This paper revisits financial market integration in the European Economic and Monetary Union, using a threshold vector error-correction model (TVECM) for a fixed rolling window. This approach enables us to analyze the dynamics of transaction costs and detect any co-movements with (policy...

This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies...

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of...

This paper investigates the PPP and UIP conditions by taking into account possible nonlinearities as well as the role of Taylor rule deviations under alternative monetary policy frameworks. The analysis is conducted using monthly data from January 1993 to December 2020 for five...

Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our model economy, which resembles a typical machine learning setting, N...

This paper studies external sovereign bonds as an asset class. We compile a new database of 220,000 monthly prices of foreign-currency government bonds traded in London and New York between 1815 (the Battle of Waterloo) and 2016, covering 91 countries. Our main insight is that, as in equity...

We identify externalities in human capital production function arising from sibling spillovers. Using regression discontinuity design generated by school-entry cutoffs and school records from one district in Florida, we find positive spillover effects from an older to a younger child in less...