Evaluating Density Forecasts

We propose methods for evaluating density forecasts. We focus primarily on methods that are applicable regardless of the particular user's loss function. We illustrate the methods with a detailed simulation example, and then we present an application to density forecasting of daily stock market returns. We discuss extensions for improving suboptimal density forecasts, multi-step-ahead density forecast evaluation, multivariate density forecast evaluation, monitoring for structural change and its relationship to density forecasting, and density forecast evaluation with known loss function.

Evaluating density forecasts

The authors propose methods for evaluating and improving density forecasts. They focus primarily on methods that are applicable regardless of the particular user's loss function, though they take explicit account of the relationships between density forecasts, action choices, and the corresponding expected loss throughout. They illustrate the methods with a detailed series of examples, and they discuss extensions to improving and combining suboptimal density forecasts, multistep-ahead density forecast evaluation, multivariate density forecast evaluation, monitoring for structural change and its relationship to density forecasting, and density forecast evaluation with known loss function.Forecasting

Evaluating Density Forecasts

We propose methods for evaluating density forecasts. We focus primarily on methods" that are applicable regardless of the particular user's loss function. We illustrate the methods" with a detailed simulation example, and then we present an application to density forecasting of" daily stock market returns. We discuss extensions for improving suboptimal density forecasts multi-step-ahead density forecast evaluation, multivariate density forecast evaluation for structural change and its relationship to density forecasting, and density forecast evaluation" with known loss function.

Forecasting Random Walks under Drift Instability

This paper considers forecast averaging when the same model is used but estimation is carried out over different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. It is shown that compared to using forecasts based on a single estimation window, averaging over estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks. Similar results are also obtained when observations are exponentially down-weighted, although in this case the performance of forecasts based on exponential down-weighting critically depends on the choice of the weighting coefficient. The forecasting techniques are applied to monthly inflation series of 21 OECD countries and it is found that average forecasting methods in general perform better than using forecasts based on a single estimation window

Induction of GADD34 Is Necessary for dsRNA-Dependent Interferon-β Production and Participates in the Control of Chikungunya Virus Infection

Nucleic acid sensing by cells is a key feature of antiviral responses, which generally result in type-I Interferon production and tissue protection. However, detection of double-stranded RNAs in virus-infected cells promotes two concomitant and apparently conflicting events. The dsRNA-dependent protein kinase (PKR) phosphorylates translation initiation factor 2-alpha (eIF2α) and inhibits protein synthesis, whereas cytosolic DExD/H box RNA helicases induce expression of type I-IFN and other cytokines. We demonstrate that the phosphatase-1 cofactor, growth arrest and DNA damage-inducible protein 34 (GADD34/Ppp1r15a), an important component of the unfolded protein response (UPR), is absolutely required for type I-IFN and IL-6 production by mouse embryonic fibroblasts (MEFs) in response to dsRNA. GADD34 expression in MEFs is dependent on PKR activation, linking cytosolic microbial sensing with the ATF4 branch of the UPR. The importance of this link for anti-viral immunity is underlined by the extreme susceptibility of GADD34-deficient fibroblasts and neonate mice to Chikungunya virus infection

What account for the differences in rent-price ratio and turnover rate? A search-and-matching approach

We build an on-the-house-search model and show analytically that the rent-to-price ratio (or rental yield) and turnover rate, which are frequently used metrics for the housing market, are jointly determined in equilibrium. We therefore adopt a simultaneous equation approach on matched sale-rental pairs in our empirical investigation, as a housing unit cannot be owner-occupied and renter-occupied at the same time. Our empirical results confirm a higher turnover rate is associated with a lower rent-to-price ratio, as predicted by the model. Furthermore, our results suggest a form of “dichotomy” in the empirical determinants of rental yield and turnover at the real-estate-development (RED) level: the demographic structure, and past return performance affect its turnover rate, while popularity, human capital environment, mortgage burden, and long run rent growth determine the rental yield. No evidence of “thick market effect” is found. The robustness of our results are established through a series of tests. In addition to these findings, our tractable search-theoretic model, a ranking of more than 130 RED in Hong Kong based on the popularity index we construct, and the estimated brand-premium for different major real estate developers may also carry independent research and practical interests

Evaluating density forecasts with applications to financial risk management

Density forecasting is increasingly more important and commonplace, for example in financial risk management, yet little attention has been given to the evaluation of density forecasts. We develop a simple and operational framework for density forecast evaluation. We illustrate the framework with a detailed application to density forecasting of asset returns in environments with time-varying volatility. Finally, we discuss several extensions.Statistics Working Papers Serie

Evaluating Density Forecasts

We propose several methods for evaluating and improving density forecasts. We focus primarily on methods that are applicable regardless of the particular user s loss function, but we also show how to use information about the loss function when and if it is known. Throughout, we take explicit account of the relationships between density forecasts, action choices, and the corresponding expected loss.