My thesis has two themes: The first theme is about studying investors' expectations and the relation to asset prices; while the second theme is about evaluating forecasting performance. Both themes focus on what we can learn from a panel of data. The first chapter of my dissertation studies rational investors' expectation of consumption growth at the presence of structure breaks and asset pricing implications. While the first chapter studies how rational individuals should do, the second and third chapters focus on forecasters' behavior in real world, by developing tools to evaluate forecasters' performance about multiple variables, across many forecasters and at single time periods.
In Chapter 1, we use data on multiple consumption goods to identify infrequent, but persistent breaks to consumption growth dynamics. Over a sixty-year sample, we find four breaks, all of which are associated with major macroeconomic and financial market events such as oil price shocks, the Great Moderation, the end of the tech stock market bubble, and the Covid pandemic. The impact of the breaks on consumption growth is highly uncertain and heterogeneous across consumption goods. We explore the asset pricing implications of our novel empirical evidence in the context of a Lucas tree model in which investors use information on multiple consumption goods to learn about model parameters. We find that break risk in consumption growth, combined with investor learning, helps resolve a number of asset pricing puzzles such as high risk premium and volatility of market returns, as well as cross-sectional anomalies such as momentum.
Chapter 2 is joint work with Allan Timmermann and Yinchu Zhu. Forecasting skills are often identified by comparing predictive accuracy across large numbers of forecasts. This generates a multiple hypothesis testing problem that can trigger many false positives. We develop a new bootstrap test approach for identifying superior predictive accuracy that applies to multi-dimensional panel settings with arbitrarily many forecasts, outcome variables, horizons, and time periods. Our approach controls the family-wise error rate while retaining the ability to identify truly skilled forecasters. An empirical analysis of the IMF's World Economic Outlook forecasts across 185 countries, five variables and several forecast horizons shows how our approach can be used to identify variables and countries for which the IMF's forecasts improve significantly at shorter horizons as well as cases where they fail to improve.
Chapter 3 is also joint work with Allan Timmermann and Yinchu Zhu. We develop new methods for pairwise comparisons of predictive accuracy with cross-sectional data. Using a common factor setup, we establish conditions on cross-sectional dependencies in forecast errors which allow us to test the null of equal predictive accuracy on a single cross-section of forecasts. We consider both unconditional tests of equal predictive accuracy as well as tests that condition on the realization of common factors and show how to decompose forecast errors into exposures to common factors and idiosyncratic components. An empirical application compares the predictive accuracy of financial analysts’ short-term earnings forecasts across six brokerage firms.
