Research

Abstract: We study a simple ℓ1-regularized generalized least-squares (GLS) estimator for high-dimensional regressions with autocorrelated errors. The estimation procedure consists of three steps: performing a LASSO regression, fitting an autoregressive model to the realized residuals, and then running a second-stage LASSO regression on the rotated (whitened) data. We examine the theoretical performance of the method in a sub-Gaussian random-design setting, in particular assessing the impact of the rotation on the design matrix and how this impacts the estimation error of the procedure. We show that the GLS (and a feasible variant) maintains a smaller estimation error than an unadjusted LASSO regression when the errors are driven by an autoregressive process. A simulation study verifies the performance of the proposed method, demonstrating that the penalized (feasible) GLS-LASSO estimator performs on par with the LASSO in the case of white noise errors, whilst outperforming when the errors exhibit significant autocorrelation. .

Abstract: Low-frequency time-series (e.g., quarterly data) are often treated as benchmarks for interpolating to higher frequencies, since they generally exhibit greater precision and accuracy in contrast to their high-frequency counterparts (e.g., monthly data) reported by governmental bodies. An array of regression-based methods have been proposed in the literature which aim to estimate a target high-frequency series using higher frequency indicators. However, in the era of big data and with the prevalence of large volume of administrative data-sources there is a need to extend traditional methods to work in high-dimensional settings, i.e. where the number of indicators is similar or larger than the number of low-frequency samples. The package DisaggregateTS includes both classical regressionsbased disaggregation methods alongside recent extensions to high-dimensional settings, c.f. Mosley et al. (2022). This paper provides guidance on how to implement these methods via the package in R, and demonstrates their use in an application to disaggregating CO2 emissions. 

Keywords: GHG emissions, temperature target, carbon markets, emission abatement, stochastic radiative forcing; 

Abstract: We study optimal climate risk mitigation with short-term emissions-reduction targets and long-run temperature stabilization goals in the presence of firms that generate greenhouse gases with different atmospheric persistence and warming potential. We investigate how the pervasive notion of carbon equivalence may undermine climate risk mitigation when carbon markets allow short-lived gases to be traded against long-lived ones. Our findings demonstrate the vulnerability of certain emissions metrics and carbon-accounting standards to greenwashing, and support reporting emissions in disaggregated form and in gas-specific mass units. 

Preprint available upon request.

Preprint available upon request.

Work in progress on online adaptive sparse linearized GLS, with applications to high-dimensional stochastic linear bandits and high-frequency financial prediction.

Work in progress on climate risk, sovereign default probabilities, and the pricing of government debt.