Does Gold Still Shelter Inflation, and, if so, When? Evidence From Four Countries

This study investigates gold as a hedge or a safe haven against inflation in four countries. We propose two standard and quantile techniques in the volatility models, with a time-varying conditional variance of regression residuals based on TGARCH specifications. Gold exhibits considerable evidence of a strong hedge in the US and China. Nevertheless, gold provides shelter at different times and not consistently across countries. With regards to be a safe haven, gold retains its status as a key investment in China. On the other hand, gold only plays a minor role in the UK and India. These findings indicate that gold can secure Chinese investment during the high inflationary periods, while gold is a profitable asset to hold over a long period of time in the US. In contrast, UK and Indian investors should hold a well-diversified portfolio for sustainable return and protection from purchasing power loss.

The Application of the SKT Short Cognitive Performance Test to English-Speaking Populations

The SKT (Syndrom-Kurz-Test) is a well-established short cognitive performance test for the detection of attention and memory deficits in Germany. The goal of this paper is to test whether the SKT could be applied to English-speaking populations to screen cognitive impairments in the US, Australia, and Ireland. A regression-based continuous norming technique was applied. Standardized test results obtained from German-speaking (n = 1056) and English-speaking (n = 285) samples were compared. Both samples consisted of cognitively unimpaired, community-dwelling, and independently living volunteers (non-patients) over 60 years of age. Means, medians, and standard deviations of raw scores were calculated. A high similarity in the raw value distributions of the criterion variables and a comparison of German and English multiple regression residuals indicated the equivalence among the samples. In addition, the obtained multiple regression equations for predicting the subtest scores including the explained variances (R2) were highly comparable. Age and intelligence turned out to be the most important and necessary predictors for each subtest performance. The results suggest that the new regression-based norming of the SKT can be validly used in the three English-speaking countries.

Distributional conformal prediction

We propose a robust method for constructing conditionally valid prediction intervals based on models for conditional distributions such as quantile and distribution regression. Our approach can be applied to important prediction problems, including cross-sectional prediction, k–step-ahead forecasts, synthetic controls and counterfactual prediction, and individual treatment effects prediction. Our method exploits the probability integral transform and relies on permuting estimated ranks. Unlike regression residuals, ranks are independent of the predictors, allowing us to construct conditionally valid prediction intervals under heteroskedasticity. We establish approximate conditional validity under consistent estimation and provide approximate unconditional validity under model misspecification, under overfitting, and with time series data. We also propose a simple “shape” adjustment of our baseline method that yields optimal prediction intervals.

Identifying Extreme COVID-19 Mortality Risks in English Small Areas: A Disease Cluster Approach

The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.

Point and interval forecasts of electricity demand with Reg-SARMA models

This paper deals especially with a two-stage approach to forecasting hourly electricity demand by using a linear regression model with serially correlated residuals. Firstly, ordinary least squares are applied to estimate a linear regression model based on purely deterministic predictors (essentially, polynomials in time and calendar dummy variables). In the case wherein the regression residuals are not a white noise series, a SARMA (seasonal autoregressive moving average) process is applied to the estimated regression residuals. After examining a vast set of potential representations, the stationary and invertible process associated with the smaller Akaike information criterion and the smaller Ljung–Box statistic is selected. Secondly, two sets of instrumental predictors are added to the current model: the estimated residuals of the first regression model plus the estimated errors of the chosen SARMA process. The new regression model is estimated by again using ordinary least squares, but taking advantage of the fact that the new regressors eliminate serial correlation. Practical issues in points and interval forecasting are illustrated with reference to nine-day ahead prediction performance for short-term electric loads in Italy.

Residual bootstraps for the uncertainty analysis of tidal models with temporally correlated noise

<div> <div> <div> <p>The accurate characterization of the uncertainty associated with the estimation of tidal constituents is critical to provide accurate water level reconstructions and predictions. However, this represents a challenge in applications since the sparse sampling and finite series length prevent sharply distinguishing between the deterministic tidal signal and the stochastic fluctuations present in the ob- served records. Specifically, the presence of various unresolved sources of vari- ability (e.g., the tide-surge, tide-tide, and tide-river flow interactions, as well as errors and in-homogeneities associated with data measurements) results in sig- nificant broad-spectrum variability of the recorded signals, as well as harmonic analysis parameter modulations from sub-daily to decadal temporal scales. As a result, the residuals obtained after performing regression harmonic analysis are temporally correlated. Conventional methods for assessing the harmonic model uncertainty typically ignore this autocorrelation. A Monte Carlo exper- iment is used to evaluate the effect of neglecting the residual autocorrelation in the estimation of tidal constituent uncertainty. The estimation of regression parameter variability from three commonly used analytical techniques (from the UTide and NS Tide packages, and the IRLS method) and two residual resam- pling (moving-block and semi-parametric bootstrap) are compared. We show that conventional methods (e.g., UTide and the IRLS) may largely underesti- mate the parameter uncertainty when relying on simplified assumptions, such as normality and independence of the regression residuals. This may lead to in- correct assessments about the significance of one or more predictors. We showed improved performance by using the two bootstrap strategies and NS Tide, as a result of a better representation of the autocorrelation structure of residuals. The moving-block bootstrap approach provides a simple alternative that can be easily applied to a large range of (unknown) autocorrelation structures of the observed residuals.</p> </div> </div> </div>

Operational Implementation of Satellite-Rain Gauge Data Merging for Hydrological Modeling

Systems exposed to hydroclimatic variability, such as the integrated electric system in Uruguay, increasingly require real-time multiscale information to optimize management. Monitoring of the precipitation field is key to inform the future hydroelectric energy availability. We present an operational implementation of an algorithm that merges satellite precipitation estimates with rain gauge data, based on a 3-step technique: (i) Regression of station data on the satellite estimate using a Generalized Linear Model; (ii) Interpolation of the regression residuals at station locations to the entire grid using Ordinary Kriging and (iii) Application of a rain/no rain mask. The operational implementation follows five steps: (i) Data download and daily accumulation; (ii) Data quality control; (iii) Merging technique; (iv) Hydrological modeling and (v) Electricity-system simulation. The hydrological modeling is carried with the GR4J rainfall-runoff model applied to 17 sub-catchments of the G. Terra basin with routing up to the reservoir. The implementation became operational at the Electricity Market Administration (ADME) on June 2020. The performance of the merged precipitation estimate was evaluated through comparison with an independent, dense and uniformly distributed rain gauge network using several relevant statistics. Further validation is presented comparing the simulated inflow to the estimate derived from a reservoir mass budget. Results confirm that the estimation that incorporates the satellite information in addition to the surface observations has a higher performance than the one that only uses rain gauge data, both in the rainfall statistical evaluation and hydrological simulation.

A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation

Cluster analysis is widely applied in the neuropsychological field for exploring patterns in cognitive profiles, but traditional hierarchical and non-hierarchical approaches could be often poorly effective or even inapplicable on certain type of data. Moreover, these traditional approaches need the initial specification of the number of clusters, based on a priori knowledge not always owned. For this reason, we proposed a novel method for cognitive clustering through the affinity propagation (AP) algorithm. In particular, we applied the AP clustering on the regression residuals of the Mini Mental State Examination scores—a commonly used screening tool for cognitive impairment—of a cohort of 49 Parkinson’s disease, 48 Progressive Supranuclear Palsy and 44 healthy control participants. We found four clusters, where two clusters (68 and 30 participants) showed almost intact cognitive performance, one cluster had a moderate cognitive impairment (34 participants), and the last cluster had a more extensive cognitive deficit (8 participants). The findings showed, for the first time, an intra- and inter-diagnostic heterogeneity in the cognitive profile of Parkinsonisms patients. Our novel method of unsupervised learning could represent a reliable tool for supporting the neuropsychologists in understanding the natural structure of the cognitive performance in the neurodegenerative diseases.