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An extension of the Generalized Autoregressive Score (GAS) model is presented for time series with excess null observations to include explanatory variables. An extension of the GAS model proposed by Harvey and Ito is suggested, and it is applied to precipitation data from a city in Chile. It is concluded that the model provides adequate prediction, and furthermore, an analysis of the relationship between the precipitation variable and the explanatory variables is shown. This relationship is compared with the meteorology literature, demonstrating concurrence. [ABSTRACT FROM AUTHOR]

In this work, we use a cointegration state space approach to estimate the quarterly series of the Chilean Gross Domestic Product (GDP) in the period 1965–2009. First, the equation of Engle–Granger is estimated using the data of the yearly GPD and some related variables, such as the price of copper, the exports of goods and services, and the mining production index. The estimated coefficients of this regression are then used to obtain a first estimation of the quarterly GDP series with measurement errors. A state space model is then applied to improve the preliminary estimation of the GDP with the main purpose of eliminating the maximum error of measurement such that the sum of the three-month values coincide swith the yearly GDP. The results are then compared with the traditional disaggregation methods. The resulting quarterly GDP series reflects the major events of the historical Chilean economy. [ABSTRACT FROM AUTHOR]

With the aim of mitigating the damage caused by the coronavirus disease 2019 (COVID-19) pandemic, it is important to use models that allow forecasting possible new infections accurately in order to face the pandemic in specific sociocultural contexts in the best possible way. Our first contribution is empirical. We use an extensive COVID-19 dataset from nine Latin American countries for the period of 1 April 2020 to 31 December 2021. Our second and third contributions are methodological. We extend relevant (i) state-space models with score-driven dynamics and (ii) nonlinear state-space models with unobserved components, respectively. We use weekly seasonal effects, in addition to the local-level and trend filters of the literature, for (i) and (ii), and the negative binomial distribution for (ii). We find that the statistical and forecasting performances of the novel score-driven specifications are superior to those of the nonlinear state-space models with unobserved components model, providing a potential valid alternative to forecasting the number of possible new COVID-19 infections. [ABSTRACT FROM AUTHOR]

The present paper reports a meta-analysis of published evidence on the effects of temperature in school classrooms on children's performance in school. The data from 18 studies were used to construct a relationship between thermal conditions in classrooms and children's performance in school. Psychological tests measuring cognitive abilities and skills, school tasks including mathematical and language-based tasks, rating schemes, and tests used to assess progress in learning including end-of-year grades and the examination results were considered as indicators of children's performance Due to the lack of complete measurements, thermal conditions were characterized by measured classroom temperatures. To create the relationship, the fractional change in performance of psychological tests and school tasks was regressed against the average temperature at which the change was recorded; all published data were used regardless of whether the change in learning outcome changed significantly with temperature. For other learning outcomes, no relationship was created because the data were insufficient. The relationship derived in the analysis shows that the performance of psychological tests and school tasks can be expected to increase on average by 20% if classroom temperatures are lowered from 30 °C to 20 °C and that the temperature for optimal performance is lower than 22 °C. The relationship is valid only for temperate climates. It requires verification for other climates and extensions to temperatures lower than 20 °C and higher than 30 °C. • A relationship was developed between temperature and various indicators of learning outcomes. • It is based on data collected mainly in temperate climates concerning performance of psychological tests and school tasks. • Reducing temperature by 10 K from 30 °C to 20 °C is expected to increase performance of tasks relevant for learning by 20%. • The effects of temperature on schoolwork seems to be greater in magnitude than has been found for office work. • The optimal temperature for the performance of schoolwork seems to be lower than for office work. [ABSTRACT FROM AUTHOR]

A two‐week‐long intervention study was performed in two classrooms in an elementary school in Costa Rica. Split‐cooling air‐conditioning (AC) units were installed in both classrooms. During the first week, the air temperature was reduced in one classroom while in the other (placebo) classroom the fans were operated but no cooling was provided. During the second week, the conditions were exchanged to create a 2 × 2 crossover design in which each pupil was their own control. A total of 37 children performed tasks similar to school work and completed questionnaires reporting their thermal sensation and perceptions. Operating the AC units reduced classroom temperature by about 5 K, from about 30 to 25°C. Thermal sensations changed from hot to neutral and slightly cold, and the percentage of children rating the thermal conditions as acceptable increased significantly. Neutral temperature was estimated to be about 27°C. The 11‐year‐old children performed the language and logical‐thinking tasks significantly better in terms of speed at the lower temperature, while the less able pupils performed better on all tasks at the lower temperature. There were no significant effects on accuracy. These results confirm published findings from moderate climates and extend their validity to the tropics. They indicate that acclimatization can increase the optimal temperature for learning. [ABSTRACT FROM AUTHOR]

Models with time-varying parameters have become more popular for time series analysis. Among these models, Generalized Autoregressive Score (GAS) models are based on the specification of the mechanism through which past observations of the variable of interest affect the current value of the time-varying parameters. GAS models allow capturing the dynamic behavior of time series processes, which is an advantage over models such as ARMA and GARCH with fixed parameters. In this paper, we extend the distribution setting of GAS models from classical densities to sinh-arcsinh (SAS) ones, with emphasis on SAS-Gaussian and SAS- t distribution. The SAS family provides flexible distributions that allow modeling the asymmetry as light or heavy tailed. The parameters of the family enable clear interpretations, and limiting distributions are especially appealing as shape parameters tend to their extreme values. The proposed method's performance is illustrated in simulations and a real-world application to a fish condition dataset. In conclusion, the SAS-Gaussian distribution fits the dataset best by far. [ABSTRACT FROM AUTHOR]

The data from published studies were used to derive systematic relationships between learning outcomes and air quality in classrooms. Psychological tests measuring cognitive abilities and skills, school tasks including mathematical and language-based tasks, rating schemes, and tests used to assess progress in learning including end-of-year grades and exam scores were used to quantify learning outcomes. Short-term sick leave was also included because it may influence progress in learning. Classroom indoor air quality was characterized by the concentration of carbon dioxide (CO 2). For psychological tests and school tasks, fractional changes in performance were regressed against the average concentrations of CO 2 at which they occurred; all data reported in studies meeting the inclusion criteria were used to derive the relationship, regardless of whether the change in performance was statistically significant at the examined levels of classroom air quality. The analysis predicts that reducing CO 2 concentration from 2,100 ppm to 900 ppm would improve the performance of psychological tests and school tasks by 12% with respect to the speed at which the tasks are performed and by 2% with respect to errors made. For other learning outcomes and short-term sick leave, only the relationships published in the original studies were available. They were therefore used to make predictions. These relationships show that reducing the CO 2 concentration from 2,300 ppm to 900 ppm would improve performance on the tests used to assess progress in learning by 5% and that reducing CO 2 from 4,100 ppm to 1,000 ppm would increase daily attendance by 2.5%. These results suggest that increasing the ventilation rate in classrooms in the range from 2 L/s-person to 10 L/s-person can bring significant benefits in terms of learning performance and pupil attendance; no data are available for higher rates. The results provide a strong incentive for improving classroom air quality and can be used in cost-benefit analyses. • The literature on ventilation and performance of schoolwork was reviewed. • School work, national tests and absence rates were used as performance measures. • CO 2 was used as a metric of ventilation. • There was a relationship between reduced CO 2 and improved performance. • Quantitative relationships were created between CO 2 , ventilation rates and performance of schoolwork. [ABSTRACT FROM AUTHOR]

Energy poverty is a multidimensional and complex phenomenon, and several indicators have been developed to evaluate and quantify it. However, often greater complexity does not mean greater precision. In the case of Chile, the Energy Poverty Network established the Three-dimensional and Territorial Indicator of Energy Poverty (EPTTI in Spanish) to assess the energy poverty situation of Chilean families. The EPTTI is based on a multidimensional approach with 10 indicators. Although, their evaluation involves resources that may hinder a practical application. This study analyzed the consistency between the individual responses of an indicator and the adapted EPTTI evaluation, using a database of 641 families. The results show that the excessive energy expenditure and the type and energy source of heating systems indicators are the variables with the greatest influence on energy poverty assessments. These results served to both propose simplified approaches for energy poverty assessment with the indicator, and establish policies of action that regional governments should address to reduce the situation of energy poverty. [ABSTRACT FROM AUTHOR]