Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under represented in the literature: many studies focus solely on measuring accuracy. Methods...

This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the...

We investigate whether economic conditions at labor market entry predict long-term differences in law graduate earnings. We find that unemployment levels at graduation continue to predict law earnings premiums within 4 years after graduation for earners at the high end and middle of the...

We estimate the increase in earnings from a law degree relative to a bachelor’s degree for graduates of different race/ethnic groups. Law earnings premiums are higher for whites than for minorities (excluding individuals raised outside the U.S.). The median annual law earnings premium is...

This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...

Global inflation has surged to 7.5 percent in August 2022, from an average of 2.1 percent in the decade preceding the COVID-19 pandemic, threatening to become an entrenched phenomenon. This paper disentangles the confluence of contributing factors to the post-pandemic rise in consumer price...

In this paper, we exploit the heterogeneity in the forecasts obtained by estimating different factor models to measure forecast uncertainty. Our approach is simple and intuitive. It consists first in selecting all the models that outperform some benchmark model, and then in constructing an...

In this paper we propose to exploit the heterogeneity of forecasts produced by different model specifications to measure forecast uncertainty. Our approach is simple and intuitive. It consists in selecting all the models that outperform some benchmark model, and then to construct an empirical...

This empirical study proposes a dependency analysis of monthly financial time series. We use the overlapping technique and non-parametric correlation in order to increase both accuracy and consistency. Copulas are used to test extreme co-movements between financial securities. Our results...