Forecasting during the COVID-19 pandemic entails a great deal of uncertainty. The same way that we would like electoral forecasters to include their confidence intervals to account for such uncertainty, we assume that COVID-19-related forecasts should follow that norm. Based on literature on...

Since testing for COVID-19 infections is not at all randomized over the general population, most epidemiological model forecasts of deaths are subject to `selection bias.' This paper updates and supplements Vinod and Theiss (2020), where the bias correction using generalized linear models (GLM)...

We offer state-wise forecasts of COVID-19 pandemic deaths for the week ending August 10, 2020 based on a sample selection model to correct for rationed and biased testing for the virus. We refer the reader to our earlier papers including Vinod and Theiss (2020b) and Vinod and Theiss (2020a),...

We propose two types of equal predictive ability (EPA) tests with panels to compare the predictions made by two forecasters. The first type, namely S-statistics, focuses on the overall EPA hypothesis which states that the EPA holds on average over all panel units and over time. The second,...

Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning...