Title 1: Forecast Evaluation for Functional Data; Title 2: Permanent School Closures and Crime: Evidence from Scotland

Shixuan Wang is Professor of Economics at the University of Reading, having initially worked as a Lecturer and then an Associate Professor. Before joining Reading in 2018, he was a postdoctoral researcher in statistical forecasting at Cardiff University. He received his PhD degree from the University of Birmingham in 2017 and was awarded a Royal Economic Society (RES) Junior Fellowship in 2016. His primary research focuses on econometric methods for change-point detection and functional data analysis, and their interactions with statistical forecasting. He also has an active research interest in using explainable AI and machine learning for healthcare analytics and operations management. His work has been published in leading journals including Annals of Statistics, Journal of Econometrics, Econometric Theory, and Journal of Business & Economic Statistics. He currently serves as an Associate Editor of International Journal of Finance & Economics, organise a research grant of Forecasting for Social Good (F4SG), and coordinate a research cluster of Econometrics with Data Science (EwDS). He has collaborated with external partners including BT and the NHS; notably, the AI solution he developed for BT was named a finalist at The National AI Awards in 2024.

Gennaro Rossi joined the University of Reading in September 2024. He moved to the UK in 2016 to enrol in an MSc in Economics and Econometrics at the University of Edinburgh. He previously completed a BSc and a MA, both in Economics, at the University of Naples Federico II. In 2023, He was awarded a PhD in Economics from the University of Strathclyde. During his PhD he investigated topics such as school choice, as well as the impact of free school meals policies and multi-grading (i.e. when primary school pupils from different year groups are grouped in the same classroom) on pupil outcomes. In 2022 he joined the Department of Economics at the University of Sheffield as a postdoc researcher. Here he worked on a project funded by the Nuffield foundation (The Skills Imperative 2035), investigating which skills will be in high demand in the near future. In the meantime, he started working on other projects in urban economics, economics of crime and economics of education.

 Title 1: Forecast Evaluation for Functional Data
Abstract: This paper proposes methods for comparing the accuracy of two competing sets of functional forecasts. This is increasingly relevant as many economic and financial forecasters are interested in predicting variables which are observed as functional data objects. However, to date there have been no formal statistical tests to evaluate the relative accuracy of competing functional forecasts. We propose a suite of novel tests, building on the classic Diebold-Mariano test, to provide formal statistical guidance on forecast accuracy in the case of functional data. We derive the asymptotic properties of the tests, including their self-normalised versions, and demonstrate the validity of analytic or bootstrap-based critical values. We investigate the finite sample performance of the tests using Monte Carlo simulations. We demonstrate their practical usefulness by evaluating forecasts of the U.S. yield curve based on forward rates versus a random walk benchmark.

Title 2: Permanent School Closures and Crime: Evidence from Scotland
Abstract: School closures occur regularly, driven by declining school performance, depopulation, school buildings not meeting safety regulations, and a range of other factors. This has given rise to a large literature examining the effect of school closures on educational outcomes, but only a limited literature on the effect of these closures on local crime rates. In this paper we study the effects of permanent school closures on crime. We leverage the closure of over 200 schools in Scotland between the school years 2006/07 and 2018/19, and employ a staggered difference-in-differences design. Our results show that neighbourhoods affected by school closures experience a reduction in crime of about 10% of a standard deviation, relative to areas where schools remained open. This effect is mainly driven by a reduction in vandalism and property crimes. We provide evidence on several mechanisms explaining the negative crime effect, such as changes in neighbourhood composition and displacement of crime-prone youth.