Book Chapters
"Forecasting the macroeconomic effects of physical climate risk" with Andrew J. Wilson, in M.P. Clements and A.B. Galvao (eds.), Handbook of Research Methods in Macroeconomic Forecasting (2024), Chapter 15
Abstract: This chapter briefly reviews the existing literature exploring the relationship between macroeconomic outcomes and physical climate risks. Critically, it focuses on the physical, market impacts of climate change, though a very large portion of the impacts of climate change on social welfare will likely be non-market effects, including harm to human health— including increased mortality—increased conflict and migration, and damage to ecosystem function. It then discusses a set of unaddressed challenges facing this literature, including how to treat uncertainty, differences in empirical approaches founded on growth rate versus GDP level effects, the treatment of adaptation, and various categories of risks omitted from existing studies. Next, it considers how some of these challenges have been addressed in the context of modeling hurricane damages; it also shows how an aggregate empirical model can be used to predict hurricane damages both now and under climate change.
"Smooth Robust Multi-Horizon Forecasts" with Jennifer L. Castle and David F. Hendry, in A. Chudik, C. Hsiao and A. Timmermann (eds.), Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, Advances in Econometrics (2022), Vol. 34A, Chapter 7, pp. 143-165.
Supplemental Material: Nuffield College Economics Discussion Paper 2021-W01, H.O. Stekler Research Program on Forecasting Working Paper 2020-009
Abstract: We investigate whether smooth robust methods for forecasting can help mitigate pronounced and persistent failure across multiple forecast horizons. We demonstrate that naive predictors are interpretable as local estimators of the long-run relationship with the advantage of adapting quickly after a break, but at a cost of additional forecast error variance. Smoothing over naive estimates helps retain these advantages while reducing the costs, especially for longer forecast horizons. We derive the performance of these predictors after a location shift, and confirm the results using simulations. We apply smooth methods to forecasts of UK productivity and US 10-year Treasury yields and show that they can dramatically reduce persistent forecast failure exhibited by forecasts from macroeconomic models and professional forecasters.
"Evaluating Government Budget Forecasts" with Neil R. Ericsson, in D. Williams, T. Calabrese (eds.), The Palgrave Handbook of Government Budget Forecasting (2019), Chapter 3, pp. 37-69, Palgrave Studies in Public Debt, Spending, and Revenue.
Abstract: This chapter reviews the literature on the evaluation of government budget forecasts, outlines a generic framework for forecast evaluation, and illustrates forecast evaluation with empirical analyses of different U.S. government agencies’ forecasts of U.S. federal debt. Techniques for forecast evaluation include comparison of mean squared forecast errors, forecast encompassing, tests of predictive failure, and tests of bias and efficiency. Recent extensions of these techniques utilize machine-learning algorithms to handle more potential regressors than observations, a characteristic common to big data. These techniques are generally applicable, including to forecasts of components of the government budget; to forecasts of budgets from municipal, state, provincial, and national governments; and to other economic and non-economic forecasts. Evaluation of forecasts is fundamental to assessing the forecasts’ usefulness, and evaluation can indicate ways in which the forecasts may be improved.