that information asymmetry and average information precision are more important than equity beta and firm size in determining firms’ cost of capital, and that such a substantial effect from information asymmetry and information precision is not apparent using unmodified BKLS measures.