Showing papers by "Francis X. Diebold published in 2022"
08 Mar 2022
TL;DR: This work demonstrates the advantages of DynReg with detailed simulations covering a range of practical issues and shows that all three problems are largely avoided by the use of a simple dynamic regression (DynReg), which is easily implemented and also avoids possible problems concerning strong exogeneity.
Abstract: Least squares regression with heteroskedasticity and autocorrelation consistent (HAC) standard errors has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HAC estimation technology to time series environments. First, most economic time series have strong autocorrelation, which renders HAC regression parameter estimates highly inefficient. Second, strong autocorrelation similarly renders HAC conditional predictions highly inefficient. Finally, the structure of most popular HAC estimators is ill-suited to capture the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in hypothesis testing, in all but the largest sample sizes. We show that all three problems are largely avoided by the use of a simple dynamic regression (DynReg), which is easily implemented and also avoids possible problems concerning strong exogeneity. We demonstrate the advantages of DynReg with detailed simulations covering a range of practical issues.
2 citations
TL;DR: In this article , the authors use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice.
1 citations
TL;DR: In this paper , a single-equation test for Uncovered Interest Parity based on a dynamic regression approach is proposed, which provides consistent and asymptotically efficient parameter estimates and is not dependent on assumptions of strict exogeneity.
Abstract: We suggest a new single-equation test for Uncovered Interest Parity () based on a dynamic regression approach. The method provides consistent and asymptotically efficient parameter estimates, and is not dependent on assumptions of strict exogeneity. This new approach is asymptotically more efficient than the common approach of using OLS with HAC robust standard errors in the static forward premium regression. The coefficient esti- mates when spot return changes are regressed on the forward premium are all positive and remarkably stable across currencies. These estimates are considerably larger than those of previous studies, which frequently find negative coefficients. The method also has the advantage of showing dynamic effects of risk premia, or other events that may lead to rejection of UIP or the efficient markets hypothesis.
1 citations
TL;DR: The Diebold-Yilmaz connectedness research program as mentioned in this paper has been extensively studied in the literature, including in the context of the Connections and Connectivity: A Review.
Abstract: We offer retrospective and prospective assessments of the Diebold-Yilmaz connectedness research program, combined with personal recollections of its development. Its centerpiece in many respects is Diebold and Yilmaz (2014), around which our discussion is organized.
1 citations
20 Oct 2022
TL;DR: In this article , the authors offer reflections on adaptation to climate change, with emphasis on developing areas, and propose a framework for developing countries to adapt to the climate change in developing areas.
Abstract: I offer reflections on adaptation to climate change, with emphasis on developing areas.
TL;DR: In this paper , the authors proposed a new single-equation test for uncovered interest parity based on a dynamic regression approach which provides consistent and asymptotically efficient parameter estimates, and is not dependent on assumptions of strict exogeneity.