Ritong Qu

This paper develops 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.

We develop new methods for testing equal predictive accuracy for panels of forecasts, exploiting information in both the time-series and cross-sectional dimensions of the data. We examine general tests of equal forecasting performance averaged across all time periods and individual units, along with tests that focus on subsets of time or clusters of units. Properties of our tests are demonstrated through Monte Carlo simulations and in an empirical application that compares International Monetary Fund forecasts of country-level real gross domestic product growth and inflation to private-sector survey forecasts and forecasts from a simple time-series model. 

In this paper, we study systemic non-financial corporate sector distress using firm-level probabilities of default (PD), covering 66 countries, and spanning the last three decades. Systemic corporate distress is identified by elevated PDs across a large portion of the firms in an economy. We document a significant impact on GDP and credit growth for both advanced and emerging markets. A machine-learning based early warning system is constructed to predict the onset of distress in one year’s time. Our results show that credit expansion, tightening monetary policy, overvalued stock prices, and debt-linked balancesheet weaknesses also predict corporate distress. 

We use data on multiple consumption goods to identify rare, 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.

We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time.  The model uses data from publicly traded companies to construct a probability of default (PD) function. This function integrates high-frequency, market-based, aggregate distress signals with low-frequency, firm-level financial ratios, and macroeconomic indicators. When provided with private firms' financial ratios, the model, which we name signal-knowledge transfer learning model (SKTL), transfers insights gained from public firms to private ones and performs well as an ordinal measure of privately held firms' default risk.

We use a unique Brazilian dataset on daily survey expectations to obtain direct measures of shocks to central bank target rates and changes in economic uncertainty. Using these measures, we gauge the effect of monetary policy shocks on economic uncertainty, term premia, inflation expectations, and bond yields in Brazil. We find strong evidence that inflation uncertainty is key to transmitting monetary policy shocks to the yield curve via time-varying term premia. Finally, Fed announcements have sizeable spillover effects on the Brazilian bond market, as positive shocks to US yields significantly raise term premia in Brazil through elevated exchange rate risk.

To answer this question, we develop new methods allowing us to test for superior forecasting skills in settings with arbitrarily many forecasters, outcome variables, and time periods. Our methods allow us to address if any economists had superior forecasting skills for any variables or at any point in time while carefully controlling for the role of “luck” which tends to give rise to false discoveries when very large numbers of forecasts are compared. We develop new economic hypotheses that allow us to identify “specialist”, “generalist”, and “timing” skills in forecasting performance. We apply our new methods to a large set of monthly forecasts of US macroeconomic data and to forecasts of GDP growth and inflation in a large number of countries. Overall, we find very little evidence that individual economists can beat simple benchmarks such as a simple equal-weighted average of peer forecasts.